-
Notifications
You must be signed in to change notification settings - Fork 137
/
Copy pathzsublime.sublime-workspace
1014 lines (1014 loc) · 963 KB
/
zsublime.sublime-workspace
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
{
"auto_complete":
{
"selected_items":
[
[
"file",
"filename\tabc"
],
[
"model",
"model_tf"
],
[
"ex",
"except"
],
[
"module",
"module_name\tparam"
],
[
"mod",
"modulex\tstatement"
],
[
"am",
"amp_i"
],
[
"fom",
"formated_df"
],
[
"mo",
"module_name"
],
[
"a",
"add_argument"
],
[
"fol",
"folder_list"
],
[
"repo_",
"repo_generate_signature"
],
[
"re",
"reponame"
],
[
"repo",
"conda_build_repo"
],
[
"gi",
"git_clone"
],
[
"os_s",
"os_system"
],
[
"os_",
"os_scan"
],
[
"sy",
"system"
],
[
"mis",
"miss_packages"
],
[
"cu",
"curr_os"
],
[
"con",
"conda_env"
],
[
"python",
"python_version"
],
[
"sto",
"stopwords_dict"
],
[
"col",
"colname"
]
]
},
"buffers":
[
{
"file": "README.md",
"settings":
{
"buffer_size": 8355,
"encoding": "UTF-8",
"line_ending": "Windows"
}
},
{
"contents": "\\documentstyle[twocolumn,jsaiac]{article}\n\n\\input{data_en}\n\n\\end{document}\n",
"file": "/D/Dropbox/__research_paper/a0_share_publication/JSAI_2020_march/jsaiac_tex_en-2020-20191025/jsaiac_tex_en-2020/guide-209_en.tex",
"file_size": 80,
"file_write_time": 130264420620000000,
"settings":
{
"buffer_size": 75,
"line_ending": "Windows"
}
},
{
"file": "requirements.txt",
"settings":
{
"buffer_size": 156,
"line_ending": "Windows"
}
},
{
"file": "mlmodels/model_keras/01_deepctr.py",
"settings":
{
"buffer_size": 7433,
"encoding": "UTF-8",
"line_ending": "Windows"
}
},
{
"file": "mlmodels/optim.py",
"settings":
{
"buffer_size": 13557,
"line_ending": "Unix"
}
},
{
"file": "setup.py",
"settings":
{
"buffer_size": 9320,
"encoding": "UTF-8",
"line_ending": "Windows"
}
},
{
"file": "mlmodels/models.py",
"settings":
{
"buffer_size": 13457,
"line_ending": "Unix"
}
},
{
"file": "mlmodels/util.py",
"settings":
{
"buffer_size": 10505,
"line_ending": "Unix"
}
},
{
"contents": "Microsoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels>cd .. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>conda list \n# packages in environment at D:\\_devs\\Python01\\ana3: \n# \n# Name Version Build Channel \n_ipyw_jlab_nb_ext_conf 0.1.0 py36he6757f0_0 \nalabaster 0.7.10 py36hcd07829_0 \nanaconda 5.2.0 py36_3 \nanaconda-client 1.6.14 py36_0 \nanaconda-navigator 1.8.7 py36_0 \nanaconda-project 0.8.2 py36hfad2e28_0 \nappdirs 1.4.3 pypi_0 pypi \nasn1crypto 0.24.0 py36_0 \nastroid 1.6.3 py36_0 \nastropy 3.0.2 py36h452e1ab_1 \nattrs 18.1.0 py36_0 \nbabel 2.5.3 py36_0 \nbackcall 0.1.0 py36_0 \nbackports 1.0 py36h81696a8_1 \nbackports.shutil_get_terminal_size 1.0.0 py36h79ab834_2 \nbeautifulsoup4 4.6.0 py36hd4cc5e8_1 \nbitarray 0.8.1 py36hfa6e2cd_1 \nbkcharts 0.2 py36h7e685f7_0 \nblack 19.3b0 pypi_0 pypi \nblas 1.0 mkl \nblaze 0.11.3 py36h8a29ca5_0 \nbleach 2.1.3 py36_0 \nblosc 1.14.3 he51fdeb_0 \nbokeh 0.12.16 py36_0 \nboto 2.48.0 py36h1a776d2_1 \nbottleneck 1.2.1 py36hd119dfa_0 \nbzip2 1.0.6 hfa6e2cd_5 \nca-certificates 2018.03.07 0 anaconda \ncertifi 2018.4.16 py36_0 conda-forge \ncffi 1.11.5 py36h945400d_0 \nchardet 3.0.4 py36h420ce6e_1 \nclick 6.7 py36hec8c647_0 \ncloudpickle 0.5.3 py36_0 \nclyent 1.2.2 py36hb10d595_1 \ncmarkgfm 0.4.2 py36hfa6e2cd_2 conda-forge \ncolorama 0.3.9 py36h029ae33_0 \ncomtypes 1.1.4 py36_0 \nconda 4.6.14 py36_0 conda-forge \nconda-build 3.10.5 py36_0 \nconda-env 2.6.0 h36134e3_1 \nconda-verify 2.0.0 py36h065de53_0 \nconsole_shortcut 0.1.1 h6bb2dd7_3 \ncontextlib2 0.5.5 py36he5d52c0_0 \ncryptography 2.2.2 py36hfa6e2cd_0 \ncurl 7.60.0 h7602738_0 \ncycler 0.10.0 py36h009560c_0 \ncython 0.28.2 py36hfa6e2cd_0 \ncytoolz 0.9.0.1 py36hfa6e2cd_0 \ndask 0.17.5 py36_0 \ndask-core 0.17.5 py36_0 \ndatashape 0.5.4 py36h5770b85_0 \ndecorator 4.3.0 py36_0 \ndistributed 1.21.8 py36_0 \ndocutils 0.14 py36h6012d8f_0 \nentrypoints 0.2.3 py36hfd66bb0_2 \net_xmlfile 1.0.1 py36h3d2d736_0 \nfastcache 1.0.2 py36hfa6e2cd_2 \nfilelock 3.0.4 py36_0 \nflask 1.0.2 py36_1 \nflask-cors 3.0.4 py36_0 \nfreetype 2.8 h51f8f2c_1 \nfuture 0.17.1 py36_1000 conda-forge \nget_terminal_size 1.0.0 h38e98db_0 \ngevent 1.3.0 py36hfa6e2cd_0 \nglob2 0.6 py36hdf76b57_0 \ngreenlet 0.4.13 py36hfa6e2cd_0 \nh5py 2.7.1 py36h3bdd7fb_2 \nhdf5 1.10.2 hac2f561_1 \nheapdict 1.0.0 py36_2 \nhtml5lib 1.0.1 py36h047fa9f_0 \nicc_rt 2017.0.4 h97af966_0 \nicu 58.2 ha66f8fd_1 \nidna 2.6 py36h148d497_1 \nimageio 2.3.0 py36_0 \nimagesize 1.0.0 py36_0 \nintel-openmp 2018.0.0 8 \nipykernel 4.8.2 py36_0 \nipython 6.4.0 py36_0 \nipython_genutils 0.2.0 py36h3c5d0ee_0 \nipywidgets 7.2.1 py36_0 \nisort 4.3.4 py36_0 \nitsdangerous 0.24 py36hb6c5a24_1 \njdcal 1.4 py36_0 \njinja2 2.10 py36h292fed1_0 \njpeg 9b hb83a4c4_2 \njsonschema 2.6.0 py36h7636477_0 \njupyter 1.0.0 py36_4 \njupyter_client 5.2.3 py36_0 \njupyter_console 5.2.0 py36h6d89b47_1 \njupyter_core 4.4.0 py36h56e9d50_0 \njupyterlab 0.32.1 py36_0 \njupyterlab_launcher 0.10.5 py36_0 \nkiwisolver 1.0.1 py36h12c3424_0 \nlazy-object-proxy 1.3.1 py36hd1c21d2_0 \nlibcurl 7.60.0 hc4dcbb0_0 \nlibiconv 1.15 h1df5818_7 \nlibpng 1.6.34 h79bbb47_0 \nlibsodium 1.0.16 h9d3ae62_0 \nlibssh2 1.8.0 hd619d38_4 \nlibtiff 4.0.9 hb8ad9f9_1 \nlibxml2 2.9.8 hadb2253_1 \nlibxslt 1.1.32 hf6f1972_0 \nllvmlite 0.23.1 py36hcacf6c6_0 \nlocket 0.2.0 py36hfed976d_1 \nlxml 4.2.1 py36heafd4d3_0 \nlzo 2.10 h6df0209_2 \nm2r 0.2.1 pypi_0 pypi \nm2w64-gcc-libgfortran 5.3.0 6 \nm2w64-gcc-libs 5.3.0 7 \nm2w64-gcc-libs-core 5.3.0 7 \nm2w64-gmp 6.1.0 2 \nm2w64-libwinpthread-git 5.0.0.4634.697f757 2 \nmarkupsafe 1.0 py36h0e26971_1 \nmatplotlib 2.2.2 py36h153e9ff_1 \nmccabe 0.6.1 py36hb41005a_1 \nmenuinst 1.4.14 py36hfa6e2cd_0 \nmistune 0.8.3 py36hfa6e2cd_1 \nmkl 2018.0.2 1 \nmkl-service 1.1.2 py36h57e144c_4 \nmkl_fft 1.0.1 py36h452e1ab_0 \nmkl_random 1.0.1 py36h9258bd6_0 \nmore-itertools 4.1.0 py36_0 \nmpmath 1.0.0 py36hacc8adf_2 \nmsgpack-python 0.5.6 py36he980bc4_0 \nmsys2-conda-epoch 20160418 1 \nmultipledispatch 0.5.0 py36_0 \nnavigator-updater 0.2.1 py36_0 \nnbconvert 5.3.1 py36h8dc0fde_0 \nnbformat 4.4.0 py36h3a5bc1b_0 \nnetworkx 2.1 py36_0 \nnltk 3.3.0 py36_0 \nnose 1.3.7 py36h1c3779e_2 \nnotebook 5.5.0 py36_0 \nnumba 0.38.0 py36h830ac7b_0 \nnumexpr 2.6.5 py36hcd2f87e_0 \nnumpy 1.14.3 py36h9fa60d3_1 \nnumpy-base 1.14.3 py36h555522e_1 \nnumpydoc 0.8.0 py36_0 \nodo 0.5.1 py36h7560279_0 \nolefile 0.45.1 py36_0 \nopenpyxl 2.5.3 py36_0 \nopenssl 1.0.2o h8ea7d77_0 anaconda \npackaging 17.1 py36_0 \npandas 0.23.0 py36h830ac7b_0 \npandoc 1.19.2.1 hb2460c7_1 \npandocfilters 1.4.2 py36h3ef6317_1 \nparso 0.2.0 py36_0 \npartd 0.3.8 py36hc8e763b_0 \npath.py 11.0.1 py36_0 \npathlib2 2.3.2 py36_0 \npatsy 0.5.0 py36_0 \npep8 1.7.1 py36_0 \npickleshare 0.7.4 py36h9de030f_0 \npillow 5.1.0 py36h0738816_0 \npip 10.0.1 py36_0 \npkginfo 1.4.2 py36_1 \npluggy 0.6.0 py36hc7daf1e_0 \nply 3.11 py36_0 \nprompt_toolkit 1.0.15 py36h60b8f86_0 \npsutil 5.4.5 py36hfa6e2cd_0 \npy 1.5.3 py36_0 \npycodestyle 2.4.0 py36_0 \npycosat 0.6.3 py36h413d8a4_0 \npycparser 2.18 py36hd053e01_1 \npycrypto 2.6.1 py36hfa6e2cd_8 anaconda \npycurl 7.43.0.1 py36h74b6da3_0 \npyflakes 1.6.0 py36h0b975d6_0 \npygments 2.2.0 py36hb010967_0 \npylint 1.8.4 py36_0 \npyodbc 4.0.23 py36h6538335_0 \npyopenssl 18.0.0 py36_0 \npyparsing 2.2.0 py36h785a196_1 \npyqt 5.9.2 py36h1aa27d4_0 \npysocks 1.6.8 py36_0 \npytables 3.4.3 py36he6f6034_1 \npytest 3.5.1 py36_0 \npytest-arraydiff 0.2 py36_0 \npytest-astropy 0.3.0 py36_0 \npytest-doctestplus 0.1.3 py36_0 \npytest-openfiles 0.3.0 py36_0 \npytest-remotedata 0.2.1 py36_0 \npython 3.6.5 h0c2934d_0 \npython-dateutil 2.7.3 py36_0 \npytz 2018.4 py36_0 \npywavelets 0.5.2 py36hc649158_0 \npywin32 223 py36hfa6e2cd_1 \npywinpty 0.5.1 py36_0 \npyyaml 3.12 py36h1d1928f_1 \npyzmq 17.0.0 py36hfa6e2cd_1 \nqt 5.9.5 vc14he4a7d60_0 [vc14] anaconda \nqtawesome 0.4.4 py36h5aa48f6_0 \nqtconsole 4.3.1 py36h99a29a9_0 \nqtpy 1.4.1 py36_0 \nreadme_renderer 24.0 py_0 conda-forge \nregex 2019.11.1 pypi_0 pypi \nrequests 2.18.4 py36h4371aae_1 \nrequests-toolbelt 0.9.1 py_0 conda-forge \nrope 0.10.7 py36had63a69_0 \nruamel_yaml 0.15.35 py36hfa6e2cd_1 \nscikit-image 0.13.1 py36hfa6e2cd_1 \nscikit-learn 0.19.1 py36h53aea1b_0 \nscipy 1.1.0 py36h672f292_0 \nseaborn 0.8.1 py36h9b69545_0 \nsend2trash 1.5.0 py36_0 \nsetuptools 39.1.0 py36_0 conda-forge \nsimplegeneric 0.8.1 py36_2 \nsingledispatch 3.4.0.3 py36h17d0c80_0 \nsip 4.19.8 py36h6538335_0 \nsix 1.11.0 py36h4db2310_1 \nsnappy 1.1.7 h777316e_3 \nsnowballstemmer 1.2.1 py36h763602f_0 \nsortedcollections 0.6.1 py36_0 \nsortedcontainers 1.5.10 py36_0 \nsphinx 1.7.4 py36_0 \nsphinxcontrib 1.0 py36hbbac3d2_1 \nsphinxcontrib-websupport 1.0.1 py36hb5e5916_1 \nspyder 3.2.8 py36_0 \nsqlalchemy 1.2.7 py36ha85dd04_0 \nsqlite 3.23.1 h35aae40_0 \nstatsmodels 0.9.0 py36h452e1ab_0 \nsympy 1.1.1 py36h96708e0_0 \ntbb 2019.9 he980bc4_1 conda-forge \ntbb4py 2019.9 py36he980bc4_1 conda-forge \ntblib 1.3.2 py36h30f5020_0 \nterminado 0.8.1 py36_1 \ntestpath 0.3.1 py36h2698cfe_0 \ntk 8.6.7 hcb92d03_3 \ntoml 0.10.0 pypi_0 pypi \ntoolz 0.9.0 py36_0 \ntornado 5.0.2 py36_0 \ntqdm 4.33.0 py_0 conda-forge \ntraitlets 4.3.2 py36h096827d_0 \ntwine 1.15.0 py_0 conda-forge \ntyping 3.6.4 py36_0 \nunicodecsv 0.14.1 py36h6450c06_0 \nurllib3 1.22 py36h276f60a_0 \nvc 14 h0510ff6_3 \nvs2015_runtime 14.0.25123 3 \nwcwidth 0.1.7 py36h3d5aa90_0 \nwebencodings 0.5.1 py36h67c50ae_1 \nwerkzeug 0.14.1 py36_0 \nwheel 0.31.1 py36_0 conda-forge \nwidgetsnbextension 3.2.1 py36_0 \nwin_inet_pton 1.0.1 py36he67d7fd_1 \nwin_unicode_console 0.5 py36hcdbd4b5_0 \nwincertstore 0.2 py36h7fe50ca_0 \nwinpty 0.4.3 4 \nwrapt 1.10.11 py36he5f5981_0 \nxlrd 1.1.0 py36h1cb58dc_1 \nxlsxwriter 1.0.4 py36_0 \nxlwings 0.11.8 py36_0 \nxlwt 1.3.0 py36h1a4751e_0 \nyaml 0.1.7 hc54c509_2 \nzeromq 4.2.5 hc6251cf_0 \nzict 0.1.3 py36h2d8e73e_0 \nzlib 1.2.11 h8395fce_2 \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>conda env list \n# conda environments: \n# \nbase * D:\\_devs\\Python01\\ana3 \ncode D:\\_devs\\Python01\\ana3\\envs\\code \npy36_tf13 D:\\_devs\\Python01\\ana3\\envs\\py36_tf13 \nspyder336 D:\\_devs\\Python01\\ana3\\envs\\spyder336 \nspyder4 D:\\_devs\\Python01\\ana3\\envs\\spyder4 \nspyder40 D:\\_devs\\Python01\\ana3\\envs\\spyder40 \ntch04 D:\\_devs\\Python01\\ana3\\envs\\tch04 \ntch12 D:\\_devs\\Python01\\ana3\\envs\\tch12 \ntest D:\\_devs\\Python01\\ana3\\envs\\test \ntf14 D:\\_devs\\Python01\\ana3\\envs\\tf14 \ntf14avx D:\\_devs\\Python01\\ana3\\envs\\tf14avx \ntf14b D:\\_devs\\Python01\\ana3\\envs\\tf14b \ntf20 D:\\_devs\\Python01\\ana3\\envs\\tf20 \ntfchainer D:\\_devs\\Python01\\ana3\\envs\\tfchainer \n \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>conda env --clone tch04 -n ztest \nusage: conda-env-script.py [-h] {create,export,list,remove,update} ... \nconda-env-script.py: error: invalid choice: 'tch04' (choose from 'create', 'export', 'list', 'remove', 'update')\n \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>conda create --clone tch04 -n ztest \nSource: D:\\_devs\\Python01\\ana3\\envs\\tch04 \nDestination: D:\\_devs\\Python01\\ana3\\envs\\ztest \nPackages: 105 \nFiles: 385 \n \nDownloading and Extracting Packages \n_tflow_select-2.3.0 | ################################################################################ | 100% \nblas-1.0 | ################################################################################ | 100% \nca-certificates-2019 | ################################################################################ | 100% \nicc_rt-2019.0.0 | ################################################################################ | 100% \nintel-openmp-2019.5 | ################################################################################ | 100% \nmsys2-conda-epoch-20 | ################################################################################ | 100% \nvs2015_runtime-15.5. | ################################################################################ | 100% \nlibmklml-2019.0.5 | ################################################################################ | 100% \nm2w64-gmp-6.1.0 | ################################################################################ | 100% \nm2w64-libwinpthread- | ################################################################################ | 100% \nmkl-2019.5 | ################################################################################ | 100% \nvc-14.1 | ################################################################################ | 100% \nicu-64.2 | ################################################################################ | 100% \njpeg-9c | ################################################################################ | 100% \nlibblas-3.8.0 | ################################################################################ | 100% \nlibclang-9.0.0 | ################################################################################ | 100% \nlibsodium-1.0.17 | ################################################################################ | 100% \nlz4-c-1.8.3 | ################################################################################ | 100% \nm2w64-gcc-libs-core- | ################################################################################ | 100% \nopenssl-1.1.1d | ################################################################################ | 100% \npython-3.6.7 | ################################################################################ | 100% \nsqlite-3.30.1 | ################################################################################ | 100% \ntk-8.6.10 | ################################################################################ | 100% \nxz-5.2.4 | ################################################################################ | 100% \nzlib-1.2.11 | ################################################################################ | 100% \nastor-0.7.1 | ################################################################################ | 100% \nbackcall-0.1.0 | ################################################################################ | 100% \ncertifi-2019.11.28 | ################################################################################ | 100% \ncloudpickle-1.2.2 | ################################################################################ | 100% \ncolorama-0.4.3 | ################################################################################ | 100% \ndecorator-4.4.1 | ################################################################################ | 100% \ngast-0.3.2 | ################################################################################ | 100% \nhdf5-1.10.5 | ################################################################################ | 100% \nipython_genutils-0.2 | ################################################################################ | 100% \nkiwisolver-1.1.0 | ################################################################################ | 100% \nlibcblas-3.8.0 | ################################################################################ | 100% \nliblapack-3.8.0 | ################################################################################ | 100% \nlibpng-1.6.37 | ################################################################################ | 100% \nlibprotobuf-3.11.1 | ################################################################################ | 100% \nm2w64-gcc-libgfortra | ################################################################################ | 100% \nolefile-0.46 | ################################################################################ | 100% \nparso-0.5.1 | ################################################################################ | 100% \npickleshare-0.7.5 | ################################################################################ | 100% \npycparser-2.19 | ################################################################################ | 100% \npyparsing-2.4.5 | ################################################################################ | 100% \npyreadline-2.1 | ################################################################################ | 100% \npytz-2019.3 | ################################################################################ | 100% \npywin32-225 | ################################################################################ | 100% \nsix-1.13.0 | ################################################################################ | 100% \ntermcolor-1.1.0 | ################################################################################ | 100% \ntornado-6.0.3 | ################################################################################ | 100% \nwcwidth-0.1.7 | ################################################################################ | 100% \nwerkzeug-0.16.0 | ################################################################################ | 100% \nwincertstore-0.2 | ################################################################################ | 100% \nwrapt-1.11.2 | ################################################################################ | 100% \nzeromq-4.3.2 | ################################################################################ | 100% \nzstd-1.4.4 | ################################################################################ | 100% \nabsl-py-0.8.1 | ################################################################################ | 100% \ncffi-1.13.2 | ################################################################################ | 100% \ncycler-0.10.0 | ################################################################################ | 100% \nfreetype-2.10.0 | ################################################################################ | 100% \njedi-0.15.1 | ################################################################################ | 100% \nlibtiff-4.1.0 | ################################################################################ | 100% \nm2w64-gcc-libs-5.3.0 | ################################################################################ | 100% \nmkl-service-2.3.0 | ################################################################################ | 100% \nnumpy-1.17.3 | ################################################################################ | 100% \npython-dateutil-2.8. | ################################################################################ | 100% \npyzmq-18.1.1 | ################################################################################ | 100% \nqt-5.12.5 | ################################################################################ | 100% \nsetuptools-42.0.2 | ################################################################################ | 100% \ntraitlets-4.3.3 | ################################################################################ | 100% \ngrpcio-1.23.0 | ################################################################################ | 100% \nh5py-2.10.0 | ################################################################################ | 100% \njoblib-0.14.0 | ################################################################################ | 100% \njupyter_core-4.6.1 | ################################################################################ | 100% \nmarkdown-3.1.1 | ################################################################################ | 100% \nmatplotlib-base-3.1. | ################################################################################ | 100% \nmkl_fft-1.0.15 | ################################################################################ | 100% \nnumexpr-2.7.0 | ################################################################################ | 100% \npandas-0.25.3 | ################################################################################ | 100% \npillow-6.2.1 | ################################################################################ | 100% \nprotobuf-3.11.1 | ################################################################################ | 100% \npygments-2.5.2 | ################################################################################ | 100% \npyqt-5.12.3 | ################################################################################ | 100% \nscipy-1.3.1 | ################################################################################ | 100% \nwheel-0.33.6 | ################################################################################ | 100% \njupyter_client-5.3.3 | ################################################################################ | 100% \nkeras-applications-1 | ################################################################################ | 100% \nkeras-preprocessing- | ################################################################################ | 100% \nmatplotlib-3.1.2 | ################################################################################ | 100% \npatsy-0.5.1 | ################################################################################ | 100% \npip-19.3.1 | ################################################################################ | 100% \nprompt_toolkit-3.0.2 | ################################################################################ | 100% \npytorch-0.4.1 | ################################################################################ | 100% \nscikit-learn-0.22 | ################################################################################ | 100% \ntensorboard-1.14.0 | ################################################################################ | 100% \ntensorflow-estimator | ################################################################################ | 100% \nipython-7.10.1 | ################################################################################ | 100% \nstatsmodels-0.10.2 | ################################################################################ | 100% \ntensorflow-base-1.14 | ################################################################################ | 100% \ntorchvision-0.2.1 | ################################################################################ | 100% \nipykernel-5.1.3 | ################################################################################ | 100% \nseaborn-0.9.0 | ################################################################################ | 100% \ntensorflow-1.14.0 | ################################################################################ | 100% \nspyder-kernels-0.5.2 | ################################################################################ | 100% \nPreparing transaction: done \nVerifying transaction: done \nExecuting transaction: done \n# \n# To activate this environment, use: \n# > activate ztest \n# \n# To deactivate an active environment, use: \n# > deactivate \n# \n# * for power-users using bash, you must source \n# \n \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>activate ztest \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>pip install -e . \nObtaining file:///D:/_devs/Python01/gitdev/mlmodels \nInstalling collected packages: mlmodels \n Running setup.py develop for mlmodels \nSuccessfully installed mlmodels \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>models \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'models')() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 489, in load_entry_\npoint \n return get_distribution(dist).load_entry_point(group, name) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2852, in load_entry\n_point \n return ep.load() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2443, in load \n return self.resolve() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2449, in resolve \n module = __import__(self.module_name, fromlist=['__name__'], level=0) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 104, in <module> \n from mlmodels.util import load_config, get_recursive_files, get_recursive_folder \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\", line 2, in <module> \n import toml \nModuleNotFoundError: No module named 'toml' \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>pip install toml \nCollecting toml \n Downloading https://files.pythonhosted.org/packages/a2/12/ced7105d2de62fa7c8fb5fce92cc4ce66b57c95fb875e9318dba\n7f8c5db0/toml-0.10.0-py2.py3-none-any.whl \nInstalling collected packages: toml \nSuccessfully installed toml-0.10.0 \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>models \ntest \nmodel_tf.1_lstm \nFailed Module model_tf notfound, No module named 'util', tuple index out of range \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>cd .. \n \n(ztest) D:\\_devs\\Python01\\gitdev>models \ntest \nmodel_tf.1_lstm \nFailed Module model_tf notfound, No module named 'util', tuple index out of range \n \n(ztest) D:\\_devs\\Python01\\gitdev>optim \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\optim-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'optim')() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 489, in load_entry_\npoint \n return get_distribution(dist).load_entry_point(group, name) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2852, in load_entry\n_point \n return ep.load() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2443, in load \n return self.resolve() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2449, in resolve \n module = __import__(self.module_name, fromlist=['__name__'], level=0) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\optim.py\", line 50, in <module> \n import optuna \nModuleNotFoundError: No module named 'optuna' \n \n(ztest) D:\\_devs\\Python01\\gitdev>pip install optuna \nCollecting optuna \n Downloading https://files.pythonhosted.org/packages/d4/6a/4d80b3014797cf318a5252afb27031e9e7502854fb7930f27db0\nee10bb75/optuna-0.19.0.tar.gz (126kB) \n |████████████████████████████████| 133kB 3.2MB/s \nCollecting alembic \n Downloading https://files.pythonhosted.org/packages/dc/6d/3c1411dfdcf089ec89ce5e2222deb2292f39b6b1a5911222e15a\nf9fe5a92/alembic-1.3.2.tar.gz (1.1MB) \n |████████████████████████████████| 1.1MB 3.3MB/s \nCollecting cliff \n Downloading https://files.pythonhosted.org/packages/69/f3/3e15cf215791f1fc32a33751fcddc3f142113e740f0b52efeadf\n617a559f/cliff-2.17.0-py2.py3-none-any.whl (77kB) \n |████████████████████████████████| 81kB 5.5MB/s \nCollecting colorlog \n Downloading https://files.pythonhosted.org/packages/00/0d/22c73c2eccb21dd3498df7d22c0b1d4a30f5a5fb3feb64e1ce06\nbc247747/colorlog-4.1.0-py2.py3-none-any.whl \nRequirement already satisfied: numpy in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from optuna) (1.17.\n3) \nRequirement already satisfied: scipy in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from optuna) (1.3.1\n) \nRequirement already satisfied: six in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from optuna) (1.13.0)\n \nCollecting sqlalchemy>=1.1.0 \n Downloading https://files.pythonhosted.org/packages/17/7f/35879c73859368ad19a952b69ee780aa97fc30350dabd45fb948\nd6a4e3ea/SQLAlchemy-1.3.12.tar.gz (6.0MB) \n |████████████████████████████████| 6.0MB 6.4MB/s \nRequirement already satisfied: tqdm in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from optuna) (4.40.1\n) \nCollecting typing \n Downloading https://files.pythonhosted.org/packages/fe/2e/b480ee1b75e6d17d2993738670e75c1feeb9ff7f64452153cf01\n8051cc92/typing-3.7.4.1-py3-none-any.whl \nCollecting Mako \n Downloading https://files.pythonhosted.org/packages/b0/3c/8dcd6883d009f7cae0f3157fb53e9afb05a0d3d33b3db1268ec2\ne6f4a56b/Mako-1.1.0.tar.gz (463kB) \n |████████████████████████████████| 471kB 6.4MB/s \nCollecting python-editor>=0.3 \n Downloading https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f\n35513717/python_editor-1.0.4-py3-none-any.whl \nRequirement already satisfied: python-dateutil in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from alem\nbic->optuna) (2.8.1) \nCollecting PyYAML>=3.10 \n Downloading https://files.pythonhosted.org/packages/58/1f/00682a829890131bcc71755e78d595e439bd6b291ea9bd5ced93\ned57cc3d/PyYAML-5.2-cp36-cp36m-win_amd64.whl (214kB) \n |████████████████████████████████| 215kB 6.4MB/s \nCollecting cmd2>=0.6.7 \n Downloading https://files.pythonhosted.org/packages/97/0d/822f97a4c17a034629f8f9a9f1d293e08c94239e4673f1f52f85\n5b586fae/cmd2-0.9.22-py3-none-any.whl (105kB) \n |████████████████████████████████| 112kB 6.4MB/s \nRequirement already satisfied: pyparsing>=2.1.0 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from cli\nff->optuna) (2.4.5) \nCollecting stevedore>=1.20.0 \n Downloading https://files.pythonhosted.org/packages/b1/e1/f5ddbd83f60b03f522f173c03e406c1bff8343f0232a292ac96a\na633b47a/stevedore-1.31.0-py2.py3-none-any.whl (43kB) \n |████████████████████████████████| 51kB 3.2MB/s \nCollecting pbr!=2.1.0,>=2.0.0 \n Downloading https://files.pythonhosted.org/packages/7a/db/a968fd7beb9fe06901c1841cb25c9ccb666ca1b9a19b114d1bbe\ndf1126fc/pbr-5.4.4-py2.py3-none-any.whl (110kB) \n |████████████████████████████████| 112kB ... \nCollecting PrettyTable<0.8,>=0.7.1 \n Downloading https://files.pythonhosted.org/packages/ef/30/4b0746848746ed5941f052479e7c23d2b56d174b82f4fd34a25e\n389831f5/prettytable-0.7.2.tar.bz2 \nRequirement already satisfied: colorama; sys_platform == \"win32\" in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-p\nackages (from colorlog->optuna) (0.4.3) \nCollecting MarkupSafe>=0.9.2 \n Downloading https://files.pythonhosted.org/packages/b9/82/833c7714951bff8f502ed054e6fbd8bd00e083d1fd96de6a4690\n5cf23378/MarkupSafe-1.1.1-cp36-cp36m-win_amd64.whl \nRequirement already satisfied: wcwidth>=0.1.7 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from cmd2>\n=0.6.7->cliff->optuna) (0.1.7) \nRequirement already satisfied: pyreadline; sys_platform == \"win32\" in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site\n-packages (from cmd2>=0.6.7->cliff->optuna) (2.1) \nCollecting pyperclip>=1.6 \n Downloading https://files.pythonhosted.org/packages/2d/0f/4eda562dffd085945d57c2d9a5da745cfb5228c02bc90f2c74bb\nac746243/pyperclip-1.7.0.tar.gz \nCollecting attrs>=16.3.0 \n Using cached https://files.pythonhosted.org/packages/a2/db/4313ab3be961f7a763066401fb77f7748373b6094076ae2bda2\n806988af6/attrs-19.3.0-py2.py3-none-any.whl \nBuilding wheels for collected packages: optuna, alembic, sqlalchemy, Mako, PrettyTable, pyperclip \n Building wheel for optuna (setup.py) ... done \n Created wheel for optuna: filename=optuna-0.19.0-cp36-none-any.whl size=170203 sha256=be21e712e56ca86fc032cc23\n5c097d3868987fb7abf9207394b0699940c8bef3 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\49\\bf\\47\\090a43457caeff74397397da1c98a8a\naed685257c16a5ba1f0 \n Building wheel for alembic (setup.py) ... done \n Created wheel for alembic: filename=alembic-1.3.2-py2.py3-none-any.whl size=151132 sha256=40484edcfd4367cbeba1\n6f1e70452edc95748a5526438bd46430ecaee432947e \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\5c\\66\\53\\e0633382ac8625ab1c099db6a290d1b\n6b24f849a4666a57105 \n Building wheel for sqlalchemy (setup.py) ... done \n Created wheel for sqlalchemy: filename=SQLAlchemy-1.3.12-cp36-cp36m-win_amd64.whl size=1167984 sha256=80b8ef5f\nb8ac24978fa9f1ca93c7f2eb0377d1476af241f3292f806f7e3c2766 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\ee\\33\\44\\0788a6e806866ae2e246d5cd841d074\n98a46bcb3f3c42ea5a4 \n Building wheel for Mako (setup.py) ... done \n Created wheel for Mako: filename=Mako-1.1.0-cp36-none-any.whl size=75369 sha256=73f10c24fa41cc55d8d0c073fb928d\n7bb58dbcbd202450b9f9e535d127f86878 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\98\\32\\7b\\a291926643fc1d1e02593e0d9e247c5\na866a366b8343b7aa27 \n Building wheel for PrettyTable (setup.py) ... done \n Created wheel for PrettyTable: filename=prettytable-0.7.2-cp36-none-any.whl size=13705 sha256=7d27113ccdb61788\nc65ac855a58848a6804c0f102e6b0b9cb8e3eba0f4512290 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\80\\34\\1c\\3967380d9676d162cb59513bd9dc862\nd0584e045a162095606 \n Building wheel for pyperclip (setup.py) ... done \n Created wheel for pyperclip: filename=pyperclip-1.7.0-cp36-none-any.whl size=8364 sha256=bc4f489329c7387ef1b2c\n7f1bb7e18c85667c3912b82d3ef161ddb1321fc8b39 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\92\\f0\\ac\\2ba2972034e98971c3654ece337ac61\ne546bdeb34ca960dc8c \nSuccessfully built optuna alembic sqlalchemy Mako PrettyTable pyperclip \nInstalling collected packages: sqlalchemy, MarkupSafe, Mako, python-editor, alembic, PyYAML, pyperclip, attrs, c\nmd2, pbr, stevedore, PrettyTable, cliff, colorlog, typing, optuna \nSuccessfully installed Mako-1.1.0 MarkupSafe-1.1.1 PrettyTable-0.7.2 PyYAML-5.2 alembic-1.3.2 attrs-19.3.0 cliff\n-2.17.0 cmd2-0.9.22 colorlog-4.1.0 optuna-0.19.0 pbr-5.4.4 pyperclip-1.7.0 python-editor-1.0.4 sqlalchemy-1.3.12\n stevedore-1.31.0 typing-3.7.4.1 \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>optim \n \n ('model_pars', {'learning_rate': {'type': 'log_uniform', 'init': 0.01, 'range': [0.001, 0.1]}, 'num_layers': {\n'type': 'int', 'init': 2, 'range': [2, 4]}, 'size': {'type': 'int', 'init': 6, 'range': [6, 6]}, 'output_size': \n{'type': 'int', 'init': 6, 'range': [6, 6]}, 'size_layer': {'type': 'categorical', 'value': [128, 256]}, 'timest\nep': {'type': 'categorical', 'value': [5]}, 'epoch': {'type': 'categorical', 'value': [2]}}) \nmodel_tf.1_lstm \nTraceback (most recent call last): \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 133, in module_load \n module = import_module( f\"mlmodels.{model_name}\") \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\importlib\\__init__.py\", line 126, in import_module \n return _bootstrap._gcd_import(name[level:], package, level) \n File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import \n File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load \n File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked \n File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked \n File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module \n File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\1_lstm.py\", line 28, in <module> \n from util import os_package_root_path, os_file_current, os_file_parent \nModuleNotFoundError: No module named 'util' \n \nDuring handling of the above exception, another exception occurred: \n \nTraceback (most recent call last): \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 144, in module_load \n model_name = str(Path(model_uri).parts[-2]) +\".\"+ str(model_name ) \nIndexError: tuple index out of range \n \nDuring handling of the above exception, another exception occurred: \n \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\optim-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'optim')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\optim.py\", line 339, in main \n test_fast() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\optim.py\", line 267, in test_fast \n compute_pars={\"engine\": \"optuna\" , \"method\" : \"prune\"} ) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\optim.py\", line 90, in optim \n save_folder, log_folder, ntrials) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\optim.py\", line 122, in optim_optuna \n module = module_load(modelname) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 149, in module_load \n raise NameError( f\"Module {model_name} notfound, {e1}, {e2}\") \nNameError: Module model_tf notfound, No module named 'util', tuple index out of range \n \n(ztest) D:\\_devs\\Python01\\gitdev>models \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'models')() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 489, in load_entry_\npoint \n return get_distribution(dist).load_entry_point(group, name) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2852, in load_entry\n_point \n return ep.load() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2443, in load \n return self.resolve() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2449, in resolve \n module = __import__(self.module_name, fromlist=['__name__'], level=0) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 104, in <module> \n from mlmodels.mlmodels.util import load_config, get_recursive_files, get_recursive_folder \nModuleNotFoundError: No module named 'mlmodels.mlmodels' \n \n(ztest) D:\\_devs\\Python01\\gitdev>models --do model_list \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'models')() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 489, in load_entry_\npoint \n return get_distribution(dist).load_entry_point(group, name) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2852, in load_entry\n_point \n return ep.load() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2443, in load \n return self.resolve() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2449, in resolve \n module = __import__(self.module_name, fromlist=['__name__'], level=0) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 104, in <module> \n from mlmodels.mlmodels.util import load_config, get_recursive_files, get_recursive_folder \nModuleNotFoundError: No module named 'mlmodels.mlmodels' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>models --do model_list \nmodel_list \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 386, in main \n module_names = get_recursive_folder(folder, r\"model*/*.py\" ) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\", line 155, in get_recursive_folder \n outFiles += get_recursive_files(os.path.join(folderPath, file), ext) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\", line 134, in get_recursive_files \n elif re.match(ext, file): \nNameError: name 're' is not defined \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>models --do model_list \nmodel_list \nmodel.py \nmodel.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>models \ntest \nmodel_tf.1_lstm \nFailed Module model_tf notfound, No module named 'util', tuple index out of range \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>models \ntest \nmodel_tf/1_lstm.py \nFailed Module model_tf.1_lstm notfound, No module named 'util', tuple index out of range \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>models \ntest \nFailed No module named 'util' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>models \ntest \nFailed cannot import name 'os_file_current' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>models \ntest \nmodel_tf/1_lstm.py \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n \n ('############# Data, Params preparation #################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n \n ('############ Model preparation #########################',) \nmodel_tf.1_lstm \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \n<mlmodels.model_tf.1_lstm.Model object at 0x000001ADD945B390> \n \n ('############ Model fit ##################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-5 \nOMP: Info #213: KMP_AFFINITY: decoding x2APIC ids. \nOMP: Info #276: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info \nOMP: Info #156: KMP_AFFINITY: 6 available OS procs \nOMP: Info #157: KMP_AFFINITY: Uniform topology \nOMP: Info #191: KMP_AFFINITY: 1 socket x 3 cores/socket x 2 threads/core (3 total cores) \nOMP: Info #215: KMP_AFFINITY: OS proc to physical thread map: \nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 9380 thread 0 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 13060 thread 1 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 11516 thread 2 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 1764 thread 3 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 2136 thread 4 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 860 thread 5 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 12908 thread 6 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 14128 thread 7 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 13660 thread 8 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 9608 thread 9 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 6500 thread 10 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 13532 thread 11 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 11968 thread 12 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 5552 thread 13 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 6420 thread 14 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 8216 thread 15 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 13732 thread 17 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 10004 tid 14304 thread 16 bound to OS proc set 3 \nfit success <tensorflow.python.client.session.InteractiveSession object at 0x000001ADE3C88E48> \n \n ('############ Prediction##########################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \n.. ... ... ... ... ... ... \n60 0.560716 0.537236 0.633156 0.609599 0.609599 0.378373 \n61 0.590968 0.536554 0.639892 0.598604 0.598604 0.346890 \n62 0.527891 0.553723 0.648035 0.627071 0.627071 0.227211 \n63 0.626475 0.591245 0.732181 0.656793 0.656793 0.210688 \n64 0.591182 0.542012 0.680708 0.655287 0.655287 0.143610 \n \n[65 rows x 6 columns] \n[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 \n 0.00000000e+00 0.00000000e+00] \n [ 4.28199992e-02 1.42883122e-01 2.15737492e-01 1.01851255e-01 \n 1.28467187e-01 1.18364617e-01] \n [ 7.61565939e-02 1.20212071e-01 -5.94490878e-02 1.00219317e-01 \n -3.09849959e-02 9.05458480e-02] \n [ 1.75466597e-01 1.88701779e-01 8.44168216e-02 1.42738745e-01 \n 2.35793382e-01 1.94177821e-01] \n [ 3.01984251e-01 1.64963663e-01 5.98988272e-02 1.66147172e-01 \n 1.80530518e-01 1.40020311e-01] \n [ 3.34381089e-02 1.63798034e-01 2.24664539e-01 2.53736138e-01 \n 2.49300390e-01 -7.55380392e-02] \n [ 3.00825864e-01 2.28330195e-02 3.81034046e-01 3.57622266e-01 \n 2.19948202e-01 7.73245096e-02] \n [ 1.90480590e-01 4.20586854e-01 3.63123983e-01 1.13898247e-01 \n 6.13270521e-01 -1.95259735e-01] \n [ 1.47163317e-01 2.30214298e-01 1.71397403e-01 3.00365120e-01 \n -2.62062196e-02 -2.30471507e-01] \n [ 1.18430674e-01 3.13476354e-01 1.20653957e-01 -2.63972953e-03 \n 5.59549406e-02 2.51385808e-01] \n [ 2.09951222e-01 1.00572966e-01 5.04693568e-01 3.01620483e-01 \n 2.95662791e-01 7.34893084e-02] \n [ 1.69162214e-01 2.79587775e-01 2.47147679e-01 3.90177965e-01 \n 4.57388610e-01 1.36585534e-01] \n [ 3.80888470e-02 -6.37186840e-02 3.78811181e-01 1.42434165e-01 \n 1.30858824e-01 1.11804865e-01] \n [ 1.66667998e-01 2.95769125e-01 1.25634354e-02 3.01806599e-01 \n 4.91429493e-02 9.97269899e-02] \n [ 1.35113731e-01 7.07136020e-02 3.03123564e-01 2.86993116e-01 \n 1.91837192e-01 2.86218733e-01] \n [ 1.82888761e-01 1.40457273e-01 4.24961895e-01 9.84958708e-02 \n 4.15848136e-01 -9.31793600e-02] \n [ 2.51143694e-01 6.63162917e-02 4.70721036e-01 3.08453310e-02 \n 2.12113649e-01 -2.53102839e-01] \n [ 2.79188544e-01 2.72867203e-01 6.12169877e-03 3.83809656e-01 \n 6.87153816e-01 1.53074205e-01] \n [-7.91585892e-02 -1.87293068e-02 8.86971056e-02 5.86962223e-01 \n 2.05915242e-01 -5.66214398e-02] \n [ 9.86251384e-02 2.16925949e-01 1.02903083e-01 2.10555136e-01 \n 2.90759891e-01 -7.42515475e-02] \n [ 9.46484059e-02 -7.66654536e-02 2.15555325e-01 -6.32539988e-02 \n 8.01979750e-02 8.21015462e-02] \n [ 1.94922730e-01 3.01939696e-01 -1.03399232e-01 1.46048009e-01 \n 4.00541961e-01 1.31705031e-01] \n [ 3.22262734e-01 4.64683324e-02 1.55048922e-01 1.30510449e-01 \n 1.09114140e-01 -8.51283893e-02] \n [ 3.68362457e-01 2.96511173e-01 2.69158036e-01 7.63891935e-01 \n 4.57581639e-01 2.31709525e-01] \n [ 3.39450628e-01 2.83947438e-01 1.93098292e-01 1.49579570e-01 \n 3.93500686e-01 -4.09165099e-02] \n [ 6.44736439e-02 3.06231454e-02 4.02719639e-02 8.63622651e-02 \n 1.92004293e-01 -6.90334216e-02] \n [ 1.95356756e-01 1.03010811e-01 7.54593015e-02 1.34677321e-01 \n 1.91386327e-01 2.67042249e-01] \n [ 2.01528575e-02 2.24431187e-01 7.49438405e-02 1.71753272e-01 \n 2.37533331e-01 -2.04486568e-02] \n [ 1.79581091e-01 2.73200184e-01 6.07183874e-01 -6.77508414e-02 \n 4.76217508e-01 -3.53654996e-02] \n [-2.09068909e-01 2.24518120e-01 3.02124172e-01 1.81096166e-01 \n 9.21116546e-02 1.68647189e-02] \n [ 1.90685511e-01 5.42848930e-02 3.91928732e-01 3.54513645e-01 \n 4.35317159e-01 2.89021973e-02] \n [ 2.07443401e-01 3.91048312e-01 2.57941931e-01 -2.19819009e-01 \n 2.18575537e-01 3.09307724e-01] \n [ 2.47425422e-01 -1.57305807e-01 2.22408146e-01 1.57533631e-01 \n -5.87047711e-02 1.41236022e-01] \n [ 1.90551981e-01 1.52816832e-01 -1.75753176e-01 1.87540486e-01 \n 2.11987913e-01 1.07226789e-01] \n [-7.86731243e-02 5.14789343e-01 4.75499123e-01 2.75037140e-01 \n 5.41227579e-01 2.82643706e-01] \n [ 5.75046360e-01 6.50635779e-01 7.60259479e-02 1.93926111e-01 \n 3.39643240e-01 2.82756597e-01] \n [ 2.27125645e-01 3.89270157e-01 7.85530746e-01 3.43594581e-01 \n 4.86953467e-01 1.95682347e-01] \n [-1.93819627e-02 -1.87323898e-01 7.71281719e-02 1.10995159e-01 \n 2.27934923e-02 1.30098686e-01] \n [ 8.33951950e-01 6.51130855e-01 2.34265596e-01 5.07879496e-01 \n 4.34373707e-01 8.60822320e-01] \n [ 4.77788150e-01 3.13748002e-01 6.93399906e-02 1.30717605e-02 \n 1.49218291e-01 1.95653170e-01] \n [ 1.98558494e-01 3.72333705e-01 7.23645389e-02 4.49368984e-01 \n 1.19387567e-01 4.78921533e-01] \n [ 7.97003746e-01 -4.49468009e-03 2.70239830e-01 -1.35686904e-01 \n 7.60872602e-01 -1.46030813e-01] \n [ 2.25154445e-01 -2.66708255e-01 2.27901042e-01 4.27262962e-01 \n 5.98693967e-01 -8.66374820e-02] \n [ 5.28967202e-01 3.84764701e-01 4.70212132e-01 1.47317722e-01 \n 1.45022854e-01 2.60676473e-01] \n [ 2.72034332e-02 2.20162451e-01 2.52581775e-01 2.30485737e-01 \n 1.51510760e-01 -9.06202644e-02] \n [ 3.10184360e-01 3.29784185e-01 7.19770044e-02 4.88816172e-01 \n 1.33814260e-01 5.97956717e-01] \n [ 6.12640381e-01 2.64565706e-01 1.95575312e-01 6.18808866e-02 \n 5.03556848e-01 6.87194824e-01] \n [ 9.36022341e-01 6.90031767e-01 9.68853951e-01 1.44359261e-01 \n 5.65159261e-01 3.33798259e-01] \n [ 1.41853318e-01 9.52177584e-01 1.18838936e-01 1.95527628e-01 \n 1.68919072e-01 4.09582675e-01] \n [-5.20663820e-02 6.71526432e-01 5.54509938e-01 4.84275222e-01 \n 2.77151853e-01 5.04413396e-02] \n [-2.20505312e-01 7.21198022e-01 5.52980006e-02 3.08341652e-01 \n 5.75131893e-01 5.02071977e-01] \n [ 2.57100791e-01 3.59627336e-01 3.03788245e-01 1.36847869e-01 \n 2.71072268e-01 -3.27240944e-01] \n [-1.21624283e-02 3.37558836e-02 6.01908028e-01 -1.34401426e-01 \n 7.20328391e-01 8.86230618e-02] \n [ 5.02406478e-01 3.66399139e-01 -1.34852201e-01 4.20275241e-01 \n 7.87283182e-01 -1.80232793e-01] \n [ 1.94190651e-01 -3.53622258e-01 3.08177799e-01 3.89602870e-01 \n 2.87647009e-01 6.53213441e-01] \n [ 6.23344064e-01 -3.05532552e-02 -2.53925361e-02 7.61053205e-01 \n 8.00298333e-01 5.56535274e-02] \n [ 2.74305642e-01 6.27831593e-02 -9.78715718e-02 6.22384787e-01 \n 7.26127997e-04 -5.41001260e-02] \n [ 2.85429746e-01 4.67108518e-01 1.50569156e-01 -1.86849117e-01 \n 3.70340317e-01 3.40269595e-01] \n [ 1.15049040e+00 4.46256727e-01 1.06287277e+00 4.95041817e-01 \n 7.52213597e-01 -3.52676958e-02] \n [ 2.22974703e-01 4.46731895e-01 1.20367324e+00 5.03872275e-01 \n -1.25244126e-01 4.55700755e-01] \n [ 2.09582999e-01 5.45631468e-01 1.47487774e-01 5.96509933e-01 \n 1.52383402e-01 -3.48554999e-02] \n [ 2.35515237e-01 6.03077829e-01 6.97229087e-01 6.88514113e-01 \n 2.44453669e-01 -5.54282367e-01] \n [ 9.92969453e-01 3.93884718e-01 9.66714680e-01 -3.46015617e-02 \n 8.83066237e-01 6.34847701e-01] \n [ 4.38152283e-01 9.38054696e-02 -3.16166021e-02 3.75658333e-01 \n -4.92622256e-02 4.37320918e-01] \n [ 2.26019159e-01 5.30808792e-02 8.83906782e-02 6.93947077e-02 \n -1.32941484e-01 6.96239844e-02]] \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>models \ntest \nmodel_tf/1_lstm.py \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n \n ('############# Data, Params preparation #################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n \n ('############ Model preparation #########################',) \nmodel_tf.1_lstm \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \n<mlmodels.model_tf.1_lstm.Model object at 0x000002A3012BB390> \n \n ('############ Model fit ##################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-5 \nOMP: Info #213: KMP_AFFINITY: decoding x2APIC ids. \nOMP: Info #276: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info \nOMP: Info #156: KMP_AFFINITY: 6 available OS procs \nOMP: Info #157: KMP_AFFINITY: Uniform topology \nOMP: Info #191: KMP_AFFINITY: 1 socket x 3 cores/socket x 2 threads/core (3 total cores) \nOMP: Info #215: KMP_AFFINITY: OS proc to physical thread map: \nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 7576 thread 0 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 1984 thread 1 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 7504 thread 2 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 3416 thread 3 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 12260 thread 4 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 7572 thread 5 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 9840 thread 6 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 10912 thread 7 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 7864 thread 8 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 11268 thread 9 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 11152 thread 10 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 13360 thread 11 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 252 thread 12 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 5460 thread 13 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 7532 thread 14 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 14316 thread 15 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 8152 thread 16 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11212 tid 13196 thread 17 bound to OS proc set 5 \nfit success <tensorflow.python.client.session.InteractiveSession object at 0x000002A30BAD8E48> \n \n ('############ Prediction##########################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \n.. ... ... ... ... ... ... \n60 0.560716 0.537236 0.633156 0.609599 0.609599 0.378373 \n61 0.590968 0.536554 0.639892 0.598604 0.598604 0.346890 \n62 0.527891 0.553723 0.648035 0.627071 0.627071 0.227211 \n63 0.626475 0.591245 0.732181 0.656793 0.656793 0.210688 \n64 0.591182 0.542012 0.680708 0.655287 0.655287 0.143610 \n \n[65 rows x 6 columns] \n[[ 0. 0. 0. 0. 0. 0. ] \n [ 0.0491344 0.08221696 0.12161008 0.12257093 0.14302133 0.06741921] \n [ 0.02179525 0.14427368 -0.02195228 0.00245948 0.080481 0.13223596] \n [ 0.21032174 0.27696511 0.00320935 0.28819716 0.19967049 0.06932873] \n [ 0.24610466 0.09481727 0.18971884 0.25256935 0.27842012 0.04338734] \n [ 0.05823352 0.31972206 0.00787029 0.14627552 0.04366691 0.14986338] \n [ 0.26374376 0.29827365 0.38800204 0.17894472 0.13522084 -0.13075607] \n [ 0.24085024 0.13196972 -0.10723568 0.25780174 0.07439879 0.40228155] \n [ 0.18873918 0.44897732 0.08766863 0.30193305 0.24255915 -0.14121346] \n [ 0.0827446 0.2677424 0.18816823 -0.06359589 -0.06359017 0.10281278] \n [ 0.15933311 0.11883332 0.03879757 0.55256701 0.50580066 0.15507393] \n [ 0.14987157 0.35425237 0.3889071 0.27289486 0.16720121 0.25125238] \n [ 0.35314387 0.60901386 0.15241744 0.39692658 0.44390169 0.39264104] \n [ 0.55162346 0.68618268 0.07493737 0.71112376 0.54859895 0.2151136 ] \n [ 0.11977201 0.02376577 0.10754606 0.17340355 0.27165028 -0.09078944] \n [ 0.25389948 0.25543573 0.11011785 0.28998762 0.03606323 0.25475788] \n [ 0.34564477 0.42566302 0.37711024 0.60674644 0.46387455 0.22036703] \n [ 0.04873487 0.11811102 0.09223758 0.12849616 0.14736578 0.04182578] \n [ 0.10579075 0.06415333 0.14341217 0.22240417 -0.02023996 0.1433164 ] \n [-0.03429627 0.08729138 0.19896983 0.32981151 0.13605227 -0.05657844] \n [-0.09991156 0.1609337 0.00943393 0.14927697 0.37392601 0.23570046] \n [ 0.16110028 -0.07538671 0.03685839 0.40902883 -0.01016195 0.04478089] \n [ 0.17323601 0.14945531 0.28127286 0.03229792 0.26610202 0.07482411] \n [ 0.3429836 0.29619044 0.194159 0.35157645 0.36433771 0.02848805] \n [ 0.12016992 0.07706001 -0.03349274 0.24792682 0.31725064 -0.07005994] \n [ 0.31975958 0.2961542 -0.02632445 0.27437481 0.47280496 0.11883425] \n [ 0.0747526 0.22163676 0.1728683 0.27292398 0.21178032 0.16380535] \n [ 0.26601091 0.23909698 -0.00402357 0.05050859 0.25547808 0.05391898] \n [ 0.24521695 0.3124446 0.30569181 0.61144477 0.08763501 0.25382832] \n [ 0.2247514 -0.01078439 0.08144888 0.47447905 0.5224396 0.04074898] \n [ 0.69537163 0.4678961 0.43317947 0.0068811 0.37868392 0.10300238] \n [ 0.07089966 0.62411553 -0.17405125 0.43169528 0.19544126 0.23947227] \n [ 0.40272552 0.07916693 0.35361674 0.33059806 -0.090389 0.18214071] \n [-0.02072004 -0.01534841 0.18083087 0.11261919 0.08121049 -0.0829263 ] \n [ 0.19846091 0.30753186 0.15693699 0.11219215 0.10810119 -0.15846157] \n [ 0.22207424 0.52588111 0.04175495 0.5583092 0.43863657 0.03903289] \n [ 0.44418341 0.33637279 0.20761043 0.25496417 0.54579425 0.14041466] \n [ 0.17082953 0.17590222 0.1516335 0.72884554 0.60212159 0.71943963] \n [-0.01639836 0.28816089 0.10679424 0.40161356 0.23982997 0.25893831] \n [-0.02313676 0.16780777 0.07833309 0.72141546 0.51653045 0.18411244] \n [ 0.32588235 0.87174857 0.47495586 0.42737356 0.12696691 0.31076238] \n [ 0.27314192 0.0516864 0.40716815 0.73235601 0.19621247 0.38390663] \n [ 0.37820488 0.56840092 0.49546492 0.19076526 0.48104009 0.3025963 ] \n [ 0.30256909 0.22692254 0.10249478 0.28457484 0.30038977 0.02264802] \n [ 0.17762294 -0.26852125 0.30790189 0.62748092 0.0568015 -0.12481479] \n [ 0.59324414 0.88567811 0.75389665 0.55870777 0.76601148 0.27446717] \n [ 0.24892621 0.21745372 -0.02009124 0.25318554 0.27806291 0.16689305] \n [ 0.29142478 -0.01359336 0.02359467 0.17913236 0.26697484 0.39335266] \n [ 0.00696586 -0.12386758 0.17742388 -0.009501 0.11307857 0.03075584] \n [ 0.58074743 0.93094456 0.0980427 0.07798269 0.82174569 0.38466558] \n [ 0.55779511 0.46519509 0.38395679 0.21022251 0.30632856 -0.70663708] \n [ 0.27409858 1.00567329 0.00167762 0.34858489 0.32292828 0.68939734] \n [ 0.39502496 0.63116795 0.05063563 0.34475771 0.11066999 0.06878562] \n [-0.03946734 0.06810946 0.26045486 0.4598203 0.15229376 0.55619651] \n [ 0.46163139 0.32639968 0.25438002 0.54531962 1.05517447 0.30232349] \n [ 0.1656317 0.42579702 0.27053332 0.50407934 0.32179892 0.16547239] \n [ 0.40852249 -0.02556854 0.59766412 0.21942806 0.63266647 -0.41110647] \n [ 0.54985726 1.17258477 -0.25872526 0.42827159 0.87449104 0.57587761] \n [ 0.69510376 0.35030091 0.06290323 0.63736939 0.0626139 0.10628113] \n [ 0.49976042 0.33313689 0.88181722 0.33530185 1.03643191 0.99227297] \n [ 1.05895269 0.30823296 -0.06020848 0.7023232 0.45535108 0.23100477] \n [ 0.42601675 0.71215129 0.86949962 0.3827388 0.04974701 0.62938559] \n [ 0.67354107 0.41162384 -0.15011786 0.37040544 0.54865521 -0.45908269] \n [ 0.81575155 0.71836072 0.39945948 0.43258512 0.88636953 -0.10583305] \n [ 0.25698486 0.19250016 0.17078787 -0.32685152 0.2726984 -0.09011013]] \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>cd mlmodels \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>python pypi.py \nProgram Started \nCurrent version: Version('0.11.1') \nNew Version: Version('0.12.1') \n'\"command -v nvidia-smi\"' is not recognized as an internal or external command, \noperable program or batch file. \nrunning sdist \nrunning egg_info \nwriting mlmodels.egg-info\\PKG-INFO \nwriting dependency_links to mlmodels.egg-info\\dependency_links.txt \nwriting entry points to mlmodels.egg-info\\entry_points.txt \nwriting top-level names to mlmodels.egg-info\\top_level.txt \nreading manifest file 'mlmodels.egg-info\\SOURCES.txt' \nreading manifest template 'MANIFEST.in' \nwriting manifest file 'mlmodels.egg-info\\SOURCES.txt' \nrunning check \ncreating mlmodels-0.12.1 \ncreating mlmodels-0.12.1\\mlmodels \ncreating mlmodels-0.12.1\\mlmodels.egg-info \ncreating mlmodels-0.12.1\\mlmodels\\dataset \ncreating mlmodels-0.12.1\\mlmodels\\model_rank \ncreating mlmodels-0.12.1\\mlmodels\\model_tf \ncreating mlmodels-0.12.1\\mlmodels\\model_tf\\deepar \ncreating mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\dataset \ncreating mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\model \ncreating mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\utils \ncreating mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying files to mlmodels-0.12.1... \ncopying MANIFEST.in -> mlmodels-0.12.1 \ncopying README.md -> mlmodels-0.12.1 \ncopying setup.py -> mlmodels-0.12.1 \ncopying mlmodels\\__init__.py -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\cli_mlmodels -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\config.toml -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\data.py -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\distri_run.sh -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\distri_tch.py -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\models.py -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\models_config.json -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\optim.py -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\optim_config.json -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\util.py -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels\\ztest.py -> mlmodels-0.12.1\\mlmodels \ncopying mlmodels.egg-info\\PKG-INFO -> mlmodels-0.12.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\SOURCES.txt -> mlmodels-0.12.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\dependency_links.txt -> mlmodels-0.12.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\entry_points.txt -> mlmodels-0.12.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\top_level.txt -> mlmodels-0.12.1\\mlmodels.egg-info \ncopying mlmodels\\dataset\\GOOG-year.csv -> mlmodels-0.12.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG-year_small.csv -> mlmodels-0.12.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG.csv -> mlmodels-0.12.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\TSLA.csv -> mlmodels-0.12.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\eur-myr.csv -> mlmodels-0.12.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\oil.csv -> mlmodels-0.12.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\usd-myr.csv -> mlmodels-0.12.1\\mlmodels\\dataset \ncopying mlmodels\\model_rank\\LambdaRank.py -> mlmodels-0.12.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\RankNet.py -> mlmodels-0.12.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\__init__.py -> mlmodels-0.12.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\load_mslr.py -> mlmodels-0.12.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\metrics.py -> mlmodels-0.12.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\utils.py -> mlmodels-0.12.1\\mlmodels\\model_rank \ncopying mlmodels\\model_tf\\10_encoder_vanilla.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\11_bidirectional_vanilla.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\12_vanilla_2path.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\13_lstm_seq2seq.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\14_lstm_attention.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\19_lstm_dilated.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\1_lstm.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\20_only_attention.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\21_multihead_attention.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\22_lstm_bahdanau.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\23_lstm_luong.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\25_dnc.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\26_lstm_residual.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\27_byte_net.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\28_attention_is_all_you_need.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\29_fairseq.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\2_encoder_lstm.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\3_bidirectional_lstm.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\4_lstm_2path.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\50lstm attention.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\5_gru.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\6_encoder_gru.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\7_bidirectional_gru.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\8_gru_2path.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\9_vanilla.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\__init__.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\access.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\addressing.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\autoencoder.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\dnc.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\util.py -> mlmodels-0.12.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\deepar\\__init__.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\settings.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\model\\__init__.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\layers.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\loss.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\lstm.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\utils\\__init__.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\deepar\\utils \ncopying mlmodels\\model_tf\\rl\\0_template_rl.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\1.turtle-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\r\nl \ncopying mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\__init__.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\updated-NES-google.py -> mlmodels-0.12.1\\mlmodels\\model_tf\\rl \nWriting mlmodels-0.12.1\\setup.cfg \nCreating tar archive \nremoving 'mlmodels-0.12.1' (and everything under it) \nrunning bdist_wheel \nrunning build \nrunning build_py \ncopying mlmodels\\models.py -> build\\lib\\mlmodels \ncopying mlmodels\\optim.py -> build\\lib\\mlmodels \ncopying mlmodels\\util.py -> build\\lib\\mlmodels \ncreating build\\lib\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\LambdaRank.py -> build\\lib\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\load_mslr.py -> build\\lib\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\metrics.py -> build\\lib\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\RankNet.py -> build\\lib\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\utils.py -> build\\lib\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\__init__.py -> build\\lib\\mlmodels\\model_rank \ncreating build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\10_encoder_vanilla.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\11_bidirectional_vanilla.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\12_vanilla_2path.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\13_lstm_seq2seq.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\14_lstm_attention.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\19_lstm_dilated.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\1_lstm.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\20_only_attention.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\21_multihead_attention.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\22_lstm_bahdanau.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\23_lstm_luong.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\25_dnc.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\26_lstm_residual.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\27_byte_net.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\28_attention_is_all_you_need.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\29_fairseq.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\2_encoder_lstm.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\3_bidirectional_lstm.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\4_lstm_2path.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\50lstm attention.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\5_gru.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\6_encoder_gru.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\7_bidirectional_gru.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\8_gru_2path.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\9_vanilla.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\access.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\addressing.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\autoencoder.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\dnc.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\util.py -> build\\lib\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\__init__.py -> build\\lib\\mlmodels\\model_tf \ncreating build\\lib\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\settings.py -> build\\lib\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\__init__.py -> build\\lib\\mlmodels\\model_tf\\deepar \ncreating build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\0_template_rl.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\1.turtle-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\updated-NES-google.py -> build\\lib\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\__init__.py -> build\\lib\\mlmodels\\model_tf\\rl \ncreating build\\lib\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> build\\lib\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> build\\lib\\mlmodels\\model_tf\\deepar\\dataset \ncreating build\\lib\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\layers.py -> build\\lib\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\loss.py -> build\\lib\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\lstm.py -> build\\lib\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\__init__.py -> build\\lib\\mlmodels\\model_tf\\deepar\\model \ncreating build\\lib\\mlmodels\\model_tf\\deepar\\utils \ncopying mlmodels\\model_tf\\deepar\\utils\\__init__.py -> build\\lib\\mlmodels\\model_tf\\deepar\\utils \ninstalling to build\\bdist.win-amd64\\wheel \nrunning install \nrunning install_lib \ncreating build\\bdist.win-amd64\\wheel \ncreating build\\bdist.win-amd64\\wheel\\mlmodels \ncopying build\\lib\\mlmodels\\cli_mlmodels -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\config.toml -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\data.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\eur-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year_small.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\oil.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\TSLA.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\usd-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\distri_run.sh -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\distri_tch.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-project -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-workspace -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels_cli.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\LambdaRank.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\load_mslr.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\metrics.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\RankNet.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\utils.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\10_encoder_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\11_bidirectional_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf \ncopying build\\lib\\mlmodels\\model_tf\\12_vanilla_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\13_lstm_seq2seq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\14_lstm_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf \ncopying build\\lib\\mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf \ncopying build\\lib\\mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf \ncopying build\\lib\\mlmodels\\model_tf\\19_lstm_dilated.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\1_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\20_only_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\21_multihead_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\22_lstm_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\23_lstm_luong.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\25_dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\26_lstm_residual.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\27_byte_net.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\28_attention_is_all_you_need.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf \ncopying build\\lib\\mlmodels\\model_tf\\29_fairseq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\2_encoder_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\3_bidirectional_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\4_lstm_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\50lstm attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\5_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\6_encoder_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\7_bidirectional_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\8_gru_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\9_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\access.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\addressing.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\autoencoder.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_t\nf\\deepar\\dataset \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\layers.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\de\nepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\loss.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\settings.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\utils\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncopying build\\lib\\mlmodels\\model_tf\\dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\0_template_rl.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\1.turtle-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\whe\nel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodel\ns\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\\nmodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\n\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\m\nlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\updated-NES-google.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\optim.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\optim_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\ztest.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \nrunning install_egg_info \nCopying mlmodels.egg-info to build\\bdist.win-amd64\\wheel\\.\\mlmodels-0.12.1-py3.6.egg-info \nrunning install_scripts \nadding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\") \ncreating build\\bdist.win-amd64\\wheel\\mlmodels-0.12.1.dist-info\\WHEEL \ncreating 'dist\\mlmodels-0.12.1-py3-none-any.whl' and adding 'build\\bdist.win-amd64\\wheel' to it \nadding 'mlmodels/__init__.py' \nadding 'mlmodels/cli_mlmodels' \nadding 'mlmodels/config.toml' \nadding 'mlmodels/data.py' \nadding 'mlmodels/distri_run.sh' \nadding 'mlmodels/distri_tch.py' \nadding 'mlmodels/mlmodels.sublime-project' \nadding 'mlmodels/mlmodels.sublime-workspace' \nadding 'mlmodels/mlmodels_cli.py' \nadding 'mlmodels/models.py' \nadding 'mlmodels/models_config.json' \nadding 'mlmodels/optim.py' \nadding 'mlmodels/optim_config.json' \nadding 'mlmodels/util.py' \nadding 'mlmodels/ztest.py' \nadding 'mlmodels/dataset/GOOG-year.csv' \nadding 'mlmodels/dataset/GOOG-year_small.csv' \nadding 'mlmodels/dataset/GOOG.csv' \nadding 'mlmodels/dataset/TSLA.csv' \nadding 'mlmodels/dataset/eur-myr.csv' \nadding 'mlmodels/dataset/oil.csv' \nadding 'mlmodels/dataset/usd-myr.csv' \nadding 'mlmodels/model_rank/LambdaRank.py' \nadding 'mlmodels/model_rank/RankNet.py' \nadding 'mlmodels/model_rank/__init__.py' \nadding 'mlmodels/model_rank/load_mslr.py' \nadding 'mlmodels/model_rank/metrics.py' \nadding 'mlmodels/model_rank/utils.py' \nadding 'mlmodels/model_tf/10_encoder_vanilla.py' \nadding 'mlmodels/model_tf/11_bidirectional_vanilla.py' \nadding 'mlmodels/model_tf/12_vanilla_2path.py' \nadding 'mlmodels/model_tf/13_lstm_seq2seq.py' \nadding 'mlmodels/model_tf/14_lstm_attention.py' \nadding 'mlmodels/model_tf/15_lstm_seq2seq_attention.py' \nadding 'mlmodels/model_tf/16_lstm_seq2seq_bidirectional.py' \nadding 'mlmodels/model_tf/17_lstm_seq2seq_bidirectional_attention.py' \nadding 'mlmodels/model_tf/18_lstm_attention_scaleddot.py' \nadding 'mlmodels/model_tf/19_lstm_dilated.py' \nadding 'mlmodels/model_tf/1_lstm.py' \nadding 'mlmodels/model_tf/20_only_attention.py' \nadding 'mlmodels/model_tf/21_multihead_attention.py' \nadding 'mlmodels/model_tf/22_lstm_bahdanau.py' \nadding 'mlmodels/model_tf/23_lstm_luong.py' \nadding 'mlmodels/model_tf/24_lstm_luong_bahdanau.py' \nadding 'mlmodels/model_tf/25_dnc.py' \nadding 'mlmodels/model_tf/26_lstm_residual.py' \nadding 'mlmodels/model_tf/27_byte_net.py' \nadding 'mlmodels/model_tf/28_attention_is_all_you_need.py' \nadding 'mlmodels/model_tf/29_fairseq.py' \nadding 'mlmodels/model_tf/2_encoder_lstm.py' \nadding 'mlmodels/model_tf/3_bidirectional_lstm.py' \nadding 'mlmodels/model_tf/4_lstm_2path.py' \nadding 'mlmodels/model_tf/50lstm attention.py' \nadding 'mlmodels/model_tf/5_gru.py' \nadding 'mlmodels/model_tf/6_encoder_gru.py' \nadding 'mlmodels/model_tf/7_bidirectional_gru.py' \nadding 'mlmodels/model_tf/8_gru_2path.py' \nadding 'mlmodels/model_tf/9_vanilla.py' \nadding 'mlmodels/model_tf/__init__.py' \nadding 'mlmodels/model_tf/access.py' \nadding 'mlmodels/model_tf/addressing.py' \nadding 'mlmodels/model_tf/autoencoder.py' \nadding 'mlmodels/model_tf/dnc.py' \nadding 'mlmodels/model_tf/util.py' \nadding 'mlmodels/model_tf/deepar/__init__.py' \nadding 'mlmodels/model_tf/deepar/settings.py' \nadding 'mlmodels/model_tf/deepar/dataset/__init__.py' \nadding 'mlmodels/model_tf/deepar/dataset/time_series.py' \nadding 'mlmodels/model_tf/deepar/model/__init__.py' \nadding 'mlmodels/model_tf/deepar/model/layers.py' \nadding 'mlmodels/model_tf/deepar/model/loss.py' \nadding 'mlmodels/model_tf/deepar/model/lstm.py' \nadding 'mlmodels/model_tf/deepar/utils/__init__.py' \nadding 'mlmodels/model_tf/rl/0_template_rl.py' \nadding 'mlmodels/model_tf/rl/1.turtle-agent.py' \nadding 'mlmodels/model_tf/rl/10.duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/11.double-duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/12.duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/13.double-duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/14.actor-critic-agent.py' \nadding 'mlmodels/model_tf/rl/15.actor-critic-duel-agent.py' \nadding 'mlmodels/model_tf/rl/16.actor-critic-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/17.actor-critic-duel-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/18.curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/19.recurrent-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/2.moving-average-agent.py' \nadding 'mlmodels/model_tf/rl/20.duel-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/21.neuro-evolution-agent.py' \nadding 'mlmodels/model_tf/rl/22.neuro-evolution-novelty-search-agent.py' \nadding 'mlmodels/model_tf/rl/3.signal-rolling-agent.py' \nadding 'mlmodels/model_tf/rl/4.policy-gradient-agent_old.py' \nadding 'mlmodels/model_tf/rl/4_policy-gradient-agent.py' \nadding 'mlmodels/model_tf/rl/5_q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/6_evolution-strategy-agent.py' \nadding 'mlmodels/model_tf/rl/7.double-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/8.recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/9.double-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/__init__.py' \nadding 'mlmodels/model_tf/rl/updated-NES-google.py' \nadding 'mlmodels-0.12.1.dist-info/LICENSE' \nadding 'mlmodels-0.12.1.dist-info/METADATA' \nadding 'mlmodels-0.12.1.dist-info/WHEEL' \nadding 'mlmodels-0.12.1.dist-info/entry_points.txt' \nadding 'mlmodels-0.12.1.dist-info/top_level.txt' \nadding 'mlmodels-0.12.1.dist-info/RECORD' \nremoving build\\bdist.win-amd64\\wheel \nEnter your username: arita37 \nEnter your password: \nUploading distributions to https://upload.pypi.org/legacy/ \nUploading mlmodels-0.10.1-py3-none-any.whl \n100%|█████████████████████████████████████████████████████████████████████| 57.3k/57.3k [00:01<00:00, 45.5kB/s] \nNOTE: Try --verbose to see response content. \nHTTPError: 403 Client Error: Invalid or non-existent authentication information. for url: https://upload.pypi.or\ng/legacy/ \nUpload files \nDeleting build files \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \nUpload success \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>python pypi.py \nProgram Started \nCurrent version: Version('0.12.1') \nNew Version: Version('0.13.1') \n'\"command -v nvidia-smi\"' is not recognized as an internal or external command, \noperable program or batch file. \nrunning sdist \nrunning egg_info \nwriting mlmodels.egg-info\\PKG-INFO \nwriting dependency_links to mlmodels.egg-info\\dependency_links.txt \nwriting entry points to mlmodels.egg-info\\entry_points.txt \nwriting top-level names to mlmodels.egg-info\\top_level.txt \nreading manifest file 'mlmodels.egg-info\\SOURCES.txt' \nreading manifest template 'MANIFEST.in' \nwriting manifest file 'mlmodels.egg-info\\SOURCES.txt' \nrunning check \ncreating mlmodels-0.13.1 \ncreating mlmodels-0.13.1\\mlmodels \ncreating mlmodels-0.13.1\\mlmodels.egg-info \ncreating mlmodels-0.13.1\\mlmodels\\dataset \ncreating mlmodels-0.13.1\\mlmodels\\model_rank \ncreating mlmodels-0.13.1\\mlmodels\\model_tf \ncreating mlmodels-0.13.1\\mlmodels\\model_tf\\deepar \ncreating mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\dataset \ncreating mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\model \ncreating mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\utils \ncreating mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying files to mlmodels-0.13.1... \ncopying MANIFEST.in -> mlmodels-0.13.1 \ncopying README.md -> mlmodels-0.13.1 \ncopying setup.py -> mlmodels-0.13.1 \ncopying mlmodels\\__init__.py -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\cli_mlmodels -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\config.toml -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\data.py -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\distri_run.sh -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\distri_tch.py -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\models.py -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\models_config.json -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\optim.py -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\optim_config.json -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\util.py -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels\\ztest.py -> mlmodels-0.13.1\\mlmodels \ncopying mlmodels.egg-info\\PKG-INFO -> mlmodels-0.13.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\SOURCES.txt -> mlmodels-0.13.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\dependency_links.txt -> mlmodels-0.13.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\entry_points.txt -> mlmodels-0.13.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\top_level.txt -> mlmodels-0.13.1\\mlmodels.egg-info \ncopying mlmodels\\dataset\\GOOG-year.csv -> mlmodels-0.13.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG-year_small.csv -> mlmodels-0.13.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG.csv -> mlmodels-0.13.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\TSLA.csv -> mlmodels-0.13.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\eur-myr.csv -> mlmodels-0.13.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\oil.csv -> mlmodels-0.13.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\usd-myr.csv -> mlmodels-0.13.1\\mlmodels\\dataset \ncopying mlmodels\\model_rank\\LambdaRank.py -> mlmodels-0.13.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\RankNet.py -> mlmodels-0.13.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\__init__.py -> mlmodels-0.13.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\load_mslr.py -> mlmodels-0.13.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\metrics.py -> mlmodels-0.13.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\utils.py -> mlmodels-0.13.1\\mlmodels\\model_rank \ncopying mlmodels\\model_tf\\10_encoder_vanilla.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\11_bidirectional_vanilla.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\12_vanilla_2path.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\13_lstm_seq2seq.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\14_lstm_attention.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\19_lstm_dilated.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\1_lstm.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\20_only_attention.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\21_multihead_attention.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\22_lstm_bahdanau.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\23_lstm_luong.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\25_dnc.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\26_lstm_residual.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\27_byte_net.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\28_attention_is_all_you_need.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\29_fairseq.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\2_encoder_lstm.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\3_bidirectional_lstm.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\4_lstm_2path.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\50lstm attention.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\5_gru.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\6_encoder_gru.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\7_bidirectional_gru.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\8_gru_2path.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\9_vanilla.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\__init__.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\access.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\addressing.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\autoencoder.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\dnc.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\util.py -> mlmodels-0.13.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\deepar\\__init__.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\settings.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\model\\__init__.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\layers.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\loss.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\lstm.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\utils\\__init__.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\deepar\\utils \ncopying mlmodels\\model_tf\\rl\\0_template_rl.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\1.turtle-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\r\nl \ncopying mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\__init__.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\updated-NES-google.py -> mlmodels-0.13.1\\mlmodels\\model_tf\\rl \nWriting mlmodels-0.13.1\\setup.cfg \nCreating tar archive \nremoving 'mlmodels-0.13.1' (and everything under it) \nrunning bdist_wheel \nrunning build \nrunning build_py \ninstalling to build\\bdist.win-amd64\\wheel \nrunning install \nrunning install_lib \ncreating build\\bdist.win-amd64\\wheel \ncreating build\\bdist.win-amd64\\wheel\\mlmodels \ncopying build\\lib\\mlmodels\\cli_mlmodels -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\config.toml -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\data.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\eur-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year_small.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\oil.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\TSLA.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\usd-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\distri_run.sh -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\distri_tch.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-project -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-workspace -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels_cli.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\LambdaRank.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\load_mslr.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\metrics.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\RankNet.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\utils.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\10_encoder_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\11_bidirectional_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf \ncopying build\\lib\\mlmodels\\model_tf\\12_vanilla_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\13_lstm_seq2seq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\14_lstm_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf \ncopying build\\lib\\mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf \ncopying build\\lib\\mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf \ncopying build\\lib\\mlmodels\\model_tf\\19_lstm_dilated.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\1_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\20_only_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\21_multihead_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\22_lstm_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\23_lstm_luong.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\25_dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\26_lstm_residual.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\27_byte_net.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\28_attention_is_all_you_need.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf \ncopying build\\lib\\mlmodels\\model_tf\\29_fairseq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\2_encoder_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\3_bidirectional_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\4_lstm_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\50lstm attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\5_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\6_encoder_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\7_bidirectional_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\8_gru_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\9_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\access.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\addressing.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\autoencoder.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_t\nf\\deepar\\dataset \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\layers.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\de\nepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\loss.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\settings.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\utils\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncopying build\\lib\\mlmodels\\model_tf\\dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\0_template_rl.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\1.turtle-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\whe\nel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodel\ns\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\\nmodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\n\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\m\nlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\updated-NES-google.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\optim.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\optim_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\ztest.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \nrunning install_egg_info \nCopying mlmodels.egg-info to build\\bdist.win-amd64\\wheel\\.\\mlmodels-0.13.1-py3.6.egg-info \nrunning install_scripts \nadding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\") \ncreating build\\bdist.win-amd64\\wheel\\mlmodels-0.13.1.dist-info\\WHEEL \ncreating 'dist\\mlmodels-0.13.1-py3-none-any.whl' and adding 'build\\bdist.win-amd64\\wheel' to it \nadding 'mlmodels/__init__.py' \nadding 'mlmodels/cli_mlmodels' \nadding 'mlmodels/config.toml' \nadding 'mlmodels/data.py' \nadding 'mlmodels/distri_run.sh' \nadding 'mlmodels/distri_tch.py' \nadding 'mlmodels/mlmodels.sublime-project' \nadding 'mlmodels/mlmodels.sublime-workspace' \nadding 'mlmodels/mlmodels_cli.py' \nadding 'mlmodels/models.py' \nadding 'mlmodels/models_config.json' \nadding 'mlmodels/optim.py' \nadding 'mlmodels/optim_config.json' \nadding 'mlmodels/util.py' \nadding 'mlmodels/ztest.py' \nadding 'mlmodels/dataset/GOOG-year.csv' \nadding 'mlmodels/dataset/GOOG-year_small.csv' \nadding 'mlmodels/dataset/GOOG.csv' \nadding 'mlmodels/dataset/TSLA.csv' \nadding 'mlmodels/dataset/eur-myr.csv' \nadding 'mlmodels/dataset/oil.csv' \nadding 'mlmodels/dataset/usd-myr.csv' \nadding 'mlmodels/model_rank/LambdaRank.py' \nadding 'mlmodels/model_rank/RankNet.py' \nadding 'mlmodels/model_rank/__init__.py' \nadding 'mlmodels/model_rank/load_mslr.py' \nadding 'mlmodels/model_rank/metrics.py' \nadding 'mlmodels/model_rank/utils.py' \nadding 'mlmodels/model_tf/10_encoder_vanilla.py' \nadding 'mlmodels/model_tf/11_bidirectional_vanilla.py' \nadding 'mlmodels/model_tf/12_vanilla_2path.py' \nadding 'mlmodels/model_tf/13_lstm_seq2seq.py' \nadding 'mlmodels/model_tf/14_lstm_attention.py' \nadding 'mlmodels/model_tf/15_lstm_seq2seq_attention.py' \nadding 'mlmodels/model_tf/16_lstm_seq2seq_bidirectional.py' \nadding 'mlmodels/model_tf/17_lstm_seq2seq_bidirectional_attention.py' \nadding 'mlmodels/model_tf/18_lstm_attention_scaleddot.py' \nadding 'mlmodels/model_tf/19_lstm_dilated.py' \nadding 'mlmodels/model_tf/1_lstm.py' \nadding 'mlmodels/model_tf/20_only_attention.py' \nadding 'mlmodels/model_tf/21_multihead_attention.py' \nadding 'mlmodels/model_tf/22_lstm_bahdanau.py' \nadding 'mlmodels/model_tf/23_lstm_luong.py' \nadding 'mlmodels/model_tf/24_lstm_luong_bahdanau.py' \nadding 'mlmodels/model_tf/25_dnc.py' \nadding 'mlmodels/model_tf/26_lstm_residual.py' \nadding 'mlmodels/model_tf/27_byte_net.py' \nadding 'mlmodels/model_tf/28_attention_is_all_you_need.py' \nadding 'mlmodels/model_tf/29_fairseq.py' \nadding 'mlmodels/model_tf/2_encoder_lstm.py' \nadding 'mlmodels/model_tf/3_bidirectional_lstm.py' \nadding 'mlmodels/model_tf/4_lstm_2path.py' \nadding 'mlmodels/model_tf/50lstm attention.py' \nadding 'mlmodels/model_tf/5_gru.py' \nadding 'mlmodels/model_tf/6_encoder_gru.py' \nadding 'mlmodels/model_tf/7_bidirectional_gru.py' \nadding 'mlmodels/model_tf/8_gru_2path.py' \nadding 'mlmodels/model_tf/9_vanilla.py' \nadding 'mlmodels/model_tf/__init__.py' \nadding 'mlmodels/model_tf/access.py' \nadding 'mlmodels/model_tf/addressing.py' \nadding 'mlmodels/model_tf/autoencoder.py' \nadding 'mlmodels/model_tf/dnc.py' \nadding 'mlmodels/model_tf/util.py' \nadding 'mlmodels/model_tf/deepar/__init__.py' \nadding 'mlmodels/model_tf/deepar/settings.py' \nadding 'mlmodels/model_tf/deepar/dataset/__init__.py' \nadding 'mlmodels/model_tf/deepar/dataset/time_series.py' \nadding 'mlmodels/model_tf/deepar/model/__init__.py' \nadding 'mlmodels/model_tf/deepar/model/layers.py' \nadding 'mlmodels/model_tf/deepar/model/loss.py' \nadding 'mlmodels/model_tf/deepar/model/lstm.py' \nadding 'mlmodels/model_tf/deepar/utils/__init__.py' \nadding 'mlmodels/model_tf/rl/0_template_rl.py' \nadding 'mlmodels/model_tf/rl/1.turtle-agent.py' \nadding 'mlmodels/model_tf/rl/10.duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/11.double-duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/12.duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/13.double-duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/14.actor-critic-agent.py' \nadding 'mlmodels/model_tf/rl/15.actor-critic-duel-agent.py' \nadding 'mlmodels/model_tf/rl/16.actor-critic-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/17.actor-critic-duel-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/18.curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/19.recurrent-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/2.moving-average-agent.py' \nadding 'mlmodels/model_tf/rl/20.duel-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/21.neuro-evolution-agent.py' \nadding 'mlmodels/model_tf/rl/22.neuro-evolution-novelty-search-agent.py' \nadding 'mlmodels/model_tf/rl/3.signal-rolling-agent.py' \nadding 'mlmodels/model_tf/rl/4.policy-gradient-agent_old.py' \nadding 'mlmodels/model_tf/rl/4_policy-gradient-agent.py' \nadding 'mlmodels/model_tf/rl/5_q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/6_evolution-strategy-agent.py' \nadding 'mlmodels/model_tf/rl/7.double-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/8.recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/9.double-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/__init__.py' \nadding 'mlmodels/model_tf/rl/updated-NES-google.py' \nadding 'mlmodels-0.13.1.dist-info/LICENSE' \nadding 'mlmodels-0.13.1.dist-info/METADATA' \nadding 'mlmodels-0.13.1.dist-info/WHEEL' \nadding 'mlmodels-0.13.1.dist-info/entry_points.txt' \nadding 'mlmodels-0.13.1.dist-info/top_level.txt' \nadding 'mlmodels-0.13.1.dist-info/RECORD' \nremoving build\\bdist.win-amd64\\wheel \nEnter your username: arita37 \nEnter your password: \nUploading distributions to https://upload.pypi.org/legacy/ \nUploading mlmodels-0.10.1-py3-none-any.whl \n100%|█████████████████████████████████████████████████████████████████████| 57.3k/57.3k [00:01<00:00, 46.6kB/s] \nNOTE: Try --verbose to see response content. \nHTTPError: 403 Client Error: Invalid or non-existent authentication information. for url: https://upload.pypi.or\ng/legacy/ \nUpload files \nDeleting build files \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \nUpload success \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>python pypi.py \nProgram Started \nCurrent version: Version('0.13.1') \nNew Version: Version('0.14.1') \n'\"command -v nvidia-smi\"' is not recognized as an internal or external command, \noperable program or batch file. \nrunning sdist \nrunning egg_info \nwriting mlmodels.egg-info\\PKG-INFO \nwriting dependency_links to mlmodels.egg-info\\dependency_links.txt \nwriting entry points to mlmodels.egg-info\\entry_points.txt \nwriting top-level names to mlmodels.egg-info\\top_level.txt \nreading manifest file 'mlmodels.egg-info\\SOURCES.txt' \nreading manifest template 'MANIFEST.in' \nwriting manifest file 'mlmodels.egg-info\\SOURCES.txt' \nrunning check \ncreating mlmodels-0.14.1 \ncreating mlmodels-0.14.1\\mlmodels \ncreating mlmodels-0.14.1\\mlmodels.egg-info \ncreating mlmodels-0.14.1\\mlmodels\\dataset \ncreating mlmodels-0.14.1\\mlmodels\\model_rank \ncreating mlmodels-0.14.1\\mlmodels\\model_tf \ncreating mlmodels-0.14.1\\mlmodels\\model_tf\\deepar \ncreating mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\dataset \ncreating mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\model \ncreating mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\utils \ncreating mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying files to mlmodels-0.14.1... \ncopying MANIFEST.in -> mlmodels-0.14.1 \ncopying README.md -> mlmodels-0.14.1 \ncopying setup.py -> mlmodels-0.14.1 \ncopying mlmodels\\__init__.py -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\cli_mlmodels -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\config.toml -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\data.py -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\distri_run.sh -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\distri_tch.py -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\models.py -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\models_config.json -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\optim.py -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\optim_config.json -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\util.py -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels\\ztest.py -> mlmodels-0.14.1\\mlmodels \ncopying mlmodels.egg-info\\PKG-INFO -> mlmodels-0.14.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\SOURCES.txt -> mlmodels-0.14.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\dependency_links.txt -> mlmodels-0.14.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\entry_points.txt -> mlmodels-0.14.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\top_level.txt -> mlmodels-0.14.1\\mlmodels.egg-info \ncopying mlmodels\\dataset\\GOOG-year.csv -> mlmodels-0.14.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG-year_small.csv -> mlmodels-0.14.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG.csv -> mlmodels-0.14.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\TSLA.csv -> mlmodels-0.14.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\eur-myr.csv -> mlmodels-0.14.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\oil.csv -> mlmodels-0.14.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\usd-myr.csv -> mlmodels-0.14.1\\mlmodels\\dataset \ncopying mlmodels\\model_rank\\LambdaRank.py -> mlmodels-0.14.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\RankNet.py -> mlmodels-0.14.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\__init__.py -> mlmodels-0.14.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\load_mslr.py -> mlmodels-0.14.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\metrics.py -> mlmodels-0.14.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\utils.py -> mlmodels-0.14.1\\mlmodels\\model_rank \ncopying mlmodels\\model_tf\\10_encoder_vanilla.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\11_bidirectional_vanilla.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\12_vanilla_2path.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\13_lstm_seq2seq.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\14_lstm_attention.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\19_lstm_dilated.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\1_lstm.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\20_only_attention.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\21_multihead_attention.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\22_lstm_bahdanau.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\23_lstm_luong.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\25_dnc.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\26_lstm_residual.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\27_byte_net.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\28_attention_is_all_you_need.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\29_fairseq.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\2_encoder_lstm.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\3_bidirectional_lstm.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\4_lstm_2path.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\50lstm attention.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\5_gru.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\6_encoder_gru.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\7_bidirectional_gru.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\8_gru_2path.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\9_vanilla.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\__init__.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\access.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\addressing.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\autoencoder.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\dnc.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\util.py -> mlmodels-0.14.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\deepar\\__init__.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\settings.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\model\\__init__.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\layers.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\loss.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\lstm.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\utils\\__init__.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\deepar\\utils \ncopying mlmodels\\model_tf\\rl\\0_template_rl.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\1.turtle-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\r\nl \ncopying mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\__init__.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\updated-NES-google.py -> mlmodels-0.14.1\\mlmodels\\model_tf\\rl \nWriting mlmodels-0.14.1\\setup.cfg \nCreating tar archive \nremoving 'mlmodels-0.14.1' (and everything under it) \nrunning bdist_wheel \nrunning build \nrunning build_py \ninstalling to build\\bdist.win-amd64\\wheel \nrunning install \nrunning install_lib \ncreating build\\bdist.win-amd64\\wheel \ncreating build\\bdist.win-amd64\\wheel\\mlmodels \ncopying build\\lib\\mlmodels\\cli_mlmodels -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\config.toml -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\data.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\eur-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year_small.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\oil.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\TSLA.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\usd-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\distri_run.sh -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\distri_tch.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-project -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-workspace -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels_cli.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\LambdaRank.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\load_mslr.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\metrics.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\RankNet.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\utils.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\10_encoder_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\11_bidirectional_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf \ncopying build\\lib\\mlmodels\\model_tf\\12_vanilla_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\13_lstm_seq2seq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\14_lstm_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf \ncopying build\\lib\\mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf \ncopying build\\lib\\mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf \ncopying build\\lib\\mlmodels\\model_tf\\19_lstm_dilated.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\1_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\20_only_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\21_multihead_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\22_lstm_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\23_lstm_luong.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\25_dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\26_lstm_residual.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\27_byte_net.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\28_attention_is_all_you_need.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf \ncopying build\\lib\\mlmodels\\model_tf\\29_fairseq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\2_encoder_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\3_bidirectional_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\4_lstm_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\50lstm attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\5_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\6_encoder_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\7_bidirectional_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\8_gru_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\9_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\access.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\addressing.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\autoencoder.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_t\nf\\deepar\\dataset \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\layers.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\de\nepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\loss.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\settings.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\utils\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncopying build\\lib\\mlmodels\\model_tf\\dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\0_template_rl.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\1.turtle-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\whe\nel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodel\ns\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\\nmodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\n\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\m\nlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\updated-NES-google.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\optim.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\optim_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\ztest.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \nrunning install_egg_info \nCopying mlmodels.egg-info to build\\bdist.win-amd64\\wheel\\.\\mlmodels-0.14.1-py3.6.egg-info \nrunning install_scripts \nadding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\") \ncreating build\\bdist.win-amd64\\wheel\\mlmodels-0.14.1.dist-info\\WHEEL \ncreating 'dist\\mlmodels-0.14.1-py3-none-any.whl' and adding 'build\\bdist.win-amd64\\wheel' to it \nadding 'mlmodels/__init__.py' \nadding 'mlmodels/cli_mlmodels' \nadding 'mlmodels/config.toml' \nadding 'mlmodels/data.py' \nadding 'mlmodels/distri_run.sh' \nadding 'mlmodels/distri_tch.py' \nadding 'mlmodels/mlmodels.sublime-project' \nadding 'mlmodels/mlmodels.sublime-workspace' \nadding 'mlmodels/mlmodels_cli.py' \nadding 'mlmodels/models.py' \nadding 'mlmodels/models_config.json' \nadding 'mlmodels/optim.py' \nadding 'mlmodels/optim_config.json' \nadding 'mlmodels/util.py' \nadding 'mlmodels/ztest.py' \nadding 'mlmodels/dataset/GOOG-year.csv' \nadding 'mlmodels/dataset/GOOG-year_small.csv' \nadding 'mlmodels/dataset/GOOG.csv' \nadding 'mlmodels/dataset/TSLA.csv' \nadding 'mlmodels/dataset/eur-myr.csv' \nadding 'mlmodels/dataset/oil.csv' \nadding 'mlmodels/dataset/usd-myr.csv' \nadding 'mlmodels/model_rank/LambdaRank.py' \nadding 'mlmodels/model_rank/RankNet.py' \nadding 'mlmodels/model_rank/__init__.py' \nadding 'mlmodels/model_rank/load_mslr.py' \nadding 'mlmodels/model_rank/metrics.py' \nadding 'mlmodels/model_rank/utils.py' \nadding 'mlmodels/model_tf/10_encoder_vanilla.py' \nadding 'mlmodels/model_tf/11_bidirectional_vanilla.py' \nadding 'mlmodels/model_tf/12_vanilla_2path.py' \nadding 'mlmodels/model_tf/13_lstm_seq2seq.py' \nadding 'mlmodels/model_tf/14_lstm_attention.py' \nadding 'mlmodels/model_tf/15_lstm_seq2seq_attention.py' \nadding 'mlmodels/model_tf/16_lstm_seq2seq_bidirectional.py' \nadding 'mlmodels/model_tf/17_lstm_seq2seq_bidirectional_attention.py' \nadding 'mlmodels/model_tf/18_lstm_attention_scaleddot.py' \nadding 'mlmodels/model_tf/19_lstm_dilated.py' \nadding 'mlmodels/model_tf/1_lstm.py' \nadding 'mlmodels/model_tf/20_only_attention.py' \nadding 'mlmodels/model_tf/21_multihead_attention.py' \nadding 'mlmodels/model_tf/22_lstm_bahdanau.py' \nadding 'mlmodels/model_tf/23_lstm_luong.py' \nadding 'mlmodels/model_tf/24_lstm_luong_bahdanau.py' \nadding 'mlmodels/model_tf/25_dnc.py' \nadding 'mlmodels/model_tf/26_lstm_residual.py' \nadding 'mlmodels/model_tf/27_byte_net.py' \nadding 'mlmodels/model_tf/28_attention_is_all_you_need.py' \nadding 'mlmodels/model_tf/29_fairseq.py' \nadding 'mlmodels/model_tf/2_encoder_lstm.py' \nadding 'mlmodels/model_tf/3_bidirectional_lstm.py' \nadding 'mlmodels/model_tf/4_lstm_2path.py' \nadding 'mlmodels/model_tf/50lstm attention.py' \nadding 'mlmodels/model_tf/5_gru.py' \nadding 'mlmodels/model_tf/6_encoder_gru.py' \nadding 'mlmodels/model_tf/7_bidirectional_gru.py' \nadding 'mlmodels/model_tf/8_gru_2path.py' \nadding 'mlmodels/model_tf/9_vanilla.py' \nadding 'mlmodels/model_tf/__init__.py' \nadding 'mlmodels/model_tf/access.py' \nadding 'mlmodels/model_tf/addressing.py' \nadding 'mlmodels/model_tf/autoencoder.py' \nadding 'mlmodels/model_tf/dnc.py' \nadding 'mlmodels/model_tf/util.py' \nadding 'mlmodels/model_tf/deepar/__init__.py' \nadding 'mlmodels/model_tf/deepar/settings.py' \nadding 'mlmodels/model_tf/deepar/dataset/__init__.py' \nadding 'mlmodels/model_tf/deepar/dataset/time_series.py' \nadding 'mlmodels/model_tf/deepar/model/__init__.py' \nadding 'mlmodels/model_tf/deepar/model/layers.py' \nadding 'mlmodels/model_tf/deepar/model/loss.py' \nadding 'mlmodels/model_tf/deepar/model/lstm.py' \nadding 'mlmodels/model_tf/deepar/utils/__init__.py' \nadding 'mlmodels/model_tf/rl/0_template_rl.py' \nadding 'mlmodels/model_tf/rl/1.turtle-agent.py' \nadding 'mlmodels/model_tf/rl/10.duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/11.double-duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/12.duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/13.double-duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/14.actor-critic-agent.py' \nadding 'mlmodels/model_tf/rl/15.actor-critic-duel-agent.py' \nadding 'mlmodels/model_tf/rl/16.actor-critic-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/17.actor-critic-duel-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/18.curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/19.recurrent-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/2.moving-average-agent.py' \nadding 'mlmodels/model_tf/rl/20.duel-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/21.neuro-evolution-agent.py' \nadding 'mlmodels/model_tf/rl/22.neuro-evolution-novelty-search-agent.py' \nadding 'mlmodels/model_tf/rl/3.signal-rolling-agent.py' \nadding 'mlmodels/model_tf/rl/4.policy-gradient-agent_old.py' \nadding 'mlmodels/model_tf/rl/4_policy-gradient-agent.py' \nadding 'mlmodels/model_tf/rl/5_q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/6_evolution-strategy-agent.py' \nadding 'mlmodels/model_tf/rl/7.double-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/8.recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/9.double-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/__init__.py' \nadding 'mlmodels/model_tf/rl/updated-NES-google.py' \nadding 'mlmodels-0.14.1.dist-info/LICENSE' \nadding 'mlmodels-0.14.1.dist-info/METADATA' \nadding 'mlmodels-0.14.1.dist-info/WHEEL' \nadding 'mlmodels-0.14.1.dist-info/entry_points.txt' \nadding 'mlmodels-0.14.1.dist-info/top_level.txt' \nadding 'mlmodels-0.14.1.dist-info/RECORD' \nremoving build\\bdist.win-amd64\\wheel \nEnter your username: arita37 \nEnter your password: \nUploading distributions to https://upload.pypi.org/legacy/ \nUploading mlmodels-0.10.1-py3-none-any.whl \n100%|█████████████████████████████████████████████████████████████████████| 57.3k/57.3k [00:01<00:00, 45.3kB/s] \nNOTE: Try --verbose to see response content. \nHTTPError: 403 Client Error: Invalid or non-existent authentication information. for url: https://upload.pypi.or\ng/legacy/ \nUpload files \nDeleting build files \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \nUpload success \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>python pypi.py \nProgram Started \nCurrent version: Version('0.14.1') \nNew Version: Version('0.15.1') \n'\"command -v nvidia-smi\"' is not recognized as an internal or external command, \noperable program or batch file. \nrunning sdist \nrunning egg_info \nwriting mlmodels.egg-info\\PKG-INFO \nwriting dependency_links to mlmodels.egg-info\\dependency_links.txt \nwriting entry points to mlmodels.egg-info\\entry_points.txt \nwriting top-level names to mlmodels.egg-info\\top_level.txt \nreading manifest file 'mlmodels.egg-info\\SOURCES.txt' \nreading manifest template 'MANIFEST.in' \nwriting manifest file 'mlmodels.egg-info\\SOURCES.txt' \nrunning check \ncreating mlmodels-0.15.1 \ncreating mlmodels-0.15.1\\mlmodels \ncreating mlmodels-0.15.1\\mlmodels.egg-info \ncreating mlmodels-0.15.1\\mlmodels\\dataset \ncreating mlmodels-0.15.1\\mlmodels\\model_rank \ncreating mlmodels-0.15.1\\mlmodels\\model_tf \ncreating mlmodels-0.15.1\\mlmodels\\model_tf\\deepar \ncreating mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\dataset \ncreating mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\model \ncreating mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\utils \ncreating mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying files to mlmodels-0.15.1... \ncopying MANIFEST.in -> mlmodels-0.15.1 \ncopying README.md -> mlmodels-0.15.1 \ncopying setup.py -> mlmodels-0.15.1 \ncopying mlmodels\\__init__.py -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\cli_mlmodels -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\config.toml -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\data.py -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\distri_run.sh -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\distri_tch.py -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\models.py -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\models_config.json -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\optim.py -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\optim_config.json -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\util.py -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels\\ztest.py -> mlmodels-0.15.1\\mlmodels \ncopying mlmodels.egg-info\\PKG-INFO -> mlmodels-0.15.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\SOURCES.txt -> mlmodels-0.15.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\dependency_links.txt -> mlmodels-0.15.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\entry_points.txt -> mlmodels-0.15.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\top_level.txt -> mlmodels-0.15.1\\mlmodels.egg-info \ncopying mlmodels\\dataset\\GOOG-year.csv -> mlmodels-0.15.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG-year_small.csv -> mlmodels-0.15.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG.csv -> mlmodels-0.15.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\TSLA.csv -> mlmodels-0.15.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\eur-myr.csv -> mlmodels-0.15.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\oil.csv -> mlmodels-0.15.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\usd-myr.csv -> mlmodels-0.15.1\\mlmodels\\dataset \ncopying mlmodels\\model_rank\\LambdaRank.py -> mlmodels-0.15.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\RankNet.py -> mlmodels-0.15.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\__init__.py -> mlmodels-0.15.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\load_mslr.py -> mlmodels-0.15.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\metrics.py -> mlmodels-0.15.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\utils.py -> mlmodels-0.15.1\\mlmodels\\model_rank \ncopying mlmodels\\model_tf\\10_encoder_vanilla.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\11_bidirectional_vanilla.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\12_vanilla_2path.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\13_lstm_seq2seq.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\14_lstm_attention.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\19_lstm_dilated.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\1_lstm.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\20_only_attention.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\21_multihead_attention.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\22_lstm_bahdanau.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\23_lstm_luong.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\25_dnc.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\26_lstm_residual.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\27_byte_net.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\28_attention_is_all_you_need.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\29_fairseq.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\2_encoder_lstm.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\3_bidirectional_lstm.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\4_lstm_2path.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\50lstm attention.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\5_gru.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\6_encoder_gru.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\7_bidirectional_gru.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\8_gru_2path.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\9_vanilla.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\__init__.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\access.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\addressing.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\autoencoder.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\dnc.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\util.py -> mlmodels-0.15.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\deepar\\__init__.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\settings.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\model\\__init__.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\layers.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\loss.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\lstm.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\utils\\__init__.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\deepar\\utils \ncopying mlmodels\\model_tf\\rl\\0_template_rl.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\1.turtle-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\r\nl \ncopying mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\__init__.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\updated-NES-google.py -> mlmodels-0.15.1\\mlmodels\\model_tf\\rl \nWriting mlmodels-0.15.1\\setup.cfg \nCreating tar archive \nremoving 'mlmodels-0.15.1' (and everything under it) \nrunning bdist_wheel \nrunning build \nrunning build_py \ninstalling to build\\bdist.win-amd64\\wheel \nrunning install \nrunning install_lib \ncreating build\\bdist.win-amd64\\wheel \ncreating build\\bdist.win-amd64\\wheel\\mlmodels \ncopying build\\lib\\mlmodels\\cli_mlmodels -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\config.toml -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\data.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\eur-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year_small.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\oil.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\TSLA.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\usd-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\distri_run.sh -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\distri_tch.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-project -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-workspace -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels_cli.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\LambdaRank.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\load_mslr.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\metrics.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\RankNet.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\utils.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\10_encoder_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\11_bidirectional_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf \ncopying build\\lib\\mlmodels\\model_tf\\12_vanilla_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\13_lstm_seq2seq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\14_lstm_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf \ncopying build\\lib\\mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf \ncopying build\\lib\\mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf \ncopying build\\lib\\mlmodels\\model_tf\\19_lstm_dilated.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\1_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\20_only_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\21_multihead_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\22_lstm_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\23_lstm_luong.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\25_dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\26_lstm_residual.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\27_byte_net.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\28_attention_is_all_you_need.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf \ncopying build\\lib\\mlmodels\\model_tf\\29_fairseq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\2_encoder_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\3_bidirectional_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\4_lstm_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\50lstm attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\5_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\6_encoder_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\7_bidirectional_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\8_gru_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\9_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\access.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\addressing.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\autoencoder.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_t\nf\\deepar\\dataset \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\layers.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\de\nepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\loss.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\settings.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\utils\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncopying build\\lib\\mlmodels\\model_tf\\dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\0_template_rl.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\1.turtle-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\whe\nel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodel\ns\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\\nmodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\n\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\m\nlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\updated-NES-google.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\optim.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\optim_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\ztest.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \nrunning install_egg_info \nCopying mlmodels.egg-info to build\\bdist.win-amd64\\wheel\\.\\mlmodels-0.15.1-py3.6.egg-info \nrunning install_scripts \nadding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\") \ncreating build\\bdist.win-amd64\\wheel\\mlmodels-0.15.1.dist-info\\WHEEL \ncreating 'dist\\mlmodels-0.15.1-py3-none-any.whl' and adding 'build\\bdist.win-amd64\\wheel' to it \nadding 'mlmodels/__init__.py' \nadding 'mlmodels/cli_mlmodels' \nadding 'mlmodels/config.toml' \nadding 'mlmodels/data.py' \nadding 'mlmodels/distri_run.sh' \nadding 'mlmodels/distri_tch.py' \nadding 'mlmodels/mlmodels.sublime-project' \nadding 'mlmodels/mlmodels.sublime-workspace' \nadding 'mlmodels/mlmodels_cli.py' \nadding 'mlmodels/models.py' \nadding 'mlmodels/models_config.json' \nadding 'mlmodels/optim.py' \nadding 'mlmodels/optim_config.json' \nadding 'mlmodels/util.py' \nadding 'mlmodels/ztest.py' \nadding 'mlmodels/dataset/GOOG-year.csv' \nadding 'mlmodels/dataset/GOOG-year_small.csv' \nadding 'mlmodels/dataset/GOOG.csv' \nadding 'mlmodels/dataset/TSLA.csv' \nadding 'mlmodels/dataset/eur-myr.csv' \nadding 'mlmodels/dataset/oil.csv' \nadding 'mlmodels/dataset/usd-myr.csv' \nadding 'mlmodels/model_rank/LambdaRank.py' \nadding 'mlmodels/model_rank/RankNet.py' \nadding 'mlmodels/model_rank/__init__.py' \nadding 'mlmodels/model_rank/load_mslr.py' \nadding 'mlmodels/model_rank/metrics.py' \nadding 'mlmodels/model_rank/utils.py' \nadding 'mlmodels/model_tf/10_encoder_vanilla.py' \nadding 'mlmodels/model_tf/11_bidirectional_vanilla.py' \nadding 'mlmodels/model_tf/12_vanilla_2path.py' \nadding 'mlmodels/model_tf/13_lstm_seq2seq.py' \nadding 'mlmodels/model_tf/14_lstm_attention.py' \nadding 'mlmodels/model_tf/15_lstm_seq2seq_attention.py' \nadding 'mlmodels/model_tf/16_lstm_seq2seq_bidirectional.py' \nadding 'mlmodels/model_tf/17_lstm_seq2seq_bidirectional_attention.py' \nadding 'mlmodels/model_tf/18_lstm_attention_scaleddot.py' \nadding 'mlmodels/model_tf/19_lstm_dilated.py' \nadding 'mlmodels/model_tf/1_lstm.py' \nadding 'mlmodels/model_tf/20_only_attention.py' \nadding 'mlmodels/model_tf/21_multihead_attention.py' \nadding 'mlmodels/model_tf/22_lstm_bahdanau.py' \nadding 'mlmodels/model_tf/23_lstm_luong.py' \nadding 'mlmodels/model_tf/24_lstm_luong_bahdanau.py' \nadding 'mlmodels/model_tf/25_dnc.py' \nadding 'mlmodels/model_tf/26_lstm_residual.py' \nadding 'mlmodels/model_tf/27_byte_net.py' \nadding 'mlmodels/model_tf/28_attention_is_all_you_need.py' \nadding 'mlmodels/model_tf/29_fairseq.py' \nadding 'mlmodels/model_tf/2_encoder_lstm.py' \nadding 'mlmodels/model_tf/3_bidirectional_lstm.py' \nadding 'mlmodels/model_tf/4_lstm_2path.py' \nadding 'mlmodels/model_tf/50lstm attention.py' \nadding 'mlmodels/model_tf/5_gru.py' \nadding 'mlmodels/model_tf/6_encoder_gru.py' \nadding 'mlmodels/model_tf/7_bidirectional_gru.py' \nadding 'mlmodels/model_tf/8_gru_2path.py' \nadding 'mlmodels/model_tf/9_vanilla.py' \nadding 'mlmodels/model_tf/__init__.py' \nadding 'mlmodels/model_tf/access.py' \nadding 'mlmodels/model_tf/addressing.py' \nadding 'mlmodels/model_tf/autoencoder.py' \nadding 'mlmodels/model_tf/dnc.py' \nadding 'mlmodels/model_tf/util.py' \nadding 'mlmodels/model_tf/deepar/__init__.py' \nadding 'mlmodels/model_tf/deepar/settings.py' \nadding 'mlmodels/model_tf/deepar/dataset/__init__.py' \nadding 'mlmodels/model_tf/deepar/dataset/time_series.py' \nadding 'mlmodels/model_tf/deepar/model/__init__.py' \nadding 'mlmodels/model_tf/deepar/model/layers.py' \nadding 'mlmodels/model_tf/deepar/model/loss.py' \nadding 'mlmodels/model_tf/deepar/model/lstm.py' \nadding 'mlmodels/model_tf/deepar/utils/__init__.py' \nadding 'mlmodels/model_tf/rl/0_template_rl.py' \nadding 'mlmodels/model_tf/rl/1.turtle-agent.py' \nadding 'mlmodels/model_tf/rl/10.duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/11.double-duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/12.duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/13.double-duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/14.actor-critic-agent.py' \nadding 'mlmodels/model_tf/rl/15.actor-critic-duel-agent.py' \nadding 'mlmodels/model_tf/rl/16.actor-critic-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/17.actor-critic-duel-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/18.curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/19.recurrent-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/2.moving-average-agent.py' \nadding 'mlmodels/model_tf/rl/20.duel-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/21.neuro-evolution-agent.py' \nadding 'mlmodels/model_tf/rl/22.neuro-evolution-novelty-search-agent.py' \nadding 'mlmodels/model_tf/rl/3.signal-rolling-agent.py' \nadding 'mlmodels/model_tf/rl/4.policy-gradient-agent_old.py' \nadding 'mlmodels/model_tf/rl/4_policy-gradient-agent.py' \nadding 'mlmodels/model_tf/rl/5_q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/6_evolution-strategy-agent.py' \nadding 'mlmodels/model_tf/rl/7.double-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/8.recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/9.double-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/__init__.py' \nadding 'mlmodels/model_tf/rl/updated-NES-google.py' \nadding 'mlmodels-0.15.1.dist-info/LICENSE' \nadding 'mlmodels-0.15.1.dist-info/METADATA' \nadding 'mlmodels-0.15.1.dist-info/WHEEL' \nadding 'mlmodels-0.15.1.dist-info/entry_points.txt' \nadding 'mlmodels-0.15.1.dist-info/top_level.txt' \nadding 'mlmodels-0.15.1.dist-info/RECORD' \nremoving build\\bdist.win-amd64\\wheel \nEnter your username: __token__ \nEnter your password: \nUploading distributions to https://upload.pypi.org/legacy/ \nUploading mlmodels-0.10.1-py3-none-any.whl \n100%|█████████████████████████████████████████████████████████████████████| 57.3k/57.3k [00:01<00:00, 46.4kB/s] \nNOTE: Try --verbose to see response content. \nHTTPError: 403 Client Error: Invalid or non-existent authentication information. for url: https://upload.pypi.or\ng/legacy/ \nUpload files \nDeleting build files \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \nUpload success \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>python pypi.py \nProgram Started \nCurrent version: Version('0.15.1') \nNew Version: Version('0.16.1') \n'\"command -v nvidia-smi\"' is not recognized as an internal or external command, \noperable program or batch file. \nrunning sdist \nrunning egg_info \nwriting mlmodels.egg-info\\PKG-INFO \nwriting dependency_links to mlmodels.egg-info\\dependency_links.txt \nwriting entry points to mlmodels.egg-info\\entry_points.txt \nwriting top-level names to mlmodels.egg-info\\top_level.txt \nreading manifest file 'mlmodels.egg-info\\SOURCES.txt' \nreading manifest template 'MANIFEST.in' \nwriting manifest file 'mlmodels.egg-info\\SOURCES.txt' \nrunning check \ncreating mlmodels-0.16.1 \ncreating mlmodels-0.16.1\\mlmodels \ncreating mlmodels-0.16.1\\mlmodels.egg-info \ncreating mlmodels-0.16.1\\mlmodels\\dataset \ncreating mlmodels-0.16.1\\mlmodels\\model_rank \ncreating mlmodels-0.16.1\\mlmodels\\model_tf \ncreating mlmodels-0.16.1\\mlmodels\\model_tf\\deepar \ncreating mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\dataset \ncreating mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\model \ncreating mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\utils \ncreating mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying files to mlmodels-0.16.1... \ncopying MANIFEST.in -> mlmodels-0.16.1 \ncopying README.md -> mlmodels-0.16.1 \ncopying setup.py -> mlmodels-0.16.1 \ncopying mlmodels\\__init__.py -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\cli_mlmodels -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\config.toml -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\data.py -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\distri_run.sh -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\distri_tch.py -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\models.py -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\models_config.json -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\optim.py -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\optim_config.json -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\util.py -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels\\ztest.py -> mlmodels-0.16.1\\mlmodels \ncopying mlmodels.egg-info\\PKG-INFO -> mlmodels-0.16.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\SOURCES.txt -> mlmodels-0.16.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\dependency_links.txt -> mlmodels-0.16.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\entry_points.txt -> mlmodels-0.16.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\top_level.txt -> mlmodels-0.16.1\\mlmodels.egg-info \ncopying mlmodels\\dataset\\GOOG-year.csv -> mlmodels-0.16.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG-year_small.csv -> mlmodels-0.16.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG.csv -> mlmodels-0.16.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\TSLA.csv -> mlmodels-0.16.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\eur-myr.csv -> mlmodels-0.16.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\oil.csv -> mlmodels-0.16.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\usd-myr.csv -> mlmodels-0.16.1\\mlmodels\\dataset \ncopying mlmodels\\model_rank\\LambdaRank.py -> mlmodels-0.16.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\RankNet.py -> mlmodels-0.16.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\__init__.py -> mlmodels-0.16.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\load_mslr.py -> mlmodels-0.16.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\metrics.py -> mlmodels-0.16.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\utils.py -> mlmodels-0.16.1\\mlmodels\\model_rank \ncopying mlmodels\\model_tf\\10_encoder_vanilla.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\11_bidirectional_vanilla.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\12_vanilla_2path.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\13_lstm_seq2seq.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\14_lstm_attention.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\19_lstm_dilated.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\1_lstm.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\20_only_attention.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\21_multihead_attention.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\22_lstm_bahdanau.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\23_lstm_luong.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\25_dnc.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\26_lstm_residual.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\27_byte_net.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\28_attention_is_all_you_need.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\29_fairseq.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\2_encoder_lstm.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\3_bidirectional_lstm.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\4_lstm_2path.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\50lstm attention.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\5_gru.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\6_encoder_gru.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\7_bidirectional_gru.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\8_gru_2path.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\9_vanilla.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\__init__.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\access.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\addressing.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\autoencoder.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\dnc.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\util.py -> mlmodels-0.16.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\deepar\\__init__.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\settings.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\model\\__init__.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\layers.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\loss.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\lstm.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\utils\\__init__.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\deepar\\utils \ncopying mlmodels\\model_tf\\rl\\0_template_rl.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\1.turtle-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\r\nl \ncopying mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\__init__.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\updated-NES-google.py -> mlmodels-0.16.1\\mlmodels\\model_tf\\rl \nWriting mlmodels-0.16.1\\setup.cfg \nCreating tar archive \nremoving 'mlmodels-0.16.1' (and everything under it) \nrunning bdist_wheel \nrunning build \nrunning build_py \ninstalling to build\\bdist.win-amd64\\wheel \nrunning install \nrunning install_lib \ncreating build\\bdist.win-amd64\\wheel \ncreating build\\bdist.win-amd64\\wheel\\mlmodels \ncopying build\\lib\\mlmodels\\cli_mlmodels -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\config.toml -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\data.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\eur-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year_small.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\oil.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\TSLA.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\usd-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\distri_run.sh -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\distri_tch.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-project -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-workspace -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels_cli.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\LambdaRank.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\load_mslr.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\metrics.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\RankNet.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\utils.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\10_encoder_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\11_bidirectional_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf \ncopying build\\lib\\mlmodels\\model_tf\\12_vanilla_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\13_lstm_seq2seq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\14_lstm_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf \ncopying build\\lib\\mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf \ncopying build\\lib\\mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf \ncopying build\\lib\\mlmodels\\model_tf\\19_lstm_dilated.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\1_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\20_only_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\21_multihead_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\22_lstm_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\23_lstm_luong.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\25_dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\26_lstm_residual.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\27_byte_net.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\28_attention_is_all_you_need.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf \ncopying build\\lib\\mlmodels\\model_tf\\29_fairseq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\2_encoder_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\3_bidirectional_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\4_lstm_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\50lstm attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\5_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\6_encoder_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\7_bidirectional_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\8_gru_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\9_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\access.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\addressing.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\autoencoder.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_t\nf\\deepar\\dataset \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\layers.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\de\nepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\loss.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\settings.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\utils\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncopying build\\lib\\mlmodels\\model_tf\\dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\0_template_rl.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\1.turtle-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\whe\nel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodel\ns\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\\nmodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\n\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\m\nlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\updated-NES-google.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\optim.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\optim_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\ztest.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \nrunning install_egg_info \nCopying mlmodels.egg-info to build\\bdist.win-amd64\\wheel\\.\\mlmodels-0.16.1-py3.6.egg-info \nrunning install_scripts \nadding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\") \ncreating build\\bdist.win-amd64\\wheel\\mlmodels-0.16.1.dist-info\\WHEEL \ncreating 'dist\\mlmodels-0.16.1-py3-none-any.whl' and adding 'build\\bdist.win-amd64\\wheel' to it \nadding 'mlmodels/__init__.py' \nadding 'mlmodels/cli_mlmodels' \nadding 'mlmodels/config.toml' \nadding 'mlmodels/data.py' \nadding 'mlmodels/distri_run.sh' \nadding 'mlmodels/distri_tch.py' \nadding 'mlmodels/mlmodels.sublime-project' \nadding 'mlmodels/mlmodels.sublime-workspace' \nadding 'mlmodels/mlmodels_cli.py' \nadding 'mlmodels/models.py' \nadding 'mlmodels/models_config.json' \nadding 'mlmodels/optim.py' \nadding 'mlmodels/optim_config.json' \nadding 'mlmodels/util.py' \nadding 'mlmodels/ztest.py' \nadding 'mlmodels/dataset/GOOG-year.csv' \nadding 'mlmodels/dataset/GOOG-year_small.csv' \nadding 'mlmodels/dataset/GOOG.csv' \nadding 'mlmodels/dataset/TSLA.csv' \nadding 'mlmodels/dataset/eur-myr.csv' \nadding 'mlmodels/dataset/oil.csv' \nadding 'mlmodels/dataset/usd-myr.csv' \nadding 'mlmodels/model_rank/LambdaRank.py' \nadding 'mlmodels/model_rank/RankNet.py' \nadding 'mlmodels/model_rank/__init__.py' \nadding 'mlmodels/model_rank/load_mslr.py' \nadding 'mlmodels/model_rank/metrics.py' \nadding 'mlmodels/model_rank/utils.py' \nadding 'mlmodels/model_tf/10_encoder_vanilla.py' \nadding 'mlmodels/model_tf/11_bidirectional_vanilla.py' \nadding 'mlmodels/model_tf/12_vanilla_2path.py' \nadding 'mlmodels/model_tf/13_lstm_seq2seq.py' \nadding 'mlmodels/model_tf/14_lstm_attention.py' \nadding 'mlmodels/model_tf/15_lstm_seq2seq_attention.py' \nadding 'mlmodels/model_tf/16_lstm_seq2seq_bidirectional.py' \nadding 'mlmodels/model_tf/17_lstm_seq2seq_bidirectional_attention.py' \nadding 'mlmodels/model_tf/18_lstm_attention_scaleddot.py' \nadding 'mlmodels/model_tf/19_lstm_dilated.py' \nadding 'mlmodels/model_tf/1_lstm.py' \nadding 'mlmodels/model_tf/20_only_attention.py' \nadding 'mlmodels/model_tf/21_multihead_attention.py' \nadding 'mlmodels/model_tf/22_lstm_bahdanau.py' \nadding 'mlmodels/model_tf/23_lstm_luong.py' \nadding 'mlmodels/model_tf/24_lstm_luong_bahdanau.py' \nadding 'mlmodels/model_tf/25_dnc.py' \nadding 'mlmodels/model_tf/26_lstm_residual.py' \nadding 'mlmodels/model_tf/27_byte_net.py' \nadding 'mlmodels/model_tf/28_attention_is_all_you_need.py' \nadding 'mlmodels/model_tf/29_fairseq.py' \nadding 'mlmodels/model_tf/2_encoder_lstm.py' \nadding 'mlmodels/model_tf/3_bidirectional_lstm.py' \nadding 'mlmodels/model_tf/4_lstm_2path.py' \nadding 'mlmodels/model_tf/50lstm attention.py' \nadding 'mlmodels/model_tf/5_gru.py' \nadding 'mlmodels/model_tf/6_encoder_gru.py' \nadding 'mlmodels/model_tf/7_bidirectional_gru.py' \nadding 'mlmodels/model_tf/8_gru_2path.py' \nadding 'mlmodels/model_tf/9_vanilla.py' \nadding 'mlmodels/model_tf/__init__.py' \nadding 'mlmodels/model_tf/access.py' \nadding 'mlmodels/model_tf/addressing.py' \nadding 'mlmodels/model_tf/autoencoder.py' \nadding 'mlmodels/model_tf/dnc.py' \nadding 'mlmodels/model_tf/util.py' \nadding 'mlmodels/model_tf/deepar/__init__.py' \nadding 'mlmodels/model_tf/deepar/settings.py' \nadding 'mlmodels/model_tf/deepar/dataset/__init__.py' \nadding 'mlmodels/model_tf/deepar/dataset/time_series.py' \nadding 'mlmodels/model_tf/deepar/model/__init__.py' \nadding 'mlmodels/model_tf/deepar/model/layers.py' \nadding 'mlmodels/model_tf/deepar/model/loss.py' \nadding 'mlmodels/model_tf/deepar/model/lstm.py' \nadding 'mlmodels/model_tf/deepar/utils/__init__.py' \nadding 'mlmodels/model_tf/rl/0_template_rl.py' \nadding 'mlmodels/model_tf/rl/1.turtle-agent.py' \nadding 'mlmodels/model_tf/rl/10.duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/11.double-duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/12.duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/13.double-duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/14.actor-critic-agent.py' \nadding 'mlmodels/model_tf/rl/15.actor-critic-duel-agent.py' \nadding 'mlmodels/model_tf/rl/16.actor-critic-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/17.actor-critic-duel-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/18.curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/19.recurrent-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/2.moving-average-agent.py' \nadding 'mlmodels/model_tf/rl/20.duel-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/21.neuro-evolution-agent.py' \nadding 'mlmodels/model_tf/rl/22.neuro-evolution-novelty-search-agent.py' \nadding 'mlmodels/model_tf/rl/3.signal-rolling-agent.py' \nadding 'mlmodels/model_tf/rl/4.policy-gradient-agent_old.py' \nadding 'mlmodels/model_tf/rl/4_policy-gradient-agent.py' \nadding 'mlmodels/model_tf/rl/5_q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/6_evolution-strategy-agent.py' \nadding 'mlmodels/model_tf/rl/7.double-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/8.recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/9.double-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/__init__.py' \nadding 'mlmodels/model_tf/rl/updated-NES-google.py' \nadding 'mlmodels-0.16.1.dist-info/LICENSE' \nadding 'mlmodels-0.16.1.dist-info/METADATA' \nadding 'mlmodels-0.16.1.dist-info/WHEEL' \nadding 'mlmodels-0.16.1.dist-info/entry_points.txt' \nadding 'mlmodels-0.16.1.dist-info/top_level.txt' \nadding 'mlmodels-0.16.1.dist-info/RECORD' \nremoving build\\bdist.win-amd64\\wheel \nEnter your username: arita37 \nEnter your password: \nUploading distributions to https://upload.pypi.org/legacy/ \nUploading mlmodels-0.10.1-py3-none-any.whl \n100%|█████████████████████████████████████████████████████████████████████| 57.3k/57.3k [00:01<00:00, 43.3kB/s] \nNOTE: Try --verbose to see response content. \nHTTPError: 403 Client Error: Invalid or non-existent authentication information. for url: https://upload.pypi.or\ng/legacy/ \nUpload files \nDeleting build files \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \nUpload success \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>python pypi.py \nProgram Started \nCurrent version: Version('0.16.1') \nNew Version: Version('0.17.1') \n'\"command -v nvidia-smi\"' is not recognized as an internal or external command, \noperable program or batch file. \nrunning sdist \nrunning egg_info \nwriting mlmodels.egg-info\\PKG-INFO \nwriting dependency_links to mlmodels.egg-info\\dependency_links.txt \nwriting entry points to mlmodels.egg-info\\entry_points.txt \nwriting top-level names to mlmodels.egg-info\\top_level.txt \nreading manifest file 'mlmodels.egg-info\\SOURCES.txt' \nreading manifest template 'MANIFEST.in' \nwriting manifest file 'mlmodels.egg-info\\SOURCES.txt' \nrunning check \ncreating mlmodels-0.17.1 \ncreating mlmodels-0.17.1\\mlmodels \ncreating mlmodels-0.17.1\\mlmodels.egg-info \ncreating mlmodels-0.17.1\\mlmodels\\dataset \ncreating mlmodels-0.17.1\\mlmodels\\model_rank \ncreating mlmodels-0.17.1\\mlmodels\\model_tf \ncreating mlmodels-0.17.1\\mlmodels\\model_tf\\deepar \ncreating mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\dataset \ncreating mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\model \ncreating mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\utils \ncreating mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying files to mlmodels-0.17.1... \ncopying MANIFEST.in -> mlmodels-0.17.1 \ncopying README.md -> mlmodels-0.17.1 \ncopying setup.py -> mlmodels-0.17.1 \ncopying mlmodels\\__init__.py -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\cli_mlmodels -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\config.toml -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\data.py -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\distri_run.sh -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\distri_tch.py -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\models.py -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\models_config.json -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\optim.py -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\optim_config.json -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\util.py -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels\\ztest.py -> mlmodels-0.17.1\\mlmodels \ncopying mlmodels.egg-info\\PKG-INFO -> mlmodels-0.17.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\SOURCES.txt -> mlmodels-0.17.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\dependency_links.txt -> mlmodels-0.17.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\entry_points.txt -> mlmodels-0.17.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\top_level.txt -> mlmodels-0.17.1\\mlmodels.egg-info \ncopying mlmodels\\dataset\\GOOG-year.csv -> mlmodels-0.17.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG-year_small.csv -> mlmodels-0.17.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG.csv -> mlmodels-0.17.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\TSLA.csv -> mlmodels-0.17.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\eur-myr.csv -> mlmodels-0.17.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\oil.csv -> mlmodels-0.17.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\usd-myr.csv -> mlmodels-0.17.1\\mlmodels\\dataset \ncopying mlmodels\\model_rank\\LambdaRank.py -> mlmodels-0.17.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\RankNet.py -> mlmodels-0.17.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\__init__.py -> mlmodels-0.17.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\load_mslr.py -> mlmodels-0.17.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\metrics.py -> mlmodels-0.17.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\utils.py -> mlmodels-0.17.1\\mlmodels\\model_rank \ncopying mlmodels\\model_tf\\10_encoder_vanilla.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\11_bidirectional_vanilla.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\12_vanilla_2path.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\13_lstm_seq2seq.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\14_lstm_attention.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\19_lstm_dilated.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\1_lstm.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\20_only_attention.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\21_multihead_attention.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\22_lstm_bahdanau.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\23_lstm_luong.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\25_dnc.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\26_lstm_residual.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\27_byte_net.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\28_attention_is_all_you_need.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\29_fairseq.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\2_encoder_lstm.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\3_bidirectional_lstm.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\4_lstm_2path.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\50lstm attention.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\5_gru.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\6_encoder_gru.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\7_bidirectional_gru.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\8_gru_2path.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\9_vanilla.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\__init__.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\access.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\addressing.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\autoencoder.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\dnc.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\util.py -> mlmodels-0.17.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\deepar\\__init__.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\settings.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\model\\__init__.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\layers.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\loss.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\lstm.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\utils\\__init__.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\deepar\\utils \ncopying mlmodels\\model_tf\\rl\\0_template_rl.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\1.turtle-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\r\nl \ncopying mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\__init__.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\updated-NES-google.py -> mlmodels-0.17.1\\mlmodels\\model_tf\\rl \nWriting mlmodels-0.17.1\\setup.cfg \nCreating tar archive \nremoving 'mlmodels-0.17.1' (and everything under it) \nrunning bdist_wheel \nrunning build \nrunning build_py \ninstalling to build\\bdist.win-amd64\\wheel \nrunning install \nrunning install_lib \ncreating build\\bdist.win-amd64\\wheel \ncreating build\\bdist.win-amd64\\wheel\\mlmodels \ncopying build\\lib\\mlmodels\\cli_mlmodels -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\config.toml -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\data.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\eur-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year_small.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\oil.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\TSLA.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\usd-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\distri_run.sh -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\distri_tch.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-project -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-workspace -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels_cli.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\LambdaRank.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\load_mslr.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\metrics.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\RankNet.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\utils.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\10_encoder_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\11_bidirectional_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf \ncopying build\\lib\\mlmodels\\model_tf\\12_vanilla_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\13_lstm_seq2seq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\14_lstm_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf \ncopying build\\lib\\mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf \ncopying build\\lib\\mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf \ncopying build\\lib\\mlmodels\\model_tf\\19_lstm_dilated.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\1_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\20_only_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\21_multihead_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\22_lstm_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\23_lstm_luong.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\25_dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\26_lstm_residual.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\27_byte_net.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\28_attention_is_all_you_need.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf \ncopying build\\lib\\mlmodels\\model_tf\\29_fairseq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\2_encoder_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\3_bidirectional_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\4_lstm_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\50lstm attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\5_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\6_encoder_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\7_bidirectional_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\8_gru_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\9_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\access.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\addressing.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\autoencoder.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_t\nf\\deepar\\dataset \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\layers.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\de\nepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\loss.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\settings.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\utils\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncopying build\\lib\\mlmodels\\model_tf\\dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\0_template_rl.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\1.turtle-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\whe\nel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodel\ns\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\\nmodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\n\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\m\nlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\updated-NES-google.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\optim.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\optim_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\ztest.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \nrunning install_egg_info \nCopying mlmodels.egg-info to build\\bdist.win-amd64\\wheel\\.\\mlmodels-0.17.1-py3.6.egg-info \nrunning install_scripts \nadding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\") \ncreating build\\bdist.win-amd64\\wheel\\mlmodels-0.17.1.dist-info\\WHEEL \ncreating 'dist\\mlmodels-0.17.1-py3-none-any.whl' and adding 'build\\bdist.win-amd64\\wheel' to it \nadding 'mlmodels/__init__.py' \nadding 'mlmodels/cli_mlmodels' \nadding 'mlmodels/config.toml' \nadding 'mlmodels/data.py' \nadding 'mlmodels/distri_run.sh' \nadding 'mlmodels/distri_tch.py' \nadding 'mlmodels/mlmodels.sublime-project' \nadding 'mlmodels/mlmodels.sublime-workspace' \nadding 'mlmodels/mlmodels_cli.py' \nadding 'mlmodels/models.py' \nadding 'mlmodels/models_config.json' \nadding 'mlmodels/optim.py' \nadding 'mlmodels/optim_config.json' \nadding 'mlmodels/util.py' \nadding 'mlmodels/ztest.py' \nadding 'mlmodels/dataset/GOOG-year.csv' \nadding 'mlmodels/dataset/GOOG-year_small.csv' \nadding 'mlmodels/dataset/GOOG.csv' \nadding 'mlmodels/dataset/TSLA.csv' \nadding 'mlmodels/dataset/eur-myr.csv' \nadding 'mlmodels/dataset/oil.csv' \nadding 'mlmodels/dataset/usd-myr.csv' \nadding 'mlmodels/model_rank/LambdaRank.py' \nadding 'mlmodels/model_rank/RankNet.py' \nadding 'mlmodels/model_rank/__init__.py' \nadding 'mlmodels/model_rank/load_mslr.py' \nadding 'mlmodels/model_rank/metrics.py' \nadding 'mlmodels/model_rank/utils.py' \nadding 'mlmodels/model_tf/10_encoder_vanilla.py' \nadding 'mlmodels/model_tf/11_bidirectional_vanilla.py' \nadding 'mlmodels/model_tf/12_vanilla_2path.py' \nadding 'mlmodels/model_tf/13_lstm_seq2seq.py' \nadding 'mlmodels/model_tf/14_lstm_attention.py' \nadding 'mlmodels/model_tf/15_lstm_seq2seq_attention.py' \nadding 'mlmodels/model_tf/16_lstm_seq2seq_bidirectional.py' \nadding 'mlmodels/model_tf/17_lstm_seq2seq_bidirectional_attention.py' \nadding 'mlmodels/model_tf/18_lstm_attention_scaleddot.py' \nadding 'mlmodels/model_tf/19_lstm_dilated.py' \nadding 'mlmodels/model_tf/1_lstm.py' \nadding 'mlmodels/model_tf/20_only_attention.py' \nadding 'mlmodels/model_tf/21_multihead_attention.py' \nadding 'mlmodels/model_tf/22_lstm_bahdanau.py' \nadding 'mlmodels/model_tf/23_lstm_luong.py' \nadding 'mlmodels/model_tf/24_lstm_luong_bahdanau.py' \nadding 'mlmodels/model_tf/25_dnc.py' \nadding 'mlmodels/model_tf/26_lstm_residual.py' \nadding 'mlmodels/model_tf/27_byte_net.py' \nadding 'mlmodels/model_tf/28_attention_is_all_you_need.py' \nadding 'mlmodels/model_tf/29_fairseq.py' \nadding 'mlmodels/model_tf/2_encoder_lstm.py' \nadding 'mlmodels/model_tf/3_bidirectional_lstm.py' \nadding 'mlmodels/model_tf/4_lstm_2path.py' \nadding 'mlmodels/model_tf/50lstm attention.py' \nadding 'mlmodels/model_tf/5_gru.py' \nadding 'mlmodels/model_tf/6_encoder_gru.py' \nadding 'mlmodels/model_tf/7_bidirectional_gru.py' \nadding 'mlmodels/model_tf/8_gru_2path.py' \nadding 'mlmodels/model_tf/9_vanilla.py' \nadding 'mlmodels/model_tf/__init__.py' \nadding 'mlmodels/model_tf/access.py' \nadding 'mlmodels/model_tf/addressing.py' \nadding 'mlmodels/model_tf/autoencoder.py' \nadding 'mlmodels/model_tf/dnc.py' \nadding 'mlmodels/model_tf/util.py' \nadding 'mlmodels/model_tf/deepar/__init__.py' \nadding 'mlmodels/model_tf/deepar/settings.py' \nadding 'mlmodels/model_tf/deepar/dataset/__init__.py' \nadding 'mlmodels/model_tf/deepar/dataset/time_series.py' \nadding 'mlmodels/model_tf/deepar/model/__init__.py' \nadding 'mlmodels/model_tf/deepar/model/layers.py' \nadding 'mlmodels/model_tf/deepar/model/loss.py' \nadding 'mlmodels/model_tf/deepar/model/lstm.py' \nadding 'mlmodels/model_tf/deepar/utils/__init__.py' \nadding 'mlmodels/model_tf/rl/0_template_rl.py' \nadding 'mlmodels/model_tf/rl/1.turtle-agent.py' \nadding 'mlmodels/model_tf/rl/10.duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/11.double-duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/12.duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/13.double-duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/14.actor-critic-agent.py' \nadding 'mlmodels/model_tf/rl/15.actor-critic-duel-agent.py' \nadding 'mlmodels/model_tf/rl/16.actor-critic-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/17.actor-critic-duel-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/18.curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/19.recurrent-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/2.moving-average-agent.py' \nadding 'mlmodels/model_tf/rl/20.duel-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/21.neuro-evolution-agent.py' \nadding 'mlmodels/model_tf/rl/22.neuro-evolution-novelty-search-agent.py' \nadding 'mlmodels/model_tf/rl/3.signal-rolling-agent.py' \nadding 'mlmodels/model_tf/rl/4.policy-gradient-agent_old.py' \nadding 'mlmodels/model_tf/rl/4_policy-gradient-agent.py' \nadding 'mlmodels/model_tf/rl/5_q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/6_evolution-strategy-agent.py' \nadding 'mlmodels/model_tf/rl/7.double-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/8.recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/9.double-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/__init__.py' \nadding 'mlmodels/model_tf/rl/updated-NES-google.py' \nadding 'mlmodels-0.17.1.dist-info/LICENSE' \nadding 'mlmodels-0.17.1.dist-info/METADATA' \nadding 'mlmodels-0.17.1.dist-info/WHEEL' \nadding 'mlmodels-0.17.1.dist-info/entry_points.txt' \nadding 'mlmodels-0.17.1.dist-info/top_level.txt' \nadding 'mlmodels-0.17.1.dist-info/RECORD' \nremoving build\\bdist.win-amd64\\wheel \nUploading distributions to https://upload.pypi.org/legacy/ \nUploading mlmodels-0.10.1-py3-none-any.whl \n100%|█████████████████████████████████████████████████████████████████████| 57.3k/57.3k [00:01<00:00, 31.2kB/s] \nUploading mlmodels-0.11.1-py3-none-any.whl \n100%|█████████████████████████████████████████████████████████████████████| 57.3k/57.3k [00:00<00:00, 64.8kB/s] \nUploading mlmodels-0.12.1-py3-none-any.whl \n100%|████████████████████████████████████████████████████████████████████████| 236k/236k [00:01<00:00, 149kB/s] \nUploading mlmodels-0.13.1-py3-none-any.whl \n100%|████████████████████████████████████████████████████████████████████████| 236k/236k [00:01<00:00, 161kB/s] \nUploading mlmodels-0.14.1-py3-none-any.whl \n100%|████████████████████████████████████████████████████████████████████████| 236k/236k [00:01<00:00, 168kB/s] \nUploading mlmodels-0.15.1-py3-none-any.whl \n100%|████████████████████████████████████████████████████████████████████████| 236k/236k [00:01<00:00, 162kB/s] \nUploading mlmodels-0.16.1-py3-none-any.whl \n100%|████████████████████████████████████████████████████████████████████████| 236k/236k [00:01<00:00, 141kB/s] \nUploading mlmodels-0.17.1-py3-none-any.whl \n100%|████████████████████████████████████████████████████████████████████████| 236k/236k [00:01<00:00, 141kB/s] \nUploading mlmodels-0.10.1.tar.gz \n100%|█████████████████████████████████████████████████████████████████████| 40.0k/40.0k [00:00<00:00, 83.9kB/s] \nUploading mlmodels-0.11.1.tar.gz \n100%|█████████████████████████████████████████████████████████████████████| 40.0k/40.0k [00:00<00:00, 70.6kB/s] \nUploading mlmodels-0.12.1.tar.gz \n100%|███████████████████████████████████████████████████████████████████████| 105k/105k [00:01<00:00, 83.2kB/s] \nUploading mlmodels-0.13.1.tar.gz \n100%|███████████████████████████████████████████████████████████████████████| 105k/105k [00:01<00:00, 88.1kB/s] \nUploading mlmodels-0.14.1.tar.gz \n100%|███████████████████████████████████████████████████████████████████████| 105k/105k [00:01<00:00, 64.0kB/s] \nUploading mlmodels-0.15.1.tar.gz \n100%|███████████████████████████████████████████████████████████████████████| 105k/105k [00:01<00:00, 83.3kB/s] \nUploading mlmodels-0.16.1.tar.gz \n100%|███████████████████████████████████████████████████████████████████████| 105k/105k [00:01<00:00, 77.3kB/s] \nUploading mlmodels-0.17.1.tar.gz \n100%|███████████████████████████████████████████████████████████████████████| 105k/105k [00:01<00:00, 71.6kB/s] \n \nView at: \nhttps://pypi.org/project/mlmodels/0.14.1/ \nhttps://pypi.org/project/mlmodels/0.16.1/ \nhttps://pypi.org/project/mlmodels/0.10.1/ \nhttps://pypi.org/project/mlmodels/0.13.1/ \nhttps://pypi.org/project/mlmodels/0.15.1/ \nhttps://pypi.org/project/mlmodels/0.12.1/ \nhttps://pypi.org/project/mlmodels/0.11.1/ \nhttps://pypi.org/project/mlmodels/0.17.1/ \nUpload files \nDeleting build files \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \nUpload success \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>python pypi.py \nProgram Started \nCurrent version: Version('0.17.1') \nNew Version: Version('0.18.1') \n'\"command -v nvidia-smi\"' is not recognized as an internal or external command, \noperable program or batch file. \nrunning sdist \nrunning egg_info \nwriting mlmodels.egg-info\\PKG-INFO \nwriting dependency_links to mlmodels.egg-info\\dependency_links.txt \nwriting entry points to mlmodels.egg-info\\entry_points.txt \nwriting top-level names to mlmodels.egg-info\\top_level.txt \nreading manifest file 'mlmodels.egg-info\\SOURCES.txt' \nreading manifest template 'MANIFEST.in' \nwriting manifest file 'mlmodels.egg-info\\SOURCES.txt' \nrunning check \ncreating mlmodels-0.18.1 \ncreating mlmodels-0.18.1\\mlmodels \ncreating mlmodels-0.18.1\\mlmodels.egg-info \ncreating mlmodels-0.18.1\\mlmodels\\dataset \ncreating mlmodels-0.18.1\\mlmodels\\model_rank \ncreating mlmodels-0.18.1\\mlmodels\\model_tf \ncreating mlmodels-0.18.1\\mlmodels\\model_tf\\deepar \ncreating mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\dataset \ncreating mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\model \ncreating mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\utils \ncreating mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying files to mlmodels-0.18.1... \ncopying MANIFEST.in -> mlmodels-0.18.1 \ncopying README.md -> mlmodels-0.18.1 \ncopying setup.py -> mlmodels-0.18.1 \ncopying mlmodels\\__init__.py -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\cli_mlmodels -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\config.toml -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\data.py -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\distri_run.sh -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\distri_tch.py -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\models.py -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\models_config.json -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\optim.py -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\optim_config.json -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\util.py -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels\\ztest.py -> mlmodels-0.18.1\\mlmodels \ncopying mlmodels.egg-info\\PKG-INFO -> mlmodels-0.18.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\SOURCES.txt -> mlmodels-0.18.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\dependency_links.txt -> mlmodels-0.18.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\entry_points.txt -> mlmodels-0.18.1\\mlmodels.egg-info \ncopying mlmodels.egg-info\\top_level.txt -> mlmodels-0.18.1\\mlmodels.egg-info \ncopying mlmodels\\dataset\\GOOG-year.csv -> mlmodels-0.18.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG-year_small.csv -> mlmodels-0.18.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\GOOG.csv -> mlmodels-0.18.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\TSLA.csv -> mlmodels-0.18.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\eur-myr.csv -> mlmodels-0.18.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\oil.csv -> mlmodels-0.18.1\\mlmodels\\dataset \ncopying mlmodels\\dataset\\usd-myr.csv -> mlmodels-0.18.1\\mlmodels\\dataset \ncopying mlmodels\\model_rank\\LambdaRank.py -> mlmodels-0.18.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\RankNet.py -> mlmodels-0.18.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\__init__.py -> mlmodels-0.18.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\load_mslr.py -> mlmodels-0.18.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\metrics.py -> mlmodels-0.18.1\\mlmodels\\model_rank \ncopying mlmodels\\model_rank\\utils.py -> mlmodels-0.18.1\\mlmodels\\model_rank \ncopying mlmodels\\model_tf\\10_encoder_vanilla.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\11_bidirectional_vanilla.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\12_vanilla_2path.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\13_lstm_seq2seq.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\14_lstm_attention.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\19_lstm_dilated.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\1_lstm.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\20_only_attention.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\21_multihead_attention.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\22_lstm_bahdanau.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\23_lstm_luong.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\25_dnc.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\26_lstm_residual.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\27_byte_net.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\28_attention_is_all_you_need.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\29_fairseq.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\2_encoder_lstm.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\3_bidirectional_lstm.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\4_lstm_2path.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\50lstm attention.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\5_gru.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\6_encoder_gru.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\7_bidirectional_gru.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\8_gru_2path.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\9_vanilla.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\__init__.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\access.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\addressing.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\autoencoder.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\dnc.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\util.py -> mlmodels-0.18.1\\mlmodels\\model_tf \ncopying mlmodels\\model_tf\\deepar\\__init__.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\settings.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar \ncopying mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\dataset \ncopying mlmodels\\model_tf\\deepar\\model\\__init__.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\layers.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\loss.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\model\\lstm.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\model \ncopying mlmodels\\model_tf\\deepar\\utils\\__init__.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\deepar\\utils \ncopying mlmodels\\model_tf\\rl\\0_template_rl.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\1.turtle-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\r\nl \ncopying mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\__init__.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \ncopying mlmodels\\model_tf\\rl\\updated-NES-google.py -> mlmodels-0.18.1\\mlmodels\\model_tf\\rl \nWriting mlmodels-0.18.1\\setup.cfg \nCreating tar archive \nremoving 'mlmodels-0.18.1' (and everything under it) \nrunning bdist_wheel \nrunning build \nrunning build_py \ninstalling to build\\bdist.win-amd64\\wheel \nrunning install \nrunning install_lib \ncreating build\\bdist.win-amd64\\wheel \ncreating build\\bdist.win-amd64\\wheel\\mlmodels \ncopying build\\lib\\mlmodels\\cli_mlmodels -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\config.toml -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\data.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\eur-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG-year_small.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\GOOG.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\oil.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\TSLA.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\dataset\\usd-myr.csv -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\dataset \ncopying build\\lib\\mlmodels\\distri_run.sh -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\distri_tch.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-project -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels.sublime-workspace -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\mlmodels_cli.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\models_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\LambdaRank.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\load_mslr.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\metrics.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\RankNet.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\utils.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncopying build\\lib\\mlmodels\\model_rank\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_rank \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\10_encoder_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\11_bidirectional_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf \ncopying build\\lib\\mlmodels\\model_tf\\12_vanilla_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\13_lstm_seq2seq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\14_lstm_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\15_lstm_seq2seq_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf \ncopying build\\lib\\mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf \ncopying build\\lib\\mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\18_lstm_attention_scaleddot.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf \ncopying build\\lib\\mlmodels\\model_tf\\19_lstm_dilated.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\1_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\20_only_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\21_multihead_attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\22_lstm_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\23_lstm_luong.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\24_lstm_luong_bahdanau.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\n \ncopying build\\lib\\mlmodels\\model_tf\\25_dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\26_lstm_residual.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\27_byte_net.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\28_attention_is_all_you_need.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf \ncopying build\\lib\\mlmodels\\model_tf\\29_fairseq.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\2_encoder_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\3_bidirectional_lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\4_lstm_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\50lstm attention.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\5_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\6_encoder_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\7_bidirectional_gru.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\8_gru_2path.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\9_vanilla.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\access.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\addressing.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\autoencoder.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\time_series.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\deepar\\dataset \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\dataset\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_t\nf\\deepar\\dataset \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\layers.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\de\nepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\loss.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\lstm.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deep\nar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\model\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\model \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\settings.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\deepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\utils\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\ndeepar\\utils \ncopying build\\lib\\mlmodels\\model_tf\\deepar\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\deepar\n \ncopying build\\lib\\mlmodels\\model_tf\\dnc.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncreating build\\bdist.win-amd64\\wheel\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\0_template_rl.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\1.turtle-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\10.duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\11.double-duel-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\12.duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\13.double-duel-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\whe\nel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\14.actor-critic-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_\ntf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\15.actor-critic-duel-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\16.actor-critic-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmod\nels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\17.actor-critic-duel-recurrent-agent.py -> build\\bdist.win-amd64\\wheel\\.\\\nmlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\18.curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodel\ns\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\19.recurrent-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\2.moving-average-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\20.duel-curiosity-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\ml\nmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\21.neuro-evolution-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mod\nel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\22.neuro-evolution-novelty-search-agent.py -> build\\bdist.win-amd64\\wheel\n\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\3.signal-rolling-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model\n_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4.policy-gradient-agent_old.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\\nmodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\4_policy-gradient-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mode\nl_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\5_q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\6_evolution-strategy-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\m\nodel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\7.double-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\mo\ndel_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\8.recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\n\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\9.double-recurrent-q-learning-agent.py -> build\\bdist.win-amd64\\wheel\\.\\m\nlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\updated-NES-google.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\\nrl \ncopying build\\lib\\mlmodels\\model_tf\\rl\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf\\rl \ncopying build\\lib\\mlmodels\\model_tf\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\model_tf\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels\\model_tf \ncopying build\\lib\\mlmodels\\optim.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\optim_config.json -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\util.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\ztest.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \ncopying build\\lib\\mlmodels\\__init__.py -> build\\bdist.win-amd64\\wheel\\.\\mlmodels \nrunning install_egg_info \nCopying mlmodels.egg-info to build\\bdist.win-amd64\\wheel\\.\\mlmodels-0.18.1-py3.6.egg-info \nrunning install_scripts \nadding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\") \ncreating build\\bdist.win-amd64\\wheel\\mlmodels-0.18.1.dist-info\\WHEEL \ncreating 'dist\\mlmodels-0.18.1-py3-none-any.whl' and adding 'build\\bdist.win-amd64\\wheel' to it \nadding 'mlmodels/__init__.py' \nadding 'mlmodels/cli_mlmodels' \nadding 'mlmodels/config.toml' \nadding 'mlmodels/data.py' \nadding 'mlmodels/distri_run.sh' \nadding 'mlmodels/distri_tch.py' \nadding 'mlmodels/mlmodels.sublime-project' \nadding 'mlmodels/mlmodels.sublime-workspace' \nadding 'mlmodels/mlmodels_cli.py' \nadding 'mlmodels/models.py' \nadding 'mlmodels/models_config.json' \nadding 'mlmodels/optim.py' \nadding 'mlmodels/optim_config.json' \nadding 'mlmodels/util.py' \nadding 'mlmodels/ztest.py' \nadding 'mlmodels/dataset/GOOG-year.csv' \nadding 'mlmodels/dataset/GOOG-year_small.csv' \nadding 'mlmodels/dataset/GOOG.csv' \nadding 'mlmodels/dataset/TSLA.csv' \nadding 'mlmodels/dataset/eur-myr.csv' \nadding 'mlmodels/dataset/oil.csv' \nadding 'mlmodels/dataset/usd-myr.csv' \nadding 'mlmodels/model_rank/LambdaRank.py' \nadding 'mlmodels/model_rank/RankNet.py' \nadding 'mlmodels/model_rank/__init__.py' \nadding 'mlmodels/model_rank/load_mslr.py' \nadding 'mlmodels/model_rank/metrics.py' \nadding 'mlmodels/model_rank/utils.py' \nadding 'mlmodels/model_tf/10_encoder_vanilla.py' \nadding 'mlmodels/model_tf/11_bidirectional_vanilla.py' \nadding 'mlmodels/model_tf/12_vanilla_2path.py' \nadding 'mlmodels/model_tf/13_lstm_seq2seq.py' \nadding 'mlmodels/model_tf/14_lstm_attention.py' \nadding 'mlmodels/model_tf/15_lstm_seq2seq_attention.py' \nadding 'mlmodels/model_tf/16_lstm_seq2seq_bidirectional.py' \nadding 'mlmodels/model_tf/17_lstm_seq2seq_bidirectional_attention.py' \nadding 'mlmodels/model_tf/18_lstm_attention_scaleddot.py' \nadding 'mlmodels/model_tf/19_lstm_dilated.py' \nadding 'mlmodels/model_tf/1_lstm.py' \nadding 'mlmodels/model_tf/20_only_attention.py' \nadding 'mlmodels/model_tf/21_multihead_attention.py' \nadding 'mlmodels/model_tf/22_lstm_bahdanau.py' \nadding 'mlmodels/model_tf/23_lstm_luong.py' \nadding 'mlmodels/model_tf/24_lstm_luong_bahdanau.py' \nadding 'mlmodels/model_tf/25_dnc.py' \nadding 'mlmodels/model_tf/26_lstm_residual.py' \nadding 'mlmodels/model_tf/27_byte_net.py' \nadding 'mlmodels/model_tf/28_attention_is_all_you_need.py' \nadding 'mlmodels/model_tf/29_fairseq.py' \nadding 'mlmodels/model_tf/2_encoder_lstm.py' \nadding 'mlmodels/model_tf/3_bidirectional_lstm.py' \nadding 'mlmodels/model_tf/4_lstm_2path.py' \nadding 'mlmodels/model_tf/50lstm attention.py' \nadding 'mlmodels/model_tf/5_gru.py' \nadding 'mlmodels/model_tf/6_encoder_gru.py' \nadding 'mlmodels/model_tf/7_bidirectional_gru.py' \nadding 'mlmodels/model_tf/8_gru_2path.py' \nadding 'mlmodels/model_tf/9_vanilla.py' \nadding 'mlmodels/model_tf/__init__.py' \nadding 'mlmodels/model_tf/access.py' \nadding 'mlmodels/model_tf/addressing.py' \nadding 'mlmodels/model_tf/autoencoder.py' \nadding 'mlmodels/model_tf/dnc.py' \nadding 'mlmodels/model_tf/util.py' \nadding 'mlmodels/model_tf/deepar/__init__.py' \nadding 'mlmodels/model_tf/deepar/settings.py' \nadding 'mlmodels/model_tf/deepar/dataset/__init__.py' \nadding 'mlmodels/model_tf/deepar/dataset/time_series.py' \nadding 'mlmodels/model_tf/deepar/model/__init__.py' \nadding 'mlmodels/model_tf/deepar/model/layers.py' \nadding 'mlmodels/model_tf/deepar/model/loss.py' \nadding 'mlmodels/model_tf/deepar/model/lstm.py' \nadding 'mlmodels/model_tf/deepar/utils/__init__.py' \nadding 'mlmodels/model_tf/rl/0_template_rl.py' \nadding 'mlmodels/model_tf/rl/1.turtle-agent.py' \nadding 'mlmodels/model_tf/rl/10.duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/11.double-duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/12.duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/13.double-duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/14.actor-critic-agent.py' \nadding 'mlmodels/model_tf/rl/15.actor-critic-duel-agent.py' \nadding 'mlmodels/model_tf/rl/16.actor-critic-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/17.actor-critic-duel-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/18.curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/19.recurrent-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/2.moving-average-agent.py' \nadding 'mlmodels/model_tf/rl/20.duel-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/21.neuro-evolution-agent.py' \nadding 'mlmodels/model_tf/rl/22.neuro-evolution-novelty-search-agent.py' \nadding 'mlmodels/model_tf/rl/3.signal-rolling-agent.py' \nadding 'mlmodels/model_tf/rl/4.policy-gradient-agent_old.py' \nadding 'mlmodels/model_tf/rl/4_policy-gradient-agent.py' \nadding 'mlmodels/model_tf/rl/5_q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/6_evolution-strategy-agent.py' \nadding 'mlmodels/model_tf/rl/7.double-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/8.recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/9.double-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/__init__.py' \nadding 'mlmodels/model_tf/rl/updated-NES-google.py' \nadding 'mlmodels-0.18.1.dist-info/LICENSE' \nadding 'mlmodels-0.18.1.dist-info/METADATA' \nadding 'mlmodels-0.18.1.dist-info/WHEEL' \nadding 'mlmodels-0.18.1.dist-info/entry_points.txt' \nadding 'mlmodels-0.18.1.dist-info/top_level.txt' \nadding 'mlmodels-0.18.1.dist-info/RECORD' \nremoving build\\bdist.win-amd64\\wheel \nUploading distributions to https://upload.pypi.org/legacy/ \nUploading mlmodels-0.18.1-py3-none-any.whl \n100%|███████████████████████████████████████████████████████████████████████| 237k/237k [00:02<00:00, 89.5kB/s] \nUploading mlmodels-0.18.1.tar.gz \n100%|███████████████████████████████████████████████████████████████████████| 105k/105k [00:01<00:00, 81.0kB/s] \nInvalidDistribution: Unknown distribution format: 'zbackup' \nUpload files \nDeleting build files \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \nUpload success \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \nadding 'mlmodels/model_tf/7_bidirectional_gru.py' \nadding 'mlmodels/model_tf/8_gru_2path.py' \nadding 'mlmodels/model_tf/9_vanilla.py' \nadding 'mlmodels/model_tf/__init__.py' \nadding 'mlmodels/model_tf/access.py' \nadding 'mlmodels/model_tf/addressing.py' \nadding 'mlmodels/model_tf/autoencoder.py' \nadding 'mlmodels/model_tf/dnc.py' \nadding 'mlmodels/model_tf/util.py' \nadding 'mlmodels/model_tf/deepar/__init__.py' \nadding 'mlmodels/model_tf/deepar/settings.py' \nadding 'mlmodels/model_tf/deepar/dataset/__init__.py' \nadding 'mlmodels/model_tf/deepar/dataset/time_series.py' \nadding 'mlmodels/model_tf/deepar/model/__init__.py' \nadding 'mlmodels/model_tf/deepar/model/layers.py' \nadding 'mlmodels/model_tf/deepar/model/loss.py' \nadding 'mlmodels/model_tf/deepar/model/lstm.py' \nadding 'mlmodels/model_tf/deepar/utils/__init__.py' \nadding 'mlmodels/model_tf/rl/0_template_rl.py' \nadding 'mlmodels/model_tf/rl/1.turtle-agent.py' \nadding 'mlmodels/model_tf/rl/10.duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/11.double-duel-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/12.duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/13.double-duel-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/14.actor-critic-agent.py' \nadding 'mlmodels/model_tf/rl/15.actor-critic-duel-agent.py' \nadding 'mlmodels/model_tf/rl/16.actor-critic-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/17.actor-critic-duel-recurrent-agent.py' \nadding 'mlmodels/model_tf/rl/18.curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/19.recurrent-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/2.moving-average-agent.py' \nadding 'mlmodels/model_tf/rl/20.duel-curiosity-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/21.neuro-evolution-agent.py' \nadding 'mlmodels/model_tf/rl/22.neuro-evolution-novelty-search-agent.py' \nadding 'mlmodels/model_tf/rl/3.signal-rolling-agent.py' \nadding 'mlmodels/model_tf/rl/4.policy-gradient-agent_old.py' \nadding 'mlmodels/model_tf/rl/4_policy-gradient-agent.py' \nadding 'mlmodels/model_tf/rl/5_q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/6_evolution-strategy-agent.py' \nadding 'mlmodels/model_tf/rl/7.double-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/8.recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/9.double-recurrent-q-learning-agent.py' \nadding 'mlmodels/model_tf/rl/__init__.py' \nadding 'mlmodels/model_tf/rl/updated-NES-google.py' \nadding 'mlmodels-0.18.1.dist-info/LICENSE' \nadding 'mlmodels-0.18.1.dist-info/METADATA' \nadding 'mlmodels-0.18.1.dist-info/WHEEL' \nadding 'mlmodels-0.18.1.dist-info/entry_points.txt' \nadding 'mlmodels-0.18.1.dist-info/top_level.txt' \nadding 'mlmodels-0.18.1.dist-info/RECORD' \nremoving build\\bdist.win-amd64\\wheel \nUploading distributions to https://upload.pypi.org/legacy/ \nUploading mlmodels-0.18.1-py3-none-any.whl \n100%|███████████████████████████████████████████████████████████████████████| 237k/237k [00:02<00:00 \n, 89.5kB/s] \nUploading mlmodels-0.18.1.tar.gz , 89.5kB/s] \n100%|███████████████████████████████████████████████████████████████████████| 105k/105k [00:01<00:00 \n, 81.0kB/s] , 81.0kB/s] \nInvalidDistribution: Unknown distribution format: 'zbackup' \nUpload files \nDeleting build files \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \n'rm' is not recognized as an internal or external command, \noperable program or batch file. \nUpload success \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels> \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n B/s] \n \n B/s] \nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels> \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels> \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels> \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \nd. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels> \n dels> \n D:\\_devs\\Python0dels> e ztest \n1\\gitdev\\mlmodels\\mlmodels> dels>activat \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmo >cd .. \ndels> \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmoels\\mlmodelsall -e . \n \n v\\mlmod \n els>cd .. \n hon01\\gi \n ls\\mlmodels>cd .. \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>pip install -e . \nObtaining file:///D:/_devs/Python01/gitdev/mlmodels \nInstalling collected packages: mlmodels \n Found existing installation: mlmodels 0.19.1 \n Uninstalling mlmodels-0.19.1: \n Successfully uninstalled mlmodels-0.19.1 \n Running setup.py develop for mlmodels \nSuccessfully installed mlmodels \n \n(ztest) D:\\_devs\\Python01\\gitdev\\mlmodels>",
"settings":
{
"buffer_size": 495436,
"line_ending": "Windows",
"name": " ",
"scratch": true
}
},
{
"contents": "Microsoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels> \nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>ipython\nPython 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] \nType 'copyright', 'credits' or 'license' for more information \nIPython 6.4.0 -- An enhanced Interactive Python. Type '?' for help. \n \nIn [1]: packages = [\"mlmodels\"] + [\"mlmodels.\" + p for p in find_packages(\"mlmodels\")] \n--------------------------------------------------------------------------- \nNameError Traceback (most recent call last) \n<ipython-input-1-8803735cd9d2> in <module>() \n----> 1 packages = [\"mlmodels\"] + [\"mlmodels.\" + p for p in find_packages(\"mlmodels\")] \n \nNameError: name 'find_packages' is not defined \n \nIn [2]: scripts = [ \"mlmodels/models.py\", \n ...: \"mlmodels/optim.py\", \n ...: \"mlmodels/cli_mlmodels\", \n ...: ] \n \nIn [3]: scripts = [ \"mlmodels/models.py\", \n ...: \"mlmodels/optim.py\", \n ...: \"mlmodels/cli_mlmodels\", \n ...: ] \n \nIn [4]: scipts \n--------------------------------------------------------------------------- \nNameError Traceback (most recent call last) \n<ipython-input-4-aa8f22b424b9> in <module>() \n----> 1 scipts \n \nNameError: name 'scipts' is not defined \n \nIn [5]: scripts \nOut[5]: ['mlmodels/models.py', 'mlmodels/optim.py', 'mlmodels/cli_mlmodels'] \n \nIn [6]: \nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>ipython\nPython 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] \nType 'copyright', 'credits' or 'license' for more information \nIPython 6.4.0 -- An enhanced Interactive Python. Type '?' for help. \n\nIn [1]: scripts = [ \"mlmodels/models.py\", \n ...: \"mlmodels/optim.py\", \n ...: \"mlmodels/cli_mlmodels\", \n ...: ] \n \nIn [2]: \nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>activate tcho04\nCould not find conda environment: tcho04 \nYou can list all discoverable environments with `conda info --envs`. \n \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>activate tch04 \n\n(tch04) D:\\_devs\\Python01\\gitdev\\mlmodels>ipython\nPython 3.6.7 (default, Dec 6 2019, 07:03:06) [MSC v.1900 64 bit (AMD64)] \nType 'copyright', 'credits' or 'license' for more information \nIPython 7.10.1 -- An enhanced Interactive Python. Type '?' for help. \n \nIn [1]: import torch as nn\n\nIn [2]: print(nn) \n<module 'torch' from 'D:\\\\_devs\\\\Python01\\\\ana3\\\\envs\\\\tch04\\\\lib\\\\site-packages\\\\torch\\\\__init__.py'> \n \nIn [3]: \nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>\nD:\\_devs\\Python01\\gitdev\\mlmodels>ipython\nPython 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] \nType 'copyright', 'credits' or 'license' for more information \nIPython 6.4.0 -- An enhanced Interactive Python. Type '?' for help. \n \nIn [1]: \nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>\nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>\nMicrosoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels>cd .. \n \nD:\\_devs\\Python01\\gitdev>ml_models \n'ml_models' is not recognized as an internal or external command, \noperable program or batch file. \n \nD:\\_devs\\Python01\\gitdev>activate ztest \n \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models \ntest \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 401, in main \n test(arg.model_uri) # '1_lstm' \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 289, in test \n print(model_uri) \nNameError: name 'model_uri' is not defined \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models \ntest \nmodel_tf/1_lstm.py \nmodel_tf.1_lstm.py \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'\n> \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'\n> \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n \n ('############# Data, Params preparation #################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'}\n \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n \n ('############ Model preparation #########################',) \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'\n> <mlmodels.model_tf.1_lstm.Model object at 0x00000214A3CC3390> \n \n ('############ Model fit ##################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'}\n \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-11 \nOMP: Info #213: KMP_AFFINITY: decoding x2APIC ids. \nOMP: Info #276: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info \nOMP: Info #156: KMP_AFFINITY: 12 available OS procs \nOMP: Info #157: KMP_AFFINITY: Uniform topology \nOMP: Info #191: KMP_AFFINITY: 1 socket x 6 cores/socket x 2 threads/core (6 total cores) \nOMP: Info #215: KMP_AFFINITY: OS proc to physical thread map: \nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 6 maps to socket 0 core 3 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 7 maps to socket 0 core 3 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 8 maps to socket 0 core 4 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 9 maps to socket 0 core 4 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 10 maps to socket 0 core 5 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 11 maps to socket 0 core 5 thread 1 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 10756 thread 0 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 16400 thread 1 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 4916 thread 2 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 17120 thread 3 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14916 thread 4 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 6900 thread 5 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15624 thread 6 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 16824 thread 7 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 5840 thread 8 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15952 thread 9 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15868 thread 10 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 10032 thread 11 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 10292 thread 12 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 9284 thread 14 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 6596 thread 13 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 12636 thread 15 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 16156 thread 16 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14200 thread 17 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 3476 thread 18 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 9304 thread 19 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14324 thread 20 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 10512 thread 21 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 9444 thread 26 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 2096 thread 22 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 4844 thread 23 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 4044 thread 24 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15436 thread 25 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15632 thread 27 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 8472 thread 28 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14120 thread 29 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 17060 thread 30 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15656 thread 31 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 4192 thread 32 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 7828 thread 33 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14840 thread 34 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 5472 thread 35 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15044 thread 37 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14032 thread 36 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 8484 thread 38 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 17388 thread 39 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 17368 thread 40 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 7292 thread 42 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 13708 thread 41 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14144 thread 43 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 16076 thread 44 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14196 thread 45 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 17300 thread 46 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 6876 thread 47 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 2780 thread 48 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 16720 thread 49 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 9884 thread 50 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 9868 thread 51 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 13480 thread 52 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14588 thread 53 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15932 thread 54 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 9576 thread 55 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 11952 thread 56 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 16312 thread 58 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 14988 thread 57 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 17256 thread 59 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 7484 thread 60 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 12572 thread 61 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 17136 thread 62 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 15220 thread 63 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 9896 thread 64 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 10680 thread 65 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 4168 thread 66 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 6088 thread 67 bound to OS proc set 3 \nfit success <tensorflow.python.client.session.InteractiveSession object at 0x00000214AF0D4198> \n \n ('############ Prediction##########################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'}\n \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \n.. ... ... ... ... ... ... \n60 0.560716 0.537236 0.633156 0.609599 0.609599 0.378373 \n61 0.590968 0.536554 0.639892 0.598604 0.598604 0.346890 \n62 0.527891 0.553723 0.648035 0.627071 0.627071 0.227211 \n63 0.626475 0.591245 0.732181 0.656793 0.656793 0.210688 \n64 0.591182 0.542012 0.680708 0.655287 0.655287 0.143610 \n \n[65 rows x 6 columns] \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 17012 thread 68 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 5952 thread 69 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 16288 thread 70 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 13304 tid 1632 thread 71 bound to OS proc set 11 \n[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 \n 0.00000000e+00 0.00000000e+00] \n [ 9.18956846e-02 6.49314895e-02 1.28102735e-01 4.93542105e-02 \n 4.30909619e-02 1.22089922e-01] \n [ 2.83885766e-02 1.14414141e-01 2.14811534e-01 -1.16870329e-02 \n 1.91170782e-01 1.31727517e-01] \n [-1.60251521e-02 5.35511896e-02 7.83516541e-02 8.75138715e-02 \n 3.61605398e-02 2.07486562e-02] \n [ 6.80879056e-02 4.40264866e-02 1.94903478e-01 3.24547365e-02 \n 1.64689362e-01 1.92456409e-01] \n [ 1.57318860e-01 9.08472911e-02 1.97301522e-01 1.36207506e-01 \n 1.55059755e-01 9.83738303e-02] \n [ 2.72976160e-01 8.88378024e-02 4.06281278e-02 -1.67992443e-01 \n 2.56338477e-01 1.04835868e-01] \n [ 5.40122353e-02 -8.63198191e-02 1.95061281e-01 2.82678723e-01 \n 9.67972353e-02 1.18959941e-01] \n [ 2.04677984e-01 1.32550538e-01 2.40028903e-01 1.65384024e-01 \n 1.84690416e-01 2.97691077e-01] \n [ 5.93172550e-01 2.07670331e-01 3.11058551e-01 -7.77758658e-03 \n 1.21837683e-01 3.79539490e-01] \n [ 5.55280000e-02 6.48749098e-02 6.77865371e-03 4.44445685e-02 \n 2.44140923e-02 -5.74124381e-02] \n [ 1.72186628e-01 3.59500706e-01 1.49456993e-01 2.13090360e-01 \n 7.88730532e-02 3.95708948e-01] \n [-1.00983903e-01 3.85630846e-01 1.95322290e-01 4.15603131e-01 \n 2.61302322e-01 1.98510721e-01] \n [ 1.17512725e-01 8.63909423e-02 4.87984419e-01 5.66271126e-01 \n 3.56572956e-01 1.69938847e-01] \n [ 1.16082527e-01 2.16847271e-01 -5.07587753e-03 2.55202323e-01 \n 2.05909550e-01 -9.92505476e-02] \n [ 3.17372501e-01 3.99773210e-01 1.25594601e-01 5.74318022e-02 \n 4.08888221e-01 4.03677046e-01] \n [ 1.34196877e-03 1.77787781e-01 1.58606157e-01 2.50649124e-01 \n 1.52651340e-01 2.80344576e-01] \n [ 8.87214690e-02 1.97556347e-01 1.30489781e-01 4.15056795e-02 \n 1.90525770e-01 1.28020659e-01] \n [ 4.81229201e-02 9.51923802e-02 3.75600278e-01 3.42107117e-01 \n -3.86594906e-02 7.37841949e-02] \n [ 1.00045174e-01 2.08072841e-01 2.65362352e-01 2.08796617e-02 \n -3.21482383e-02 1.47151858e-01] \n [-1.07501894e-02 1.85994670e-01 2.41282824e-02 1.85482055e-01 \n 3.05805683e-01 -7.60266185e-02] \n [ 4.52461362e-01 3.96668494e-01 -7.60898441e-02 1.24750562e-01 \n 1.85744554e-01 1.31605919e-02] \n [ 2.83705711e-01 3.03749382e-01 2.94586241e-01 2.20860273e-01 \n 5.02194405e-01 2.67843038e-01] \n [ 7.34899789e-02 2.36612856e-01 1.63765818e-01 1.98528439e-01 \n 3.32346708e-01 1.25835717e-01] \n [-6.23561069e-02 1.58541530e-01 3.09375614e-01 -5.76648861e-03 \n 1.24299780e-01 2.10219979e-01] \n [ 3.52583289e-01 1.85883701e-01 3.12484980e-01 7.36506060e-02 \n 2.55768418e-01 2.68721253e-01] \n [ 6.11403525e-01 5.38448870e-01 4.15642262e-01 1.04385093e-02 \n 4.04496759e-01 2.69849867e-01] \n [ 3.02640319e-01 4.29132909e-01 2.55086213e-01 3.60691726e-01 \n 3.72468114e-01 3.76933724e-01] \n [ 5.47366142e-02 2.73679286e-01 1.34752423e-01 -5.07316366e-02 \n 3.95384699e-01 1.98305622e-01] \n [ 2.31547162e-01 2.47286752e-01 1.35656685e-01 9.52795520e-02 \n 3.14732939e-01 3.47880810e-01] \n [ 3.44002664e-01 8.80874470e-02 2.49844402e-01 4.00402218e-01 \n 2.91106462e-01 1.61818847e-01] \n [ 5.24271131e-01 7.01974094e-01 1.15845871e+00 2.72459313e-02 \n 6.40163958e-01 2.17935443e-01] \n [ 4.14315581e-01 3.52945387e-01 2.82711357e-01 1.50265545e-01 \n 2.90578276e-01 2.71677107e-01] \n [ 2.54959077e-01 4.79941785e-01 4.46130373e-02 2.96452314e-01 \n 2.53639370e-01 -1.69005930e-01] \n [ 1.73621938e-01 3.40683222e-01 -5.45027666e-03 4.66650367e-01 \n 2.91455269e-01 2.95601189e-01] \n [ 6.48128271e-01 2.24459946e-01 7.57338181e-02 3.13758641e-01 \n 3.07452947e-01 5.06045699e-01] \n [ 1.23354435e-01 3.09666038e-01 2.56239861e-01 4.88357306e-01 \n -2.10898481e-02 4.65676859e-02] \n [ 5.10292232e-01 2.60446370e-01 3.34002435e-01 -5.62023669e-02 \n 3.79921943e-01 4.59463149e-01] \n [ 5.31388283e-01 3.97793621e-01 5.79717517e-01 -3.02301288e-01 \n 4.69982505e-01 7.03176975e-01] \n [ 1.05350471e+00 2.60607451e-01 8.66018474e-01 1.94428027e-01 \n 4.33150858e-01 3.14865649e-01] \n [-1.06312469e-01 1.76196456e-01 -2.21686233e-02 2.62476832e-01 \n 2.72791535e-02 1.69165164e-01] \n [ 6.57383263e-01 5.43777764e-01 2.31655806e-01 2.52117068e-01 \n 6.01260245e-01 3.48707706e-01] \n [ 1.11158811e-01 4.90402244e-03 2.39765182e-01 3.62954944e-01 \n 2.30711490e-01 1.96011782e-01] \n [ 2.17090428e-01 3.05400014e-01 -2.23576978e-01 3.95859778e-03 \n 1.67877197e-01 6.72543868e-02] \n [ 5.54249406e-01 6.87561184e-02 6.79898024e-01 6.96976840e-01 \n 4.97376502e-01 2.31458470e-01] \n [ 4.11228001e-01 6.78932846e-01 3.83948207e-01 3.06064039e-01 \n 3.79981488e-01 1.58028960e-01] \n [ 2.34695956e-01 7.94230223e-01 6.92103267e-01 5.48762619e-01 \n 1.06237543e+00 6.15867615e-01] \n [ 1.66921690e-01 -1.87049434e-01 1.94124579e-01 3.54134858e-01 \n 8.40630755e-02 1.77482963e-01] \n [ 7.09394217e-01 6.15831614e-01 3.12024534e-01 4.40811336e-01 \n 5.01497567e-01 3.53282541e-01] \n [ 4.67392743e-01 3.25406462e-01 5.20125389e-01 3.37774158e-01 \n 1.72487088e-02 1.01697817e-01] \n [ 6.28206551e-01 5.62976182e-01 -1.79456368e-01 6.26744568e-01 \n 6.95033610e-01 3.14551979e-01] \n [ 6.78324521e-01 5.14307246e-02 3.79076570e-01 9.45257783e-01 \n 3.74662578e-01 5.85736156e-01] \n [ 3.44432712e-01 6.81734860e-01 1.46642596e-01 6.03911102e-01 \n 8.10938060e-01 4.65407521e-01] \n [ 8.52028966e-01 5.24716794e-01 6.18805826e-01 -1.20763332e-02 \n 8.41294587e-01 4.85999614e-01] \n [ 2.23904207e-01 2.76972294e-01 1.74871668e-01 2.72270828e-01 \n 2.07694709e-01 6.85953915e-01] \n [ 3.43977749e-01 -8.19509625e-02 3.66782784e-01 4.74960029e-01 \n 5.48595488e-01 7.85506248e-01] \n [ 9.99673963e-01 6.00522459e-01 9.15091634e-01 7.22217023e-01 \n 9.22626495e-01 4.45220411e-01] \n [ 3.88338864e-01 7.40199804e-01 -3.27420607e-02 5.05433738e-01 \n 1.81122661e-01 6.40623450e-01] \n [ 1.18378915e-01 2.11233661e-01 1.50196388e-01 2.00436711e-02 \n -5.42494282e-02 4.15268913e-03] \n [ 8.13611329e-01 9.06240404e-01 -2.08579019e-01 -7.94788525e-02 \n 1.02212369e+00 2.78078049e-01] \n [ 8.94622564e-01 4.56022710e-01 1.67041644e-01 3.11125189e-01 \n 4.42310542e-01 5.53681493e-01] \n [ 2.87581921e-01 6.25126898e-01 1.48979068e+00 4.03537825e-02 \n 1.23627377e+00 9.22114253e-01] \n [ 9.00425673e-01 5.63458860e-01 1.50214702e-01 -1.39689177e-01 \n 6.19095981e-01 8.23799968e-01] \n [ 6.94981337e-01 6.73996389e-01 4.58604246e-01 7.06285909e-02 \n 3.29277188e-01 3.57494444e-01] \n [ 1.79826263e-02 3.92886817e-01 7.11171031e-01 3.21526706e-01 \n -2.13884711e-02 4.65651512e-01]] \n \n(ztest) D:\\_devs\\Python01\\gitdev>ml_optim \n \n ('model details', 'model_tf.1_lstm', {'learning_rate': {'type': 'log_uniform', 'init': 0.01, 'range': [0.001\n, 0.1]}, 'num_layers': {'type': 'int', 'init': 2, 'range': [2, 4]}, 'size': {'type': 'int', 'init': 6, 'range'\n: [6, 6]}, 'output_size': {'type': 'int', 'init': 6, 'range': [6, 6]}, 'size_layer': {'type': 'categorical', '\nvalue': [128, 256]}, 'timestep': {'type': 'categorical', 'value': [5]}, 'epoch': {'type': 'categorical', 'valu\ne': [2]}}) \n \n ('data_path', 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv') \n \n ('path_save', 'D:\\\\_devs\\\\Python01\\\\gitdev/ztest/optuna_1lstm/') \n \n ('model_pars', {'learning_rate': {'type': 'log_uniform', 'init': 0.01, 'range': [0.001, 0.1]}, 'num_layers':\n {'type': 'int', 'init': 2, 'range': [2, 4]}, 'size': {'type': 'int', 'init': 6, 'range': [6, 6]}, 'output_siz\ne': {'type': 'int', 'init': 6, 'range': [6, 6]}, 'size_layer': {'type': 'categorical', 'value': [128, 256]}, '\ntimestep': {'type': 'categorical', 'value': [5]}, 'epoch': {'type': 'categorical', 'value': [2]}}) \n \n (<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.\npy'>,) \n \n ('###### Hyper-optimization through study ####################################',) \ncheck <module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_ls\ntm.py'> \n<mlmodels.model_tf.1_lstm.Model object at 0x00000276C9738518> \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': 'pa\nndas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-11 \nOMP: Info #213: KMP_AFFINITY: decoding x2APIC ids. \nOMP: Info #276: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info \nOMP: Info #156: KMP_AFFINITY: 12 available OS procs \nOMP: Info #157: KMP_AFFINITY: Uniform topology \nOMP: Info #191: KMP_AFFINITY: 1 socket x 6 cores/socket x 2 threads/core (6 total cores) \nOMP: Info #215: KMP_AFFINITY: OS proc to physical thread map: \nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 6 maps to socket 0 core 3 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 7 maps to socket 0 core 3 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 8 maps to socket 0 core 4 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 9 maps to socket 0 core 4 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 10 maps to socket 0 core 5 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 11 maps to socket 0 core 5 thread 1 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 15968 thread 0 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 6240 thread 1 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8568 thread 2 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 5992 thread 4 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8960 thread 3 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16700 thread 5 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16300 thread 6 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 14836 thread 7 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8644 thread 11 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 2628 thread 10 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 13980 thread 9 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 940 thread 8 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 15972 thread 12 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 15764 thread 13 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16420 thread 14 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8564 thread 15 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 12892 thread 17 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 9128 thread 16 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8052 thread 18 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 12816 thread 19 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 6568 thread 20 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16340 thread 21 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 17032 thread 22 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 17252 thread 23 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 284 thread 24 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 3536 thread 25 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 9528 thread 26 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 17324 thread 27 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 17404 thread 28 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 10516 thread 29 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 7240 thread 30 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 15700 thread 31 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 14056 thread 34 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 9696 thread 33 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 9484 thread 32 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 2024 thread 35 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16380 thread 36 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 12824 thread 37 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 9924 thread 38 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 12744 thread 39 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 15612 thread 40 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16352 thread 41 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8732 thread 42 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8500 thread 43 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16584 thread 44 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16204 thread 45 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8200 thread 46 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 14452 thread 47 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 8844 thread 48 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 14400 thread 49 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 15048 thread 50 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 16472 thread 52 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 10280 thread 51 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 1672 thread 53 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 9784 thread 54 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 17336 thread 55 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 5744 thread 56 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 14160 thread 57 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 14368 thread 58 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 10676 thread 59 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 15452 thread 60 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 5328 thread 61 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 9072 thread 63 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 7524 thread 62 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 5868 thread 64 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 7696 thread 65 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 12564 thread 66 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 2884 thread 67 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 5468 thread 68 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 13572 thread 69 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 14500 thread 70 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 16932 tid 6660 thread 71 bound to OS proc set 11 \n[I 2020-01-13 00:06:27,091] Finished trial#0 resulted in value: 0.16062689429292312. Current best value is 0.1\n6062689429292312 with parameters: {'learning_rate': 0.0015464300145886492, 'num_layers': 4, 'size': 6, 'output\n_size': 6, 'size_layer': 128, 'timestep': 5, 'epoch': 2}. \ncheck <module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_ls\ntm.py'> \n<mlmodels.model_tf.1_lstm.Model object at 0x00000276CC3207F0> \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': 'pa\nndas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1735: UserWarning: An \ninteractive session is already active. This can cause out-of-memory errors in some cases. You must explicitly \ncall `InteractiveSession.close()` to release resources held by the other session(s). \n warnings.warn('An interactive session is already active. This can ' \n[I 2020-01-13 00:06:30,556] Finished trial#1 resulted in value: 1.780603064940526. Current best value is 0.160\n62689429292312 with parameters: {'learning_rate': 0.0015464300145886492, 'num_layers': 4, 'size': 6, 'output_s\nize': 6, 'size_layer': 128, 'timestep': 5, 'epoch': 2}. \n \n ################################### ('Optim, finished',) ################################### \n \n ('### Run Model with best ################################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': 'pa\nndas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1735: UserWarning: An \ninteractive session is already active. This can cause out-of-memory errors in some cases. You must explicitly \ncall `InteractiveSession.close()` to release resources held by the other session(s). \n warnings.warn('An interactive session is already active. This can ' \n \n ('#### Saving ###########################################################',) \n \n ('### Save Stats ##########################################################',) \n \n ############################## ('Finished OPTIMIZATION',) ############################## \n{'learning_rate': 0.0015464300145886492, 'num_layers': 4, 'size': 6, 'output_size': 6, 'size_layer': 128, 'tim\nestep': 5, 'epoch': 2, 'best_value': 0.16062689429292312} \n \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nmodel.py \nmodel.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nmodel.py \nmodel.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nmodel.py \nmodel.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nmodel.py \nmodel.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 392, in main \n module_names = get_recursive_files(folder, r\"model*\", \"py\" ) \nTypeError: get_recursive_files() takes 2 positional arguments but 3 were given \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nmodel_tf-1_lstm.json \nmodels.py \nmodels_config.json \nmodel_dns_ori.pkl \nmodel \nmodel.dat \nmodel.py \nmodel.py \nmodel_tf_sequential.py \nmodel_tf.1_lstm.ckpt.data-00000-of-00001 \nmodel_tf.1_lstm.ckpt.index \nmodel_tf.1_lstm.ckpt.meta \nmodel_tf.1_lstm_config.json \nmodel_tf-1_lstm-py.ckpt.data-00000-of-00001 \nmodel_tf-1_lstm-py.ckpt.index \nmodel_tf-1_lstm-py.ckpt.meta \nmodel_tf-1_lstm-py_best-params.json \nmodel_tf-1_lstm-py_study.csv \nmodels.cpython-36.pyc \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 392, in main \n module_names = get_recursive_files(folder, r\"model*\", \"py\" ) \nTypeError: get_recursive_files() takes 2 positional arguments but 3 were given \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nmodel_tf-1_lstm.json \nmodels.py \nmodels_config.json \nmodel_dns_ori.pkl \nmodel \nmodel.dat \nmodel.py \nmodel.py \nmodel_tf_sequential.py \nmodel_tf.1_lstm.ckpt.data-00000-of-00001 \nmodel_tf.1_lstm.ckpt.index \nmodel_tf.1_lstm.ckpt.meta \nmodel_tf.1_lstm_config.json \nmodel_tf-1_lstm-py.ckpt.data-00000-of-00001 \nmodel_tf-1_lstm-py.ckpt.index \nmodel_tf-1_lstm-py.ckpt.meta \nmodel_tf-1_lstm-py_best-params.json \nmodel_tf-1_lstm-py_study.csv \nmodels.cpython-36.pyc \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 489, in load_entry_poin\nt \n return get_distribution(dist).load_entry_point(group, name) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2852, in load_entry_poi\nnt \n return ep.load() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2443, in load \n return self.resolve() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pkg_resources\\__init__.py\", line 2449, in resolve \n module = __import__(self.module_name, fromlist=['__name__'], level=0) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 104, in <module> \n from mlmodels.util import load_config, get_recursive_files, get_recursive_folder \nImportError: cannot import name 'get_recursive_folder' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 392, in main \n module_names = get_recursive_files(folder, r'/*model*/*.py' ) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\", line 91, in get_recursive_files \n files = glob.glob( folderPath + ext, recursive=True) \nNameError: name 'glob' is not defined \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_dev\\ml_mosaic.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_dev\\mytest.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_flow\\mlflow_run.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_keras\\0_modeltest.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_rank\\LambdaRank.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_rank\\load_mslr.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_rank\\metrics.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_rank\\RankNet.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_rank\\utils.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_rank\\__init__.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_sklearn\\model.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tch\\1_lstm.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tch\\cnn_classifier.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tch\\mlp.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\10_encoder_vanilla.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\11_bidirectional_vanilla.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\12_vanilla_2path.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\13_lstm_seq2seq.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\14_lstm_attention.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\15_lstm_seq2seq_attention.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\16_lstm_seq2seq_bidirectional.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\17_lstm_seq2seq_bidirectional_attention.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\18_lstm_attention_scaleddot.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\19_lstm_dilated.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\1_lstm.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\20_only_attention.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\21_multihead_attention.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\22_lstm_bahdanau.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\23_lstm_luong.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\24_lstm_luong_bahdanau.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\25_dnc.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\26_lstm_residual.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\27_byte_net.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\28_attention_is_all_you_need.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\29_fairseq.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\2_encoder_lstm.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\3_bidirectional_lstm.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\4_lstm_2path.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\50lstm attention.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\5_gru.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\6_encoder_gru.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\7_bidirectional_gru.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\8_gru_2path.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\9_vanilla.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\access.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\addressing.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\autoencoder.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\dnc.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\util.py \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\__init__.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nmodel_dev\\ml_mosaic.py \nmodel_dev\\mytest.py \nmodel_flow\\mlflow_run.py \nmodel_keras\\0_modeltest.py \nmodel_rank\\LambdaRank.py \nmodel_rank\\load_mslr.py \nmodel_rank\\metrics.py \nmodel_rank\\RankNet.py \nmodel_rank\\utils.py \nmodel_rank\\__init__.py \nmodel_sklearn\\model.py \nmodel_tch\\1_lstm.py \nmodel_tch\\cnn_classifier.py \nmodel_tch\\mlp.py \nmodel_tf\\10_encoder_vanilla.py \nmodel_tf\\11_bidirectional_vanilla.py \nmodel_tf\\12_vanilla_2path.py \nmodel_tf\\13_lstm_seq2seq.py \nmodel_tf\\14_lstm_attention.py \nmodel_tf\\15_lstm_seq2seq_attention.py \nmodel_tf\\16_lstm_seq2seq_bidirectional.py \nmodel_tf\\17_lstm_seq2seq_bidirectional_attention.py \nmodel_tf\\18_lstm_attention_scaleddot.py \nmodel_tf\\19_lstm_dilated.py \nmodel_tf\\1_lstm.py \nmodel_tf\\20_only_attention.py \nmodel_tf\\21_multihead_attention.py \nmodel_tf\\22_lstm_bahdanau.py \nmodel_tf\\23_lstm_luong.py \nmodel_tf\\24_lstm_luong_bahdanau.py \nmodel_tf\\25_dnc.py \nmodel_tf\\26_lstm_residual.py \nmodel_tf\\27_byte_net.py \nmodel_tf\\28_attention_is_all_you_need.py \nmodel_tf\\29_fairseq.py \nmodel_tf\\2_encoder_lstm.py \nmodel_tf\\3_bidirectional_lstm.py \nmodel_tf\\4_lstm_2path.py \nmodel_tf\\50lstm attention.py \nmodel_tf\\5_gru.py \nmodel_tf\\6_encoder_gru.py \nmodel_tf\\7_bidirectional_gru.py \nmodel_tf\\8_gru_2path.py \nmodel_tf\\9_vanilla.py \nmodel_tf\\access.py \nmodel_tf\\addressing.py \nmodel_tf\\autoencoder.py \nmodel_tf\\dnc.py \nmodel_tf\\util.py \nmodel_tf\\__init__.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\ \nmodel_dev.ml_mosaic.py \nmodel_dev.mytest.py \nmodel_flow.mlflow_run.py \nmodel_keras.0_modeltest.py \nmodel_rank.LambdaRank.py \nmodel_rank.load_mslr.py \nmodel_rank.metrics.py \nmodel_rank.RankNet.py \nmodel_rank.utils.py \nmodel_rank.__init__.py \nmodel_sklearn.model.py \nmodel_tch.1_lstm.py \nmodel_tch.cnn_classifier.py \nmodel_tch.mlp.py \nmodel_tf.10_encoder_vanilla.py \nmodel_tf.11_bidirectional_vanilla.py \nmodel_tf.12_vanilla_2path.py \nmodel_tf.13_lstm_seq2seq.py \nmodel_tf.14_lstm_attention.py \nmodel_tf.15_lstm_seq2seq_attention.py \nmodel_tf.16_lstm_seq2seq_bidirectional.py \nmodel_tf.17_lstm_seq2seq_bidirectional_attention.py \nmodel_tf.18_lstm_attention_scaleddot.py \nmodel_tf.19_lstm_dilated.py \nmodel_tf.1_lstm.py \nmodel_tf.20_only_attention.py \nmodel_tf.21_multihead_attention.py \nmodel_tf.22_lstm_bahdanau.py \nmodel_tf.23_lstm_luong.py \nmodel_tf.24_lstm_luong_bahdanau.py \nmodel_tf.25_dnc.py \nmodel_tf.26_lstm_residual.py \nmodel_tf.27_byte_net.py \nmodel_tf.28_attention_is_all_you_need.py \nmodel_tf.29_fairseq.py \nmodel_tf.2_encoder_lstm.py \nmodel_tf.3_bidirectional_lstm.py \nmodel_tf.4_lstm_2path.py \nmodel_tf.50lstm attention.py \nmodel_tf.5_gru.py \nmodel_tf.6_encoder_gru.py \nmodel_tf.7_bidirectional_gru.py \nmodel_tf.8_gru_2path.py \nmodel_tf.9_vanilla.py \nmodel_tf.access.py \nmodel_tf.addressing.py \nmodel_tf.autoencoder.py \nmodel_tf.dnc.py \nmodel_tf.util.py \nmodel_tf.__init__.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do model_list \nmodel_list \nmodel_dev.ml_mosaic.py \nmodel_dev.mytest.py \nmodel_flow.mlflow_run.py \nmodel_keras.0_modeltest.py \nmodel_rank.LambdaRank.py \nmodel_rank.load_mslr.py \nmodel_rank.metrics.py \nmodel_rank.RankNet.py \nmodel_rank.utils.py \nmodel_rank.__init__.py \nmodel_sklearn.model.py \nmodel_tch.1_lstm.py \nmodel_tch.cnn_classifier.py \nmodel_tch.mlp.py \nmodel_tf.10_encoder_vanilla.py \nmodel_tf.11_bidirectional_vanilla.py \nmodel_tf.12_vanilla_2path.py \nmodel_tf.13_lstm_seq2seq.py \nmodel_tf.14_lstm_attention.py \nmodel_tf.15_lstm_seq2seq_attention.py \nmodel_tf.16_lstm_seq2seq_bidirectional.py \nmodel_tf.17_lstm_seq2seq_bidirectional_attention.py \nmodel_tf.18_lstm_attention_scaleddot.py \nmodel_tf.19_lstm_dilated.py \nmodel_tf.1_lstm.py \nmodel_tf.20_only_attention.py \nmodel_tf.21_multihead_attention.py \nmodel_tf.22_lstm_bahdanau.py \nmodel_tf.23_lstm_luong.py \nmodel_tf.24_lstm_luong_bahdanau.py \nmodel_tf.25_dnc.py \nmodel_tf.26_lstm_residual.py \nmodel_tf.27_byte_net.py \nmodel_tf.28_attention_is_all_you_need.py \nmodel_tf.29_fairseq.py \nmodel_tf.2_encoder_lstm.py \nmodel_tf.3_bidirectional_lstm.py \nmodel_tf.4_lstm_2path.py \nmodel_tf.50lstm attention.py \nmodel_tf.5_gru.py \nmodel_tf.6_encoder_gru.py \nmodel_tf.7_bidirectional_gru.py \nmodel_tf.8_gru_2path.py \nmodel_tf.9_vanilla.py \nmodel_tf.access.py \nmodel_tf.addressing.py \nmodel_tf.autoencoder.py \nmodel_tf.dnc.py \nmodel_tf.util.py \nmodel_tf.__init__.py \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do test \ntest \nmodel_tf/1_lstm.py \nmodel_tf.1_lstm.py \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n \n ('############# Data, Params preparation #################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n \n ('############ Model preparation #########################',) \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> <mlm\nodels.model_tf.1_lstm.Model object at 0x000002253B052400> \n \n ('############ Model fit ##################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-11 \nOMP: Info #213: KMP_AFFINITY: decoding x2APIC ids. \nOMP: Info #276: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info \nOMP: Info #156: KMP_AFFINITY: 12 available OS procs \nOMP: Info #157: KMP_AFFINITY: Uniform topology \nOMP: Info #191: KMP_AFFINITY: 1 socket x 6 cores/socket x 2 threads/core (6 total cores) \nOMP: Info #215: KMP_AFFINITY: OS proc to physical thread map: \nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 6 maps to socket 0 core 3 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 7 maps to socket 0 core 3 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 8 maps to socket 0 core 4 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 9 maps to socket 0 core 4 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 10 maps to socket 0 core 5 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 11 maps to socket 0 core 5 thread 1 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14412 thread 0 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 13992 thread 1 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 4964 thread 2 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 15424 thread 4 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14688 thread 3 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16956 thread 5 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 2400 thread 6 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16576 thread 7 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14940 thread 11 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 4212 thread 10 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 3256 thread 9 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 1384 thread 8 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 1984 thread 12 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 3104 thread 13 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 9160 thread 14 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 7920 thread 15 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 10308 thread 16 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 1388 thread 17 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 6004 thread 18 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 17380 thread 19 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 13900 thread 20 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 6416 thread 21 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 1336 thread 22 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 9524 thread 23 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 7872 thread 24 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16472 thread 25 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14980 thread 26 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 6364 thread 27 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 6088 thread 28 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14588 thread 29 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 1584 thread 33 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 6856 thread 31 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 4940 thread 32 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 13672 thread 30 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 2544 thread 34 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 12820 thread 36 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 7052 thread 37 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 11768 thread 35 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 15964 thread 38 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16496 thread 39 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16548 thread 40 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 108 thread 41 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 13012 thread 43 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14224 thread 42 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16428 thread 44 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14572 thread 45 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 15024 thread 46 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 15768 thread 47 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 9644 thread 48 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16772 thread 52 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 1972 thread 51 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 4004 thread 50 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 8500 thread 49 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 9828 thread 53 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 5952 thread 54 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 1340 thread 55 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 11056 thread 56 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16212 thread 57 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14900 thread 58 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 2152 thread 60 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 8 thread 59 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 3976 thread 61 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 10920 thread 62 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 16272 thread 63 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 13380 thread 64 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14596 thread 65 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 4008 thread 66 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 10668 thread 67 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 13384 thread 68 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 10688 thread 69 bound to OS proc set 7 \nfit success <tensorflow.python.client.session.InteractiveSession object at 0x000002254623E080> \n \n ('############ Prediction##########################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \n.. ... ... ... ... ... ... \n60 0.560716 0.537236 0.633156 0.609599 0.609599 0.378373 \n61 0.590968 0.536554 0.639892 0.598604 0.598604 0.346890 \n62 0.527891 0.553723 0.648035 0.627071 0.627071 0.227211 \n63 0.626475 0.591245 0.732181 0.656793 0.656793 0.210688 \n64 0.591182 0.542012 0.680708 0.655287 0.655287 0.143610 \n \n[65 rows x 6 columns] \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14920 thread 70 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 9752 tid 14052 thread 71 bound to OS proc set 11 \n[[ 0. 0. 0. 0. 0. 0. ] \n [ 0.07609419 0.0277668 0.01875095 0.06571135 0.07344398 0.00618029] \n [ 0.02788818 0.07424014 0.05545834 0.06696049 0.20144309 -0.04397275] \n [ 0.09312646 0.09209256 0.18745147 0.21466599 0.00306948 0.04927436] \n [ 0.21922858 0.27467448 0.13204877 0.08639924 0.0207551 0.24616596] \n [ 0.37148142 0.50884563 0.63155031 0.61709559 0.11210226 0.10687403] \n [ 0.17528012 0.04012122 0.10226582 0.16827406 0.11852056 -0.11779966] \n [ 0.16388142 0.33724022 0.1739845 0.13594006 0.35012314 0.36909416] \n [ 0.30741379 0.24827729 0.42753044 0.60413527 0.24103777 0.00601708] \n [ 0.20490918 0.32268909 0.29599506 0.14725858 0.38532194 0.25473297] \n [ 0.16460408 0.24944802 0.34742215 0.51366144 0.25107691 0.41353744] \n [ 0.38339227 0.14037572 0.22194603 -0.04081386 0.20863055 0.25915945] \n [ 0.48622611 0.44251281 0.16940902 -0.05148462 0.2262684 0.17707686] \n [ 0.175157 0.42813602 0.27195868 0.43299517 0.16383967 0.05784459] \n [ 0.32425922 0.72388887 0.69913036 0.42801356 0.1527192 0.19983937] \n [ 0.27017063 0.23896262 0.64169103 0.35666472 0.54972196 0.27858633] \n [ 0.21639818 0.1818729 0.26894233 0.24542394 0.22505148 0.08348543] \n [ 0.53178525 0.25020012 0.73424363 0.48881596 0.5374372 -0.1650483 ] \n [ 0.07478254 0.49796113 0.59361756 0.55478895 0.37913966 0.29049417] \n [ 0.35467958 0.77133673 0.45354891 0.73243356 0.16867676 0.17000107] \n [ 0.28922856 0.33840781 0.55448157 0.33883786 0.55604678 0.27680412] \n [ 0.53246331 0.02636174 0.36424702 0.22503676 0.14559069 0.05247454] \n [-0.00210504 0.22510055 0.3608253 0.00748229 0.24411809 0.1094015 ] \n [ 0.07882909 0.31909838 0.5301668 0.37484413 0.22179832 -0.04329681] \n [ 0.07267938 0.03494012 0.53743875 0.40526834 0.36925688 -0.13504571] \n [ 0.08673576 0.0985441 0.37663609 0.17029867 0.27395976 -0.03579665] \n [ 0.59933227 0.16683453 0.40286541 0.35310739 0.31729218 -0.32757714] \n [ 0.04880954 0.03938454 0.26637954 0.32460961 0.22965232 0.05164421] \n [ 0.27078518 0.0123229 0.26968715 0.01597399 0.35558864 -0.07962675] \n [ 0.65242124 0.09289958 0.75973725 0.38512596 0.31008032 0.02349692] \n [-0.0903187 0.31169119 0.75758642 0.50482881 0.34827548 0.27906862] \n [ 0.24037378 0.0842157 0.4667021 0.12801619 0.19453339 0.10350154] \n [ 0.37911975 0.54781979 0.32517692 0.24859211 -0.13600957 0.27236891] \n [ 0.49467561 0.05155741 0.91494465 0.61290699 -0.17813441 -0.14506383] \n [ 0.41795355 0.58096838 1.02527905 0.78603411 0.04322055 0.12315919] \n [ 0.02869074 0.40133345 0.1789719 0.29404932 0.47774005 0.35249448] \n [ 0.97506768 0.75104266 1.04190719 0.7900666 -0.02549529 0.08056719] \n [ 0.03110624 0.24454227 0.504269 0.40194726 0.46112934 0.05146847] \n [ 0.01845441 0.15924194 -0.11983187 0.22723225 0.49518278 0.19891734] \n [ 0.46886262 0.63746613 1.51430929 0.23906298 0.35863987 0.37123874] \n [ 0.25346959 0.22553298 0.85446316 0.57140255 0.25872919 -0.10070101] \n [ 0.10573692 0.36486813 0.32144022 0.05941603 0.39566365 0.15463528] \n [ 0.64808989 0.38274506 1.02407682 0.34956902 -0.11042793 0.18261181] \n [-0.22516933 0.36858317 0.84402031 0.52765387 0.90318137 0.60054559] \n [ 0.50398111 0.27113712 0.47017017 0.3984524 0.54839671 0.45358434] \n [ 0.6215651 0.89258605 0.51657093 0.7675339 0.42280036 0.50096226] \n [ 0.20530634 0.28664196 0.29547954 0.40989634 0.37569037 0.17823917] \n [ 0.34322092 -0.17330579 0.43646115 0.10006171 0.07051606 -0.01111402] \n [ 0.17671104 0.13785592 0.30722359 0.5433681 0.0552972 0.11279988] \n [ 0.18517935 -0.02672358 0.44969201 0.62856758 0.45844248 -0.07141417] \n [ 0.31954971 0.52048171 0.9601841 0.45262295 0.14248832 0.13981006] \n [ 0.2261135 0.53281981 1.13170826 0.79259777 0.62068498 0.69578129] \n [ 0.48128235 0.19295566 0.91426885 -0.1301322 0.30006418 0.33883151] \n [ 0.10622855 0.03163254 0.75618291 0.7073794 0.79263705 0.07093314] \n [ 0.21183562 -0.07707995 0.34500754 0.24635838 0.2731446 0.19867915] \n [ 0.1825003 0.33894274 -0.0377685 0.13186826 0.13377468 -0.01595519] \n [ 0.64182281 0.74692965 0.80551374 0.6237734 -0.48096517 0.13966134] \n [ 0.56375277 0.30878153 0.11651273 0.98574024 -0.17093661 -0.36544064] \n [ 0.35855383 0.67963141 0.04578801 0.20146042 0.11320563 0.12718718] \n [ 0.14919873 -0.24690098 0.52514958 0.58063334 0.42855138 0.29540351] \n [ 0.2434421 0.07219809 0.4273915 0.49686426 0.24496323 0.07618075] \n [ 1.12188983 0.685426 1.34584987 1.12103379 0.54448968 0.13480203] \n [ 0.38940632 0.08967817 0.36437637 0.56771374 0.23441106 -0.20360076] \n [ 0.37709844 0.53007936 1.69852018 0.26160055 0.78255582 -0.41915116] \n [ 1.00016391 -0.40767795 0.79705465 0.86604929 0.50490069 -0.17660253]] \n \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 412, in main \n model_p, data_p, compute_p, out_p = config_get_pars(arg.config_file, arg.config_mode) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 307, in config_get_pars \n js = json.load(open(config_file, 'r')) #Config \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\json\\__init__.py\", line 299, in load \n parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\json\\__init__.py\", line 354, in loads \n return _default_decoder.decode(s) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\json\\decoder.py\", line 339, in decode \n obj, end = self.raw_decode(s, idx=_w(s, 0).end()) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\json\\decoder.py\", line 355, in raw_decode \n obj, end = self.scan_once(s, idx) \njson.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 25 column 3 (char 375) \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 414, in main \n module = module_load(arg.modelname) # '1_lstm.py \nAttributeError: 'Namespace' object has no attribute 'modelname' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 414, in main \n module = module_load(arg.modelname) # '1_lstm.py \nAttributeError: 'Namespace' object has no attribute 'modelname' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 415, in main \n model = model_create(model_p) # Exact map JSON and paramters \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 175, in model_create \n model_pars = module.get_pars() \nAttributeError: 'dict' object has no attribute 'get_pars' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n obj, end = self.raw_decode(s, idx=_w(s, 0).end()) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\json\\decoder.py\", line 355, in raw_decode \n obj, end = self.scan_once(s, idx) \njson.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 25 column 3 (char 375) \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 414, in main \n module = module_load(arg.modelname) # '1_lstm.py \nAttributeError: 'Namespace' object has no attribute 'modelname' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 414, in main \n module = module_load(arg.modelname) # '1_lstm.py \nAttributeError: 'Namespace' object has no attribute 'modelname' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 415, in main \n model = model_create(model_p) # Exact map JSON and paramters \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 175, in model_create \n model_pars = module.get_pars() \nAttributeError: 'dict' object has no attribute 'get_pars' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(s, 0).end()) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\json\\decoder.py\", line 355, in raw_decode \n obj, end = self.scan_once(s, idx) \njson.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 25 column 3 (char 375) \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 414, in main \n module = module_load(arg.modelname) # '1_lstm.py \nAttributeError: 'Namespace' object has no attribute 'modelname' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 414, in main \n module = module_load(arg.modelname) # '1_lstm.py \nAttributeError: 'Namespace' object has no attribute 'modelname' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 415, in main \n model = model_create(model_p) # Exact map JSON and paramters \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 175, in model_create \n model_pars = module.get_pars() \nAttributeError: 'dict' object has no attribute 'get_pars' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do fit \nfit \n \n ('Fit',) \n{'data_path': 'dataset/GOOG-year.csv', 'data_type': 'pandas', 'size': [0, 0, 6], 'output_size': [0, 6]} \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 418, in main \n sess = module.fit(model, data_pars=data_p, compute_pars= compute_p) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\1_lstm.py\", line 86, in fit \n df = get_dataset(data_pars) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\model_tf\\1_lstm.py\", line 197, in get_dataset \n df = pd.read_csv(filename) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pandas\\io\\parsers.py\", line 685, in parser_f \n return _read(filepath_or_buffer, kwds) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pandas\\io\\parsers.py\", line 457, in _read \n parser = TextFileReader(fp_or_buf, **kwds) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pandas\\io\\parsers.py\", line 895, in __init__ \n self._make_engine(self.engine) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pandas\\io\\parsers.py\", line 1135, in _make_engine \n self._engine = CParserWrapper(self.f, **self.options) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\pandas\\io\\parsers.py\", line 1917, in __init__ \n self._reader = parsers.TextReader(src, **kwds) \n File \"pandas/_libs/parsers.pyx\", line 382, in pandas._libs.parsers.TextReader.__cinit__ \n File \"pandas/_libs/parsers.pyx\", line 689, in pandas._libs.parsers.TextReader._setup_parser_source \nFileNotFoundError: [Errno 2] File b'dataset/GOOG-year.csv' does not exist: b'dataset/GOOG-year.csv' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do predict \npredict \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 427, in main \n module, model, session = load(arg.load_folder) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 219, in load \n return load_tf(folder_name) \nTypeError: load_tf() missing 1 required positional argument: 'filename' \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do predict \npredict \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 427, in main \n module, model, session = load(arg.load_folder) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 219, in load \n return load_tf(folder_name, filename) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\", line 111, in load_tf \n import mlflow.tensorflow \nModuleNotFoundError: No module named 'mlflow' \n \n(ztest) D:\\_devs\\Python01\\gitdev>pip install mlflow \nCollecting mlflow \n Downloading https://files.pythonhosted.org/packages/61/3a/350e8db39894920b66c702d211ae880daf8c188a3733e32d356ee9e446\n10/mlflow-1.5.0.tar.gz (14.6MB) \n |████████████████████████████████| 14.6MB 1.7MB/s \nRequirement already satisfied: alembic in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (1.3.2) \nCollecting click>=7.0 \n Downloading https://files.pythonhosted.org/packages/fa/37/45185cb5abbc30d7257104c434fe0b07e5a195a6847506c074527aa599\nec/Click-7.0-py2.py3-none-any.whl (81kB) \n |████████████████████████████████| 81kB ... \nRequirement already satisfied: cloudpickle in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (1.2.2\n) \nCollecting databricks-cli>=0.8.7 \n Downloading https://files.pythonhosted.org/packages/85/03/fb0c4d31559d48f15403ad88aefff8ae0dddbbeab273d198c20232a937\nc2/databricks-cli-0.9.1.tar.gz (45kB) \n |████████████████████████████████| 51kB 3.2MB/s \nRequirement already satisfied: requests>=2.17.3 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (\n2.22.0) \nRequirement already satisfied: six>=1.10.0 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (1.13.\n0) \nCollecting Flask \n Downloading https://files.pythonhosted.org/packages/9b/93/628509b8d5dc749656a9641f4caf13540e2cdec85276964ff8f43bbb1d\n3b/Flask-1.1.1-py2.py3-none-any.whl (94kB) \n |████████████████████████████████| 102kB 6.4MB/s \nRequirement already satisfied: numpy in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (1.17.3) \nRequirement already satisfied: pandas in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (0.25.3) \nRequirement already satisfied: python-dateutil in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (2\n.8.1) \nRequirement already satisfied: protobuf>=3.6.0 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (3\n.11.1) \nCollecting gitpython>=2.1.0 \n Downloading https://files.pythonhosted.org/packages/20/8c/4543981439d23c4ff65b2e62dddd767ebc84a8e664a9b67e840d1e2730\nd3/GitPython-3.0.5-py3-none-any.whl (455kB) \n |████████████████████████████████| 460kB 6.4MB/s \nRequirement already satisfied: pyyaml in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (5.2) \nCollecting querystring_parser \n Downloading https://files.pythonhosted.org/packages/4a/fa/f54f5662e0eababf0c49e92fd94bf178888562c0e7b677c8941bbbcd1b\nd6/querystring_parser-1.2.4.tar.gz \nCollecting simplejson \n Downloading https://files.pythonhosted.org/packages/7a/62/6b9dcd8727a3139c5335653a3447e5fea81c225766857f75243e07e3b2\n85/simplejson-3.17.0-cp36-cp36m-win_amd64.whl (73kB) \n |████████████████████████████████| 81kB 5.5MB/s \nCollecting docker>=4.0.0 \n Downloading https://files.pythonhosted.org/packages/cc/ca/699d4754a932787ef353a157ada74efd1ceb6d1fc0bfb7989ae1e7b331\n11/docker-4.1.0-py2.py3-none-any.whl (139kB) \n |████████████████████████████████| 143kB 6.4MB/s \nCollecting entrypoints \n Downloading https://files.pythonhosted.org/packages/ac/c6/44694103f8c221443ee6b0041f69e2740d89a25641e62fb4f2ee568f2f\n9c/entrypoints-0.3-py2.py3-none-any.whl \nCollecting sqlparse \n Downloading https://files.pythonhosted.org/packages/ef/53/900f7d2a54557c6a37886585a91336520e5539e3ae2423ff1102daf4f3\na7/sqlparse-0.3.0-py2.py3-none-any.whl \nRequirement already satisfied: sqlalchemy in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from mlflow) (1.3.12\n) \nCollecting gorilla \n Downloading https://files.pythonhosted.org/packages/e3/56/5a683944cbfc77e429c6f03c636ca50504a785f60ffae91ddd7f5f7bb5\n20/gorilla-0.3.0-py2.py3-none-any.whl \nCollecting prometheus-flask-exporter \n Downloading https://files.pythonhosted.org/packages/77/70/8f2ad254ef0595d72dd65150156a095a296af69990417f86af9e5cb6e8\nf7/prometheus_flask_exporter-0.12.1.tar.gz \nCollecting waitress \n Downloading https://files.pythonhosted.org/packages/6b/0d/589f9e46222fd5b300dc4550427452ccd5fbe9720dad4148f227ba8e3b\nf7/waitress-1.4.2-py2.py3-none-any.whl (148kB) \n |████████████████████████████████| 153kB 6.4MB/s \nRequirement already satisfied: Mako in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from alembic->mlflow) (1.1\n.0) \nRequirement already satisfied: python-editor>=0.3 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from alembic\n->mlflow) (1.0.4) \nProcessing c:\\users\\dellbook\\appdata\\local\\pip\\cache\\wheels\\9c\\9b\\f4\\eb243fdb89676ec00588e8c54bb54360724c06e7fafe95278\ne\\tabulate-0.8.6-cp36-none-any.whl \nCollecting configparser>=0.3.5 \n Downloading https://files.pythonhosted.org/packages/7a/2a/95ed0501cf5d8709490b1d3a3f9b5cf340da6c433f896bbe9ce08dbe67\n85/configparser-4.0.2-py2.py3-none-any.whl \nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from requ\nests>=2.17.3->mlflow) (3.0.4) \nRequirement already satisfied: certifi>=2017.4.17 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from request\ns>=2.17.3->mlflow) (2019.11.28) \nRequirement already satisfied: idna<2.9,>=2.5 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from requests>=2\n.17.3->mlflow) (2.8) \nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-p\nackages (from requests>=2.17.3->mlflow) (1.25.7) \nRequirement already satisfied: Werkzeug>=0.15 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from Flask->mlfl\now) (0.16.0) \nCollecting Jinja2>=2.10.1 \n Downloading https://files.pythonhosted.org/packages/65/e0/eb35e762802015cab1ccee04e8a277b03f1d8e53da3ec3106882ec4255\n8b/Jinja2-2.10.3-py2.py3-none-any.whl (125kB) \n |████████████████████████████████| 133kB 6.4MB/s \nCollecting itsdangerous>=0.24 \n Downloading https://files.pythonhosted.org/packages/76/ae/44b03b253d6fade317f32c24d100b3b35c2239807046a4c953c7b89fa4\n9e/itsdangerous-1.1.0-py2.py3-none-any.whl \nRequirement already satisfied: pytz>=2017.2 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from pandas->mlflo\nw) (2019.3) \nRequirement already satisfied: setuptools in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from protobuf>=3.6.0\n->mlflow) (42.0.2.post20191201) \nCollecting gitdb2>=2.0.0 \n Downloading https://files.pythonhosted.org/packages/03/6c/99296f89bad2ef85626e1df9f677acbee8885bb043ad82ad3ed4746d23\n25/gitdb2-2.0.6-py2.py3-none-any.whl (63kB) \n |████████████████████████████████| 71kB 4.8MB/s \nRequirement already satisfied: websocket-client>=0.32.0 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from d\nocker>=4.0.0->mlflow) (0.56.0) \nCollecting pypiwin32==223; sys_platform == \"win32\" and python_version >= \"3.6\" \n Downloading https://files.pythonhosted.org/packages/d0/1b/2f292bbd742e369a100c91faa0483172cd91a1a422a6692055ac920946\nc5/pypiwin32-223-py3-none-any.whl \nCollecting prometheus_client \n Downloading https://files.pythonhosted.org/packages/b3/23/41a5a24b502d35a4ad50a5bb7202a5e1d9a0364d0c12f56db3dbf7aca7\n6d/prometheus_client-0.7.1.tar.gz \nRequirement already satisfied: MarkupSafe>=0.9.2 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from Mako->al\nembic->mlflow) (1.1.1) \nCollecting smmap2>=2.0.0 \n Downloading https://files.pythonhosted.org/packages/55/d2/866d45e3a121ee15a1dc013824d58072fd5c7799c9c34d01378eb262ca\n8f/smmap2-2.0.5-py2.py3-none-any.whl \nRequirement already satisfied: pywin32>=223 in d:\\_devs\\python01\\ana3\\envs\\ztest\\lib\\site-packages (from pypiwin32==22\n3; sys_platform == \"win32\" and python_version >= \"3.6\"->docker>=4.0.0->mlflow) (225) \nBuilding wheels for collected packages: mlflow, databricks-cli, querystring-parser, prometheus-flask-exporter, prometh\neus-client \n Building wheel for mlflow (setup.py) ... done \n Created wheel for mlflow: filename=mlflow-1.5.0-cp36-none-any.whl size=14783650 sha256=63e98b7361a9d01abe8c901f9561c\n1e1aaa15a80e69568005a21ad4cfe868f25 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\f2\\cc\\f4\\50cf516fb828a49be6929942715516f91061c\nda44fcf4ea6bd \n Building wheel for databricks-cli (setup.py) ... done \n Created wheel for databricks-cli: filename=databricks_cli-0.9.1-cp36-none-any.whl size=83759 sha256=a8d0e755e522b388\n0a33382383ce11ff647f4880bb67e64d69a9db1c50d2ee5b \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\bc\\27\\58\\c6ef96e649962e9584a50f58d5b6abafb71a0\n3512b2e381ad1 \n Building wheel for querystring-parser (setup.py) ... done \n Created wheel for querystring-parser: filename=querystring_parser-1.2.4-cp36-none-any.whl size=7085 sha256=cf8abcac3\nbfd98ba980d60dafdc04bff37a14cba4ceefd43aae1af692fa759c8 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\1e\\41\\34\\23ebf5d1089a9aed847951e0ee375426eb4ad\n0a7079d88d41e \n Building wheel for prometheus-flask-exporter (setup.py) ... done \n Created wheel for prometheus-flask-exporter: filename=prometheus_flask_exporter-0.12.1-cp36-none-any.whl size=14872 \nsha256=f7efe50ec6981fd98a5e3a0a842e0b4de4071a93bd8df8bfdae0bae1e35d9ef6 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\d8\\ac\\e7\\a25835c4165fe8a4c76aec97a19e4ba10834d\nca80ea329b6e4 \n Building wheel for prometheus-client (setup.py) ... done \n Created wheel for prometheus-client: filename=prometheus_client-0.7.1-cp36-none-any.whl size=41404 sha256=e0c1d2f371\nefae659355d6f16de99a3383e5c7518f75f6e0aac83b40692ea0c6 \n Stored in directory: C:\\Users\\dellbook\\AppData\\Local\\pip\\Cache\\wheels\\1c\\54\\34\\fd47cd9b308826cc4292b54449c1899a30251\nef3b506bc91ea \nSuccessfully built mlflow databricks-cli querystring-parser prometheus-flask-exporter prometheus-client \nInstalling collected packages: click, tabulate, configparser, databricks-cli, Jinja2, itsdangerous, Flask, smmap2, git\ndb2, gitpython, querystring-parser, simplejson, pypiwin32, docker, entrypoints, sqlparse, gorilla, prometheus-client, \nprometheus-flask-exporter, waitress, mlflow \nSuccessfully installed Flask-1.1.1 Jinja2-2.10.3 click-7.0 configparser-4.0.2 databricks-cli-0.9.1 docker-4.1.0 entryp\noints-0.3 gitdb2-2.0.6 gitpython-3.0.5 gorilla-0.3.0 itsdangerous-1.1.0 mlflow-1.5.0 prometheus-client-0.7.1 prometheu\ns-flask-exporter-0.12.1 pypiwin32-223 querystring-parser-1.2.4 simplejson-3.17.0 smmap2-2.0.5 sqlparse-0.3.0 tabulate-\n0.8.6 waitress-1.4.2 \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do predict \npredict \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 427, in main \n module, model, session = load(arg.load_folder) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 219, in load \n return load_tf(folder_name, filename) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\", line 114, in load_tf \n model_uri = foldername + \"/\" + filename \nTypeError: must be str, not NoneType \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do predict \npredict \nWARNING:tensorflow:From d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py:116: The name tf.Session is deprecated. Ple\nase use tf.compat.v1.Session instead. \n \n2020-01-13 02:18:51.966285: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized w\nith Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: AVX AVX2 \nTo enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags. \n2020-01-13 02:18:51.983231: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with defaul\nt inter op setting: 12. Tune using inter_op_parallelism_threads for best performance. \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 427, in main \n module, model, session = load(arg.load_folder) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 219, in load \n return load_tf(folder_name, filename) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\", line 119, in load_tf \n tf_sess=tf_sess) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\mlflow\\tensorflow.py\", line 283, in load_model \n _get_and_parse_flavor_configuration(model_path=local_model_path) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\mlflow\\tensorflow.py\", line 346, in _get_and_parse_flavor_\nconfiguration \n flavor_conf = _get_flavor_configuration(model_path=model_path, flavor_name=FLAVOR_NAME) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\mlflow\\utils\\model_utils.py\", line 26, in _get_flavor_conf\niguration \n RESOURCE_DOES_NOT_EXIST) \nmlflow.exceptions.MlflowException: Could not find an \"MLmodel\" configuration file at \"D:\\_devs\\Python01\\gitdev\\ztest\" \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n g_parser-1.2.4-cp36-none-any.whl size=7085 sha256=cf8abcac3\n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> Cache\\wheels\\1e\\41\\34\\23ebf5d1089a9aed847951e0ee375426eb4ad\n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do predict \npredict .. done \nTraceback (most recent call last): metheus_flask_exporter-0.12.1-cp36-none-any.whl size=14872 \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_modelsbae1e35d9ef6 \n-script.py\", line 11, in <module> Cache\\wheels\\d8\\ac\\e7\\a25835c4165fe8a4c76aec97a19e4ba10834d\n load_entry_point('mlmodels', 'console_scripts', 'ml_mod \nels')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.pclient-0.7.1-cp36-none-any.whl size=41404 sha256=e0c1d2f371\ny\", line 427, in main \n module, model, session = load(arg.load_folder) Cache\\wheels\\1c\\54\\34\\fd47cd9b308826cc4292b54449c1899a30251\n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.p \ny\", line 219, in load prometheus-flask-exporter prometheus-client \n return load_tf(folder_name, filename) r, databricks-cli, Jinja2, itsdangerous, Flask, smmap2, git\n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\"docker, entrypoints, sqlparse, gorilla, prometheus-client, \n, line 114, in load_tf \n model_uri = foldername + \"/\" + filename configparser-4.0.2 databricks-cli-0.9.1 docker-4.1.0 entryp\nTypeError: must be str, not NoneType gerous-1.1.0 mlflow-1.5.0 prometheus-client-0.7.1 prometheu\n 2.4 simplejson-3.17.0 smmap2-2.0.5 sqlparse-0.3.0 tabulate-\n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do predict \npredict \nWARNING:tensorflow:From d:\\_devs\\python01\\gitdev\\mlmodels\\m \nlmodels\\util.py:116: The name tf.Session is deprecated. Ple \nase use tf.compat.v1.Session instead. \n -script.py\", line 11, in <module> \n2020-01-13 02:18:51.966285: I tensorflow/core/platform/cpu_els')() \nfeature_guard.cc:145] This TensorFlow binary is optimized wy\", line 427, in main \nith Intel(R) MKL-DNN to use the following CPU instructions \nin performance critical operations: AVX AVX2 y\", line 219, in load \nTo enable them in non-MKL-DNN operations, rebuild TensorFlo \nw with the appropriate compiler flags. , line 114, in load_tf \n2020-01-13 02:18:51.983231: I tensorflow/core/common_runtim \ne/process_util.cc:115] Creating new thread pool with defaul \nt inter op setting: 12. Tune using inter_op_parallelism_thr \neads for best performance. \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models \n-script.py\", line 11, in <module> \n load_entry_point('mlmodels', 'console_scripts', 'ml_modlmodels\\util.py:116: The name tf.Session is deprecated. Ple\nels')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.p \ny\", line 427, in main feature_guard.cc:145] This TensorFlow binary is optimized w\n module, model, session = load(arg.load_folder) in performance critical operations: AVX AVX2 \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.pw with the appropriate compiler flags. \ny\", line 219, in load e/process_util.cc:115] Creating new thread pool with defaul\n return load_tf(folder_name, filename) eads for best performance. \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\util.py\" \n, line 119, in load_tf -script.py\", line 11, in <module> \n tf_sess=tf_sess) els')() \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packagesy\", line 427, in main \n\\mlflow\\tensorflow.py\", line 283, in load_model \n _get_and_parse_flavor_configuration(model_path=local_moy\", line 219, in load \ndel_path) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages, line 119, in load_tf \n\\mlflow\\tensorflow.py\", line 346, in _get_and_parse_flavor_ \nconfiguration \\mlflow\\tensorflow.py\", line 283, in load_model \n flavor_conf = _get_flavor_configuration(model_path=modedel_path) \nl_path, flavor_name=FLAVOR_NAME) \\mlflow\\tensorflow.py\", line 346, in _get_and_parse_flavor_\n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages \n\\mlflow\\utils\\model_utils.py\", line 26, in _get_flavor_confl_path, flavor_name=FLAVOR_NAME) \niguration \\mlflow\\utils\\model_utils.py\", line 26, in _get_flavor_conf\n RESOURCE_DOES_NOT_EXIST) \nmlflow.exceptions.MlflowException: Could not find an \"MLmod \nel\" configuration file at \"D:\\_devs\\Python01\\gitdev\\ztest\" el\" configuration file at \"D:\\_devs\\Python01\\gitdev\\ztest\" \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n module, model, session = load(arg.load_f g_parser-1.2.4-cp36-none-any.whl size=7085 sha256=cf8abcac3\nolder) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\ml Cache\\wheels\\1e\\41\\34\\23ebf5d1089a9aed847951e0ee375426eb4ad\nmodels\\models.py\", line 219, in load -do predict \n return load_tf(folder_name, filename) .. done \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\ml metheus_flask_exporter-0.12.1-cp36-none-any.whl size=14872 \nmodels\\util.py\", line 114, in load_tf ripts\\ml_modelsbae1e35d9ef6 \n model_uri = foldername + \"/\" + filename Cache\\wheels\\d8\\ac\\e7\\a25835c4165fe8a4c76aec97a19e4ba10834d\nTypeError: must be str, not NoneType ripts', 'ml_mod \n \n(ztest) D:\\_devs\\Python01\\gitdev> models\\models.pclient-0.7.1-cp36-none-any.whl size=41404 sha256=e0c1d2f371\n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models -older) Cache\\wheels\\1c\\54\\34\\fd47cd9b308826cc4292b54449c1899a30251\n-do predict models\\models.p \npredict prometheus-flask-exporter prometheus-client \nWARNING:tensorflow:From d:\\_devs\\python01\\gi r, databricks-cli, Jinja2, itsdangerous, Flask, smmap2, git\ntdev\\mlmodels\\mlmodels\\util.py:116: The namemodels\\util.py\"docker, entrypoints, sqlparse, gorilla, prometheus-client, \n tf.Session is deprecated. Please use tf.com \npat.v1.Session instead. configparser-4.0.2 databricks-cli-0.9.1 docker-4.1.0 entryp\n gerous-1.1.0 mlflow-1.5.0 prometheus-client-0.7.1 prometheu\n2020-01-13 02:18:51.966285: I tensorflow/cor 2.4 simplejson-3.17.0 smmap2-2.0.5 sqlparse-0.3.0 tabulate-\ne/platform/cpu_feature_guard.cc:145] This Te \nnsorFlow binary is optimized with Intel(R) M \nKL-DNN to use the following CPU instructions-do predict \n in performance critical operations: AVX AV \nX2 tdev\\mlmodels\\m \nTo enable them in non-MKL-DNN operations, redeprecated. Ple \nbuild TensorFlow with the appropriate compil \ner flags. -script.py\", line 11, in <module> \n2020-01-13 02:18:51.983231: I tensorflow/core/platform/cpu_els')() \ne/common_runtime/process_util.cc:115] Creati is optimized wy\", line 427, in main \nng new thread pool with default inter op setU instructions \nting: 12. Tune using inter_op_parallelism_th2 y\", line 219, in load \nreads for best performance. build TensorFlo \nTraceback (most recent call last): , line 114, in load_tf \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Sce/common_runtim \nripts\\ml_models-script.py\", line 11, in <modool with defaul \nule> parallelism_thr \n load_entry_point('mlmodels', 'console_sc \nripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlripts\\ml_models \nmodels\\models.py\", line 427, in main \n module, model, session = load(arg.load_fripts', 'ml_modlmodels\\util.py:116: The name tf.Session is deprecated. Ple\nolder) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.p \nmodels\\models.py\", line 219, in load feature_guard.cc:145] This TensorFlow binary is optimized w\n return load_tf(folder_name, filename) older) in performance critical operations: AVX AVX2 \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.pw with the appropriate compiler flags. \nmodels\\util.py\", line 119, in load_tf e/process_util.cc:115] Creating new thread pool with defaul\n tf_sess=tf_sess) eads for best performance. \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\limodels\\util.py\" \nb\\site-packages\\mlflow\\tensorflow.py\", line -script.py\", line 11, in <module> \n283, in load_model els')() \n _get_and_parse_flavor_configuration(modeb\\site-packagesy\", line 427, in main \nl_path=local_model_path) del \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lil_path=local_moy\", line 219, in load \nb\\site-packages\\mlflow\\tensorflow.py\", line \n346, in _get_and_parse_flavor_configuration b\\site-packages, line 119, in load_tf \n flavor_conf = _get_flavor_configuration(d_parse_flavor_ \nmodel_path=model_path, flavor_name=FLAVOR_NA \\mlflow\\tensorflow.py\", line 283, in load_model \nME) model_path=modedel_path) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\li \\mlflow\\tensorflow.py\", line 346, in _get_and_parse_flavor_\nb\\site-packages\\mlflow\\utils\\model_utils.py\"b\\site-packages \n, line 26, in _get_flavor_configuration get_flavor_confl_path, flavor_name=FLAVOR_NAME) \n RESOURCE_DOES_NOT_EXIST) \\mlflow\\utils\\model_utils.py\", line 26, in _get_flavor_conf\nmlflow.exceptions.MlflowException: Could not \n find an \"MLmodel\" configuration file at \"D: find an \"MLmod \n\\_devs\\Python01\\gitdev\\ztest\" \\gitdev\\ztest\" el\" configuration file at \"D:\\_devs\\Python01\\gitdev\\ztest\" \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> size=7085 sha256=cf8abcac3\n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> 9a9aed847951e0ee375426eb4ad\n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do generate_template --outputfoler ztes2 \nusage: ml_models [-h] [--config_file CONFIG_FILE] [--config_mode CONFIG_MODE] \n [--log_file LOG_FILE] [--do DO] [--model_uri MODEL_URI] 36-none-any.whl size=14872 \n [--load_folder LOAD_FOLDER] [--dataname DATANAME] \n [--save_folder SAVE_FOLDER] 5fe8a4c76aec97a19e4ba10834d\nml_models: error: unrecognized arguments: --outputfoler ztes2 \n \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do generate_template --save_path ztest2 ize=41404 sha256=e0c1d2f371\nusage: ml_models [-h] [--config_file CONFIG_FILE] [--config_mode CONFIG_MODE] \n [--log_file LOG_FILE] [--do DO] [--model_uri MODEL_URI] 8826cc4292b54449c1899a30251\n [--load_folder LOAD_FOLDER] [--dataname DATANAME] \n [--save_folder SAVE_FOLDER] theus-client \nml_models: error: unrecognized arguments: --save_path ztest2 ngerous, Flask, smmap2, git\n orilla, prometheus-client, \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do generate_template --save_folder ztest2 \ngenerate_template i-0.9.1 docker-4.1.0 entryp\nztest2 heus-client-0.7.1 prometheu\n .5 sqlparse-0.3.0 tabulate-\n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do generate_template --save_folder ztest2 \ngenerate_template \nztest2 \nTraceback (most recent call last): \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\Scripts\\ml_models-script.py\", line 11, in <module \n> > \n load_entry_point('mlmodels', 'console_scripts', 'ml_models')() \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 457, in main \n config_generate_template(arg.model_uri, to_path= arg.save_folder) \n File \"d:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\models.py\", line 364, in config_generate \n_template \n shutil.copytree(os_root+\"/template/\", to_path) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\shutil.py\", line 315, in copytree \n os.makedirs(dst) \n File \"D:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\os.py\", line 220, in makedirs \n mkdir(name, mode) \nFileExistsError: [WinError 183] Cannot create a file when that file already exists: 'ztest2 \n' \n \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do generate_template --save_folder ztest3 .Session is deprecated. Ple\ngenerate_template \nztest3 \n rFlow binary is optimized w\n(ztest) D:\\_devs\\Python01\\gitdev> ns: AVX AVX2 \n(ztest) D:\\_devs\\Python01\\gitdev> flags. \n(ztest) D:\\_devs\\Python01\\gitdev> new thread pool with defaul\n(ztest) D:\\_devs\\Python01\\gitdev>ml_models --do test \ntest \nmodel_tf/1_lstm.py > \nmodel_tf.1_lstm.py \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\m \nodel_tf\\\\1_lstm.py'> \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\m \nodel_tf\\\\1_lstm.py'> \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n \n ('############# Data, Params preparation #################',) , in load_model \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'da \nta_type': 'pandas'} , in _get_and_parse_flavor_\nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 ine 26, in _get_flavor_conf\n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 evs\\Python01\\gitdev\\ztest\" \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n \n ('############ Model preparation #########################',) \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> <mlmodels.mod\nel_tf.1_lstm.Model object at 0x000001E819DD7D68> \n \n ('############ Model fit ##################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-11 \nOMP: Info #213: KMP_AFFINITY: decoding x2APIC ids. \nOMP: Info #276: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info \nOMP: Info #156: KMP_AFFINITY: 12 available OS procs \nOMP: Info #157: KMP_AFFINITY: Uniform topology \nOMP: Info #191: KMP_AFFINITY: 1 socket x 6 cores/socket x 2 threads/core (6 total cores) \nOMP: Info #215: KMP_AFFINITY: OS proc to physical thread map: \nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 6 maps to socket 0 core 3 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 7 maps to socket 0 core 3 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 8 maps to socket 0 core 4 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 9 maps to socket 0 core 4 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 10 maps to socket 0 core 5 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 11 maps to socket 0 core 5 thread 1 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9348 thread 0 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 7520 thread 1 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 7200 thread 2 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9212 thread 3 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9752 thread 4 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15232 thread 5 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 10468 thread 6 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 13520 thread 7 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15380 thread 8 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 12408 thread 10 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9088 thread 9 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9604 thread 11 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 12380 thread 12 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 2068 thread 13 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 10320 thread 14 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 13500 thread 15 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 16516 thread 17 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 16676 thread 16 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9156 thread 18 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 4956 thread 19 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 13644 thread 20 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 16012 thread 21 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9492 thread 22 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 2960 thread 23 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15560 thread 24 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 11780 thread 25 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 14484 thread 26 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 17056 thread 27 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 7880 thread 28 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 12876 thread 29 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 2876 thread 30 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 1180 thread 31 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 14088 thread 32 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 12200 thread 33 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15844 thread 34 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15796 thread 35 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 16336 thread 36 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 11200 thread 37 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9932 thread 38 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 11104 thread 39 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 8960 thread 40 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 10148 thread 41 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 5472 thread 42 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 8672 thread 46 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 13944 thread 44 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 8892 thread 45 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 8464 thread 43 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 17152 thread 47 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9584 thread 48 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 8108 thread 49 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 420 thread 50 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 1440 thread 51 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15600 thread 52 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15956 thread 53 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 16764 thread 54 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 17316 thread 55 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15920 thread 56 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 4928 thread 57 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 6332 thread 59 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 10540 thread 58 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 17260 thread 60 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 14612 thread 61 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15264 thread 62 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 2448 thread 63 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 12076 thread 64 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 6860 thread 65 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 9696 thread 66 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 4144 thread 67 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 15324 thread 68 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 1932 thread 69 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 14116 thread 70 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 3016 tid 16064 thread 71 bound to OS proc set 11 \nfit success <tensorflow.python.client.session.InteractiveSession object at 0x000001E8251E1630> \n \n ('############ Prediction##########################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \n.. ... ... ... ... ... ... \n60 0.560716 0.537236 0.633156 0.609599 0.609599 0.378373 \n61 0.590968 0.536554 0.639892 0.598604 0.598604 0.346890 \n62 0.527891 0.553723 0.648035 0.627071 0.627071 0.227211 \n63 0.626475 0.591245 0.732181 0.656793 0.656793 0.210688 \n64 0.591182 0.542012 0.680708 0.655287 0.655287 0.143610 \n \n[65 rows x 6 columns] \n[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 \n 0.00000000e+00 0.00000000e+00] \n [-2.83036381e-02 7.24661201e-02 7.21882805e-02 4.12472486e-02 \n 7.26911724e-02 4.58739325e-02] \n [ 9.78736132e-02 3.58187258e-02 8.79325792e-02 2.07251921e-01 \n 1.24227554e-01 1.37837827e-01] \n [ 3.83277349e-02 -6.46874756e-02 1.41525880e-01 1.09898522e-01 \n 1.05776802e-01 6.12371676e-02] \n [ 2.28772074e-01 2.85574198e-01 2.65458375e-01 2.89490104e-01 \n 2.81718880e-01 5.90199381e-02] \n [ 3.41748148e-01 3.40945423e-01 3.50206256e-01 6.05514169e-01 \n 7.92744160e-02 6.09104484e-02] \n [ 1.06904477e-01 2.85930097e-01 3.18046510e-01 -9.89688188e-03 \n 2.43115813e-01 1.54380396e-01] \n [ 4.89726514e-01 -5.49919046e-02 4.81282234e-01 3.83934945e-01 \n 8.24689418e-02 1.87279418e-01] \n [ 1.24004960e-01 3.44698608e-01 -5.58834150e-02 -1.21683329e-01 \n 1.94135278e-01 2.80137509e-02] \n [ 7.64592141e-02 4.51426923e-01 3.47759306e-01 2.02689022e-01 \n -6.33269772e-02 3.47903371e-01] \n [ 2.37354741e-01 4.25677858e-02 3.24402809e-01 2.06657514e-01 \n 1.92868143e-01 1.28126636e-01] \n [ 2.61610925e-01 1.17272213e-02 4.87228841e-01 4.21857178e-01 \n 1.20584235e-01 1.78109631e-01] \n [ 4.32333916e-01 -1.07962087e-01 5.06881893e-01 3.25102776e-01 \n 2.43876725e-01 2.66496748e-01] \n [ 2.81728208e-01 4.20419574e-01 2.20075279e-01 1.45566806e-01 \n -1.01313926e-02 -6.99030459e-02] \n [ 2.55991668e-01 4.67335694e-02 4.38549817e-01 5.87519825e-01 \n 5.54904282e-01 4.13248301e-01] \n [-1.21337920e-03 3.79116356e-01 2.25046143e-01 3.21759790e-01 \n 1.90530121e-01 7.63055980e-02] \n [ 3.81085314e-02 -5.49410097e-02 2.48509988e-01 8.46182778e-02 \n -1.03201918e-01 1.94945946e-01] \n [ 2.78254114e-02 4.60547268e-01 2.30145827e-01 3.05683941e-01 \n 1.51448041e-01 1.56455830e-01] \n [ 2.81868637e-01 6.06122687e-02 1.41781896e-01 1.72935769e-01 \n 1.43995509e-02 1.91848800e-01] \n [ 3.25233787e-01 8.41011107e-02 3.47427428e-01 3.38478774e-01 \n -1.98381871e-01 2.09263772e-01] \n [-5.47617190e-02 5.92153072e-01 2.72812724e-01 2.66546577e-01 \n 6.05992794e-01 6.02982566e-02] \n [ 1.04755983e-01 3.47517461e-01 5.29115081e-01 3.24989498e-01 \n 2.20477551e-01 3.16758811e-01] \n [ 1.73308894e-01 2.07855195e-01 9.90387276e-02 3.23349416e-01 \n 7.65217692e-02 2.00630918e-01] \n [ 1.39437601e-01 -1.71168521e-01 2.08030045e-01 4.45374966e-01 \n -6.45940155e-02 2.50833362e-01] \n [ 7.11937845e-02 5.52353151e-02 3.58057857e-01 4.08151954e-01 \n 2.27948844e-01 1.28007144e-01] \n [ 4.86645848e-01 1.91521153e-01 1.38529286e-01 1.23307660e-01 \n 1.21017911e-01 2.12929696e-01] \n [ 1.67324826e-01 2.50022769e-01 -1.88588370e-02 2.50461102e-01 \n 1.27013624e-01 -8.04486051e-02] \n [ 4.81188804e-01 1.52930155e-01 4.70611364e-01 -1.50140040e-02 \n 1.01123698e-01 2.28290781e-01] \n [-8.06046575e-02 1.91061378e-01 2.50087291e-01 2.53705740e-01 \n 2.44013608e-01 -3.10732145e-03] \n [ 3.59959215e-01 2.89734691e-01 4.66079414e-02 4.87287760e-01 \n 5.78765690e-01 2.31859162e-01] \n [ 1.08596891e-01 5.39850950e-01 -2.49924988e-01 4.14807796e-01 \n 6.93489611e-01 4.20473188e-01] \n [ 3.31742346e-01 1.66839927e-01 5.00477135e-01 3.60172808e-01 \n 1.75037801e-01 3.28090161e-01] \n [ 7.07607150e-01 1.30112320e-01 4.07003552e-01 4.39327210e-01 \n 5.69266826e-03 7.00649172e-02] \n [ 1.29607126e-01 5.69292963e-01 5.86673141e-01 7.85103798e-01 \n 9.81178582e-01 3.03082108e-01] \n [ 6.46582365e-01 1.20081115e+00 7.86919966e-02 3.94246995e-01 \n 1.01782642e-01 9.01698768e-01] \n [ 4.49521214e-01 5.81871867e-01 1.24560428e+00 6.76770627e-01 \n 6.16035759e-01 1.72711998e-01] \n [ 3.86566430e-01 3.90547484e-01 5.69214821e-01 3.10203016e-01 \n 4.30457652e-01 1.26662120e-01] \n [ 2.07875028e-01 3.10491361e-02 2.53343489e-02 3.26923460e-01 \n 5.58314085e-01 -2.36281648e-01] \n [ 4.04472798e-01 3.70327711e-01 4.26938593e-01 5.49170196e-01 \n 7.48649776e-01 1.50868967e-01] \n [ 1.58546224e-01 3.19081545e-01 1.70701504e-01 4.33681726e-01 \n 9.65523541e-01 5.67471266e-01] \n [-2.92969435e-01 4.95546430e-01 1.51313305e-01 2.32422292e-01 \n 3.63472283e-01 5.95198497e-02] \n [ 3.76116365e-01 1.91568449e-01 5.19959152e-01 2.04664215e-01 \n 1.14873804e-01 6.06814325e-01] \n [ 2.16541454e-01 4.20868218e-01 5.96932471e-01 6.54063523e-01 \n 2.10689932e-01 2.39220932e-01] \n [ 7.50704885e-01 5.57535350e-01 6.04393542e-01 3.51950914e-01 \n -1.41364217e-01 1.64234117e-01] \n [-7.21644610e-02 -2.81320140e-03 3.34349304e-01 1.71303838e-01 \n 7.13772103e-02 2.35414669e-01] \n [-7.00910091e-02 6.68835044e-01 3.04758668e-01 2.55401015e-01 \n -2.47705817e-01 2.48966664e-01] \n [ 2.70096511e-01 3.12385201e-01 2.96167284e-01 2.58908719e-01 \n 4.75609750e-01 -3.84852588e-02] \n [ 9.53703523e-02 5.08292854e-01 4.09158111e-01 1.05258882e+00 \n 5.50532401e-01 3.66398931e-01] \n [ 2.15012655e-01 1.27323902e+00 5.47618449e-01 1.15677547e+00 \n 6.39289677e-01 7.28569031e-02] \n [ 6.08812749e-01 3.26477349e-01 3.58320683e-01 3.70615572e-01 \n 2.50308514e-01 3.56481001e-02] \n [ 1.02312303e+00 -5.96995540e-02 7.92187929e-01 7.18003213e-01 \n 2.11499721e-01 1.77965090e-01] \n [-1.09882116e-01 4.03346360e-01 3.33025515e-01 1.24272943e-01 \n -5.45066297e-01 4.63934630e-01] \n [ 4.03555930e-02 -2.33266339e-01 2.40586787e-01 2.84885854e-01 \n -1.32130533e-01 3.31846803e-01] \n [ 5.74311793e-01 3.22989553e-01 4.07784790e-01 1.07911497e-01 \n -8.45829695e-02 7.28450060e-01] \n [ 4.67057735e-01 4.47554998e-02 4.05834526e-01 4.16054279e-01 \n 9.38593522e-02 5.58671534e-01] \n [ 3.84668857e-01 1.85150728e-01 4.17745352e-01 2.26418242e-01 \n 5.00530377e-02 1.90177262e-01] \n [-3.75049599e-02 1.53771490e-01 1.35451004e-01 3.29838961e-01 \n 2.05583572e-01 -1.39926448e-01] \n [ 5.18272877e-01 4.90480125e-01 6.19990155e-02 6.26294017e-01 \n 4.88657445e-01 3.23958337e-01] \n [ 8.28012526e-01 -1.32961750e-01 6.25526369e-01 2.34898478e-01 \n -3.37309539e-01 1.85643539e-01] \n [-6.00569509e-02 4.99962449e-01 3.50067049e-01 9.82723236e-02 \n 4.91326243e-01 -2.14215219e-02] \n [ 5.41095316e-01 9.23101902e-02 1.92281470e-01 3.85143042e-01 \n 3.82758677e-01 5.08534193e-01] \n [ 3.28303844e-01 8.40586245e-01 7.14291036e-01 5.89610279e-01 \n 8.12445104e-01 2.45921746e-01] \n [-9.32898372e-02 -1.90743104e-01 2.70811409e-01 1.76350430e-01 \n 4.15155143e-01 -6.19105026e-02] \n [-1.72103822e-01 4.06223983e-01 9.31922942e-02 3.23024541e-01 \n 1.63356334e-01 4.29687470e-01] \n [ 7.12149739e-01 1.17427260e-01 5.96423268e-01 7.67015278e-01 \n 2.83213630e-02 4.66459274e-01]] \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev> \n(ztest) D:\\_devs\\Python01\\gitdev>ml_models \ntest \nmodel_tf/1_lstm.py \nmodel_tf.1_lstm.py \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n \n ('############# Data, Params preparation #################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n \n ('############ Model preparation #########################',) \n<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> <mlmodels.mod\nel_tf.1_lstm.Model object at 0x000001E810237C18> \n \n ('############ Model fit ##################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-11 \nOMP: Info #213: KMP_AFFINITY: decoding x2APIC ids. \nOMP: Info #276: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info \nOMP: Info #156: KMP_AFFINITY: 12 available OS procs \nOMP: Info #157: KMP_AFFINITY: Uniform topology \nOMP: Info #191: KMP_AFFINITY: 1 socket x 6 cores/socket x 2 threads/core (6 total cores) \nOMP: Info #215: KMP_AFFINITY: OS proc to physical thread map: \nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 6 maps to socket 0 core 3 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 7 maps to socket 0 core 3 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 8 maps to socket 0 core 4 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 9 maps to socket 0 core 4 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 10 maps to socket 0 core 5 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 11 maps to socket 0 core 5 thread 1 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 152 thread 0 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 8448 thread 1 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 1556 thread 3 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 16572 thread 2 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14936 thread 4 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14796 thread 5 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 5884 thread 6 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14968 thread 7 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 5228 thread 8 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 1500 thread 9 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 1636 thread 10 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 13128 thread 11 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 16556 thread 12 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 9512 thread 13 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 11868 thread 14 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 10180 thread 15 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 12648 thread 17 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 17240 thread 16 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 16632 thread 18 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 12392 thread 19 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 15644 thread 20 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14696 thread 21 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 16672 thread 22 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 600 thread 23 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 2532 thread 24 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14916 thread 25 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 12372 thread 26 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 15656 thread 27 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 8356 thread 28 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 15460 thread 29 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 6600 thread 31 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 10852 thread 30 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 8020 thread 32 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 3324 thread 33 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14452 thread 35 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 15192 thread 34 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 1728 thread 37 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 10812 thread 36 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 2980 thread 38 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 12312 thread 40 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 8088 thread 39 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14540 thread 41 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 12788 thread 42 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 5744 thread 43 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 16264 thread 44 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 13636 thread 45 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 3184 thread 46 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 8732 thread 47 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 6844 thread 48 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 17388 thread 49 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 1984 thread 50 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 13344 thread 51 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 8800 thread 53 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14512 thread 52 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 15976 thread 54 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 12436 thread 55 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14072 thread 56 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 16484 thread 57 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 12892 thread 58 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14048 thread 59 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 1644 thread 60 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 15792 thread 61 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 2268 thread 62 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 16088 thread 63 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 9224 thread 67 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 2132 thread 65 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 14660 thread 66 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 17364 thread 64 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 13116 thread 68 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 16932 thread 69 bound to OS proc set 7 \nfit success <tensorflow.python.client.session.InteractiveSession object at 0x000001E81B704710> \n \n ('############ Prediction##########################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \n.. ... ... ... ... ... ... \n60 0.560716 0.537236 0.633156 0.609599 0.609599 0.378373 \n61 0.590968 0.536554 0.639892 0.598604 0.598604 0.346890 \n62 0.527891 0.553723 0.648035 0.627071 0.627071 0.227211 \n63 0.626475 0.591245 0.732181 0.656793 0.656793 0.210688 \n64 0.591182 0.542012 0.680708 0.655287 0.655287 0.143610 \n \n[65 rows x 6 columns] \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 15412 thread 70 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 15640 tid 8416 thread 71 bound to OS proc set 11 \n[[ 0. 0. 0. 0. 0. 0. ] \n [ 0.16727871 0.10273398 0.12813526 0.0590217 0.13900933 0.14632134] \n [ 0.05550172 0.06859855 0.16265857 0.06395088 0.07979055 0.07479915] \n [ 0.15846148 0.03595349 0.16198802 0.04624098 0.12925094 0.12300833] \n [ 0.16635281 0.22181672 0.15394078 0.17495783 -0.01115535 0.03368502] \n [ 0.18376261 0.17330498 0.26248178 0.30825853 0.05103201 0.10202948] \n [ 0.18477225 0.23791227 -0.06673861 0.30289483 -0.02747793 -0.12673196] \n [ 0.4745653 0.04836376 0.25605398 0.40409815 -0.20084071 0.01341098] \n [ 0.15468284 0.12603766 0.24067658 0.42848149 -0.14799079 0.20589396] \n [ 0.51495427 0.62599707 0.34094885 0.23370267 0.24699146 -0.06181645] \n [ 0.10227656 0.03419564 0.1614528 0.26344055 0.09891976 0.28078812] \n [ 0.43092832 0.64928126 0.26701194 0.20680946 0.14557415 0.24541019] \n [ 0.54771799 0.56729537 0.40484491 0.40388969 0.6338073 0.31173354] \n [ 0.16842753 0.15504134 0.05953354 0.19975284 0.18450987 0.16525964] \n [ 0.36009192 0.26304525 0.11751229 0.28210339 0.07971793 0.11384951] \n [ 0.32048103 0.19004449 0.32168376 -0.13685526 0.23722309 0.35396671] \n [ 0.58766961 -0.12337782 0.51595312 0.16031575 0.25209942 0.22098644] \n [ 0.18313575 0.44298485 0.37690705 0.362463 -0.24279723 0.09426816] \n [ 0.49181703 0.22773796 0.39674529 0.55179095 0.24168745 0.26018113] \n [ 0.19156 0.04524945 0.0342287 0.4133898 0.08830376 -0.15183951] \n [ 0.10169428 -0.01050712 0.2443212 0.12314878 0.22464621 0.14148779] \n [ 0.1188987 0.07241069 0.02704019 0.0784905 0.27905306 0.13283682] \n [ 0.18386757 0.12427562 0.12260126 0.19273247 0.17293194 0.30547041] \n [ 0.00644146 0.03420936 0.18319194 0.20661139 0.43687293 0.05318025] \n [ 0.2117525 0.1175264 -0.1367861 0.18806405 0.08982964 0.15652063] \n [ 0.32768342 0.10346427 0.07797579 0.03952445 0.08919846 -0.13012423] \n [ 0.41267624 0.478297 0.24283022 0.33207673 0.30055976 0.24983209] \n [ 0.30942366 0.16835833 0.25353161 0.26221791 0.47024825 0.01331476] \n [-0.02936593 0.35525417 0.55726421 0.41630471 0.28104377 -0.16176122] \n [ 0.47210583 0.04528286 -0.13546917 0.52989399 0.68121612 0.4176448 ] \n [ 0.21921647 0.44814989 0.38082653 -0.14382145 0.15284681 0.26631868] \n [ 0.16184834 0.55069178 0.46505308 0.41798812 0.29873523 0.12943724] \n [ 0.73112833 -0.06385264 0.27531719 0.84104908 0.60893399 0.28861499] \n [ 0.54163963 0.52712971 0.08260353 0.30378097 0.39127243 0.47275859] \n [ 0.3129915 0.3214497 0.18873714 0.32797605 -0.2182624 0.1078036 ] \n [ 0.34606147 0.33093095 0.19534229 0.20131926 0.21069497 0.05399793] \n [ 0.54046774 0.19081622 0.13415818 0.51614636 -0.08599758 0.65500563] \n [ 0.91947794 0.40086132 0.64860028 0.58280784 0.36219832 0.29839104] \n [-0.08488071 0.04020375 0.56182015 0.2549642 0.33237484 -0.15617837] \n [ 0.41234031 0.12544295 0.18058214 0.43386292 0.35236958 -0.15482791] \n [ 0.22549957 0.10164826 0.15238115 0.6388858 0.25510469 0.19520263] \n [ 0.31957644 0.52749908 0.36017337 0.21620029 0.15648299 0.45865995] \n [ 0.22676086 0.15520281 0.29181978 0.28635871 0.27364299 0.23102595] \n [ 0.23867637 0.42351982 0.53234291 0.43910486 0.10094831 -0.13048762] \n [ 0.69359648 0.01375426 0.65455377 0.67153174 0.08367517 0.53638434] \n [ 0.48905843 0.0561026 0.64627343 0.55668247 0.33520594 -0.09293444] \n [ 0.36811557 0.21402895 0.0059501 0.20098656 0.17250139 0.4194054 ] \n [ 0.44235253 0.71766174 0.55882132 0.23309699 0.38749388 0.331631 ] \n [ 0.15821892 0.58839393 0.30799842 0.48786348 0.14345858 0.46577156] \n [-0.0788281 0.0376374 0.31186116 0.44794285 0.44308552 -0.16809982] \n [ 0.77930158 0.84271163 0.5407244 0.64865828 0.46705607 0.1556197 ] \n [ 0.96861887 1.04839909 0.53601265 0.08484117 0.41587681 0.01825681] \n [ 0.21003413 -0.18068206 0.1988894 0.34964532 0.96497822 0.42281741] \n [ 1.00389135 0.66514659 0.24443488 0.47567955 0.3212963 0.42953533] \n [ 0.36681095 0.41671622 0.81423825 0.45924884 -0.07839511 -0.06544094] \n [ 0.1814006 0.93170547 0.83664376 0.44555908 0.09031222 0.26461643] \n [ 0.31817737 0.00883216 0.28180394 0.35972828 0.55475038 0.52036536] \n [-0.21947643 -0.04913045 0.15539037 0.73555028 0.10031357 0.15093578] \n [ 0.43847045 0.76785928 0.82381922 0.52886206 0.3352952 -0.02996786] \n [ 0.14980316 1.48585534 1.04169166 1.34887743 0.31362936 -0.03214565] \n [ 0.89142472 -0.46704626 0.65300167 0.80588657 0.8070327 -0.00218005] \n [ 0.00468475 1.07924509 0.24328114 0.63929957 0.84377915 0.1583609 ] \n [ 0.80925077 -0.14105698 0.19272502 0.66841 1.10401249 0.53016359] \n [ 0.51309109 0.55593312 -0.20175824 0.43505752 0.24536428 0.57712084] \n [ 0.67326546 0.18364066 0.38297683 0.69932938 0.24917531 -0.10968751]] \n \n(ztest) D:\\_devs\\Python01\\gitdev>ml_optim \n \n ('model details', 'model_tf.1_lstm', {'learning_rate': {'type': 'log_uniform', 'init': 0.01, 'range': [0.001, 0.1]}, 'num_l\nayers': {'type': 'int', 'init': 2, 'range': [2, 4]}, 'size': {'type': 'int', 'init': 6, 'range': [6, 6]}, 'output_size': {'ty\npe': 'int', 'init': 6, 'range': [6, 6]}, 'size_layer': {'type': 'categorical', 'value': [128, 256]}, 'timestep': {'type': 'ca\ntegorical', 'value': [5]}, 'epoch': {'type': 'categorical', 'value': [2]}}) \n \n ('data_path', 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv') \n \n ('path_save', 'D:\\\\_devs\\\\Python01\\\\gitdev/ztest/optuna_1lstm/') \n \n ('model_pars', {'learning_rate': {'type': 'log_uniform', 'init': 0.01, 'range': [0.001, 0.1]}, 'num_layers': {'type': 'int'\n, 'init': 2, 'range': [2, 4]}, 'size': {'type': 'int', 'init': 6, 'range': [6, 6]}, 'output_size': {'type': 'int', 'init': 6,\n 'range': [6, 6]}, 'size_layer': {'type': 'categorical', 'value': [128, 256]}, 'timestep': {'type': 'categorical', 'value': [\n5]}, 'epoch': {'type': 'categorical', 'value': [2]}}) \n \n (<module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'>,) \n \n ('###### Hyper-optimization through study ####################################',) \ncheck <module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \n<mlmodels.model_tf.1_lstm.Model object at 0x0000013BA8486CC0> \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-11 \nOMP: Info #213: KMP_AFFINITY: decoding x2APIC ids. \nOMP: Info #276: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info \nOMP: Info #156: KMP_AFFINITY: 12 available OS procs \nOMP: Info #157: KMP_AFFINITY: Uniform topology \nOMP: Info #191: KMP_AFFINITY: 1 socket x 6 cores/socket x 2 threads/core (6 total cores) \nOMP: Info #215: KMP_AFFINITY: OS proc to physical thread map: \nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 6 maps to socket 0 core 3 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 7 maps to socket 0 core 3 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 8 maps to socket 0 core 4 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 9 maps to socket 0 core 4 thread 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 10 maps to socket 0 core 5 thread 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 11 maps to socket 0 core 5 thread 1 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4828 thread 0 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 6956 thread 1 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 13356 thread 5 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15768 thread 2 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15596 thread 4 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 9780 thread 3 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 12388 thread 6 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 9680 thread 7 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4736 thread 11 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15616 thread 8 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16716 thread 10 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 8924 thread 9 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15068 thread 12 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15932 thread 13 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16804 thread 14 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 7568 thread 15 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16780 thread 16 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 13716 thread 17 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15744 thread 18 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 3516 thread 19 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 10620 thread 20 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 12440 thread 21 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 17036 thread 22 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 7344 thread 23 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4460 thread 24 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 14320 thread 25 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 14988 thread 26 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 1340 thread 27 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4464 thread 28 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16512 thread 29 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4616 thread 30 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4896 thread 31 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 3740 thread 32 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 10304 thread 33 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 12936 thread 34 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 14084 thread 35 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 2360 thread 36 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 12432 thread 37 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 17168 thread 38 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15872 thread 39 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 8656 thread 40 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 10280 thread 41 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 13932 thread 42 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 6580 thread 43 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 10272 thread 44 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 9900 thread 45 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16136 thread 47 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 8612 thread 46 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 13328 thread 48 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 13308 thread 49 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15304 thread 50 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16472 thread 51 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 5952 thread 52 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4720 thread 53 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 17284 thread 54 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 14160 thread 55 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 13372 thread 56 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15316 thread 57 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16700 thread 58 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16752 thread 60 bound to OS proc set 0 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 15064 thread 59 bound to OS proc set 11 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16872 thread 61 bound to OS proc set 2 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16532 thread 62 bound to OS proc set 4 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16468 thread 63 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 3820 thread 65 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4168 thread 64 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 9012 thread 66 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 8572 thread 67 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 13696 thread 68 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 7600 thread 69 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 8036 thread 70 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16080 thread 71 bound to OS proc set 11 \n[I 2020-01-13 13:40:58,105] Finished trial#0 resulted in value: 0.22598128049419478. Current best value is 0.2259812804941947\n8 with parameters: {'learning_rate': 0.0076683602050513, 'num_layers': 2, 'size': 6, 'output_size': 6, 'size_layer': 256, 'ti\nmestep': 5, 'epoch': 2}. \ncheck <module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\1_lstm.py'> \n<mlmodels.model_tf.1_lstm.Model object at 0x0000013BAA5D9828> \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1735: UserWarning: An interactive ses\nsion is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close\n()` to release resources held by the other session(s). \n warnings.warn('An interactive session is already active. This can ' \n[I 2020-01-13 13:41:00,201] Finished trial#1 resulted in value: 0.14441770315170288. Current best value is 0.1444177031517028\n8 with parameters: {'learning_rate': 0.007198981040978415, 'num_layers': 2, 'size': 6, 'output_size': 6, 'size_layer': 128, '\ntimestep': 5, 'epoch': 2}. \n \n ################################### ('Optim, finished',) ################################### \n \n ('### Run Model with best ################################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1735: UserWarning: An interactive ses\nsion is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close\n()` to release resources held by the other session(s). \n warnings.warn('An interactive session is already active. This can ' \n \n ('#### Saving ###########################################################',) \n \n ('### Save Stats ##########################################################',) \n \n ############################## ('Finished OPTIMIZATION',) ############################## \n{'learning_rate': 0.007198981040978415, 'num_layers': 2, 'size': 6, 'output_size': 6, 'size_layer': 128, 'timestep': 5, 'epoc\nh': 2, 'best_value': 0.14441770315170288} \n \n(ztest) D:\\_devs\\Python01\\gitdev> \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16468 thread 63 bound to OS proc set 6 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 3820 thread 65 bound to OS proc set 10 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 4168 thread 64 bound to OS proc set 8 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 9012 thread 66 bound to OS proc set 1 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 8572 thread 67 bound to OS proc set 3 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 13696 thread 68 bound to OS proc set 5 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 7600 thread 69 bound to OS proc set 7 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 8036 thread 70 bound to OS proc set 9 \nOMP: Info #251: KMP_AFFINITY: pid 11084 tid 16080 thread 71 bound to OS proc set 11 \n[I 2020-01-13 13:40:58,105] Finished trial#0 resulted in value: 0.22598128049419478. Current best value is \n0.22598128049419478 with parameters: {'learning_rate': 0.0076683602050513, 'num_layers': 2, 'size': 6, 'out \nput_size': 6, 'size_layer': 256, 'timestep': 5, 'epoch': 2}. \ncheck <module 'mlmodels.model_tf.1_lstm' from 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\model_tf\\\\10.2259812804941947\n_lstm.py'> e_layer': 256, 'ti\n<mlmodels.model_tf.1_lstm.Model object at 0x0000013BAA5D9828> \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': _lstm.py'> \n'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv 'pandas'} \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1735: UserWarning: \nAn interactive session is already active. This can cause out-of-memory errors in some cases. You must expliAn interactive ses\ncitly call `InteractiveSession.close()` to release resources held by the other session(s). ctiveSession.close\n warnings.warn('An interactive session is already active. This can ' \n[I 2020-01-13 13:41:00,201] Finished trial#1 resulted in value: 0.14441770315170288. Current best value is \n0.14441770315170288 with parameters: {'learning_rate': 0.007198981040978415, 'num_layers': 2, 'size': 6, 'o0.1444177031517028\nutput_size': 6, 'size_layer': 128, 'timestep': 5, 'epoch': 2}. ize_layer': 128, '\n \n ################################### ('Optim, finished',) ################################### \n \n ('### Run Model with best ################################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': \n'pandas'} 'pandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 \n 0 1 2 3 4 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1735: UserWarning: An interactive ses\nAn interactive session is already active. This can cause out-of-memory errors in some cases. You must explictiveSession.close\ncitly call `InteractiveSession.close()` to release resources held by the other session(s). \n warnings.warn('An interactive session is already active. This can ' \n \n ('#### Saving ###########################################################',) \n \n ('### Save Stats ##########################################################',) \n \n ############################## ('Finished OPTIMIZATION',) ############################## \n{'learning_rate': 0.007198981040978415, 'num_layers': 2, 'size': 6, 'output_size': 6, 'size_layer': 128, 'timestep': 5, 'epoc\nimestep': 5, 'epoch': 2, 'best_value': 0.14441770315170288} \n \n(ztest) D:\\_devs\\Python01\\gitdev> \n \n ################################### ('Optim, finished',) ################################### \n \n ('### Run Model with best ################################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': 'p \nandas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 2259812804941947\n 0 1 2 3 4 5 layer': 256, 'ti\n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 stm.py'> \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 andas'} \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1735: UserWarning: An \n interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitl \ny call `InteractiveSession.close()` to release resources held by the other session(s). \n warnings.warn('An interactive session is already active. This can ' \n \n ('#### Saving ###########################################################',) \n \n ('### Save Stats ##########################################################',) \n \n ############################## ('Finished OPTIMIZATION',) ############################## \n{'learning_rate': 0.007198981040978415, 'num_layers': 2, 'size': 6, 'output_size': 6, 'size_layer': 128, 'tim \nestep': 5, 'epoch': 2, 'best_value': 0.14441770315170288} \n interactive ses\n(ztest) D:\\_devs\\Python01\\gitdev> iveSession.close\n \n \n 1444177031517028\n e_layer': 128, '\n \n \n \n \n \n andas'} \n \n \n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n iveSession.close\n \n \n \n \n \n \n \n \n estep': 5, 'epoc\n \n ################################### ('Optim, finished',) ################################### \n \n ('### Run Model with best ################################################',) \n{'data_path': 'd:\\\\_devs\\\\python01\\\\gitdev\\\\mlmodels\\\\mlmodels\\\\dataset/GOOG-year_small.csv', 'data_type': 'pa \nndas'} \nd:\\_devs\\python01\\gitdev\\mlmodels\\mlmodels\\dataset/GOOG-year_small.csv \n Date Open High Low Close Adj Close Volume \n0 2016-11-02 778.200012 781.650024 763.450012 768.700012 768.700012 1872400 \n1 2016-11-03 767.250000 769.950012 759.030029 762.130005 762.130005 1943200 \n2 2016-11-04 750.659973 770.359985 750.560974 762.020020 762.020020 2134800 \n3 2016-11-07 774.500000 785.190002 772.549988 782.520020 782.520020 1585100 \n4 2016-11-08 783.400024 795.632996 780.190002 790.510010 790.510010 1350800 259812804941947\n 0 1 2 3 4 5 ayer': 256, 'ti\n0 0.360545 0.314383 0.361013 0.327543 0.327543 0.309058 \n1 0.243081 0.181353 0.316578 0.261572 0.261572 0.326086 tm.py'> \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0.372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0.239959 ndas'} \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0.183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1735: UserWarning: An \ninteractive session is already active. This can cause out-of-memory errors in some cases. You must explicitly \ncall `InteractiveSession.close()` to release resources held by the other session(s). \n warnings.warn('An interactive session is already active. This can ' \n \n ('#### Saving ###########################################################',) \n \n ('### Save Stats ##########################################################',) \n \n ############################## ('Finished OPTIMIZATION',) ############################## \n{'learning_rate': 0.007198981040978415, 'num_layers': 2, 'size': 6, 'output_size': 6, 'size_layer': 128, 'time \nstep': 5, 'epoch': 2, 'best_value': 0.14441770315170288} \n interactive ses\n(ztest) D:\\_devs\\Python01\\gitdev> veSession.close\n \n \n 444177031517028\n _layer': 128, '\n \n \n \n \n \n ndas'} \n \n \n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n er session(s). \n \n \n #########',) \n \n #########',) \n \n ################### \n utput_size': 6, 'size_layer': 128, 'time \n \n interactive ses\n veSession.close\n \n \n 444177031517028\n _layer': 128, '\n \n \n \n \n \n ndas'} \n \n \n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n510010 790.510010 1350800 interactive ses\n 0 1 2 3 4 veSession.close\n 5 \n0 0.360545 0.314383 0.361013 0.327543 0.327543 0. \n309058 444177031517028\n1 0.243081 0.181353 0.316578 0.261572 0.261572 0. _layer': 128, '\n326086 \n2 0.065114 0.186014 0.231437 0.260468 0.260468 0. \n372168 \n3 0.320854 0.354632 0.452498 0.466312 0.466312 0. \n239959 \n4 0.416327 0.473371 0.529305 0.546541 0.546541 0. ndas'} \n183607 \nD:\\_devs\\Python01\\ana3\\envs\\ztest\\lib\\site-packages\\ten \nsorflow\\python\\client\\session.py:1735: UserWarning: An \ninteractive session is already active. This can cause o \nut-of-memory errors in some cases. You must explicitly \ncall `InteractiveSession.close()` to release resources \nheld by the other session(s). \n warnings.warn('An interactive session is already acti \nve. This can ' \n \n ('#### Saving ################################### \n########################',) \n \n ('### Save Stats ################################## interactive ses\n########################',) veSession.close\n \n ############################## ('Finished OPTIMIZATION \n',) ############################## \n{'learning_rate': 0.007198981040978415, 'num_layers': 2 \n, 'size': 6, 'output_size': 6, 'size_layer': 128, 'time \nstep': 5, 'epoch': 2, 'best_value': 0.14441770315170288 \n} \n \n(ztest) D:\\_devs\\Python01\\gitdev> step': 5, 'epoc\n 1735: UserWarning: An \n ('#### Saving #############tive. This can cause o \n#################################. You must explicitly \n#############',) to release resources \n \n ('### Save Stats ############ession is already acti \n################################# \n#############',) \n ###################### \n ############################## ( \n'Finished OPTIMIZATION',) ####### \n####################### ###################### interactive ses\n{'learning_rate': 0.0071989810409 veSession.close\n78415, 'num_layers': 2, 'size': 6 \n, 'output_size': 6, 'size_layer':'Finished OPTIMIZATION \n 128, 'timestep': 5, 'epoch': 2, # \n'best_value': 0.1444177031517028878415, 'num_layers': 2 \n} ize_layer': 128, 'time \n ': 0.14441770315170288 \n(ztest) D:\\_devs\\Python01\\gitdev> \n \n step': 5, 'epoc\n Warning: An \n can cause o \n explicitly \n e resources \n \n already acti \n \n \n ############ \n \n \n ############ interactive ses\n veSession.close\n \n OPTIMIZATION \n \n m_layers': 2 \n : 128, 'time \n 770315170288 \n \n \n step': 5, 'epoc\n g: An \n ause o \n citly \n urces \n \n y acti \n \n \n ###### \n \n \n ###### interactive ses\n veSession.close\n \n ZATION \n \n rs': 2 \n 'time \n 170288 \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc\n \n \n \n \n \n \n \n \n \n \n \n interactive ses\n veSession.close\n \n \n \n \n \n \n \n \n step': 5, 'epoc",
"settings":
{
"buffer_size": 382048,
"line_ending": "Windows",
"name": "IPython: D:gitdev/mlmodels",
"scratch": true
}
},
{
"contents": "Microsoft Windows [Version 10.0.18362.30] \n(c) 2019 Microsoft Corporation. All rights reserved. \n \nD:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels>ipython \nPython 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] \nType 'copyright', 'credits' or 'license' for more information \nIPython 6.4.0 -- An enhanced Interactive Python. Type '?' for help. \n \nIn [1]: import glob \n ...: \n ...: files = glob.glob(my_path + '/**/*.py', recursive=True) \n--------------------------------------------------------------------------- \nNameError Traceback (most recent call last) \n<ipython-input-1-90f4162fc483> in <module>() \n 1 import glob \n 2 \n----> 3 files = glob.glob(my_path + '/**/*.py', recursive=True) \n \nNameError: name 'my_path' is not defined \n \nIn [2]: import glob \n ...: \n ...: path = \".\" \n ...: files = glob.glob(path + '/**/*.py', recursive=True) \n \nIn [3]: files \nOut[3]: \n['.\\\\data.py', \n '.\\\\distri_tch.py', \n '.\\\\models.py', \n '.\\\\optim.py', \n '.\\\\util.py', \n '.\\\\ztest.py', \n '.\\\\__init__.py', \n '.\\\\model_dev\\\\ml_mosaic.py', \n '.\\\\model_dev\\\\mytest.py', \n '.\\\\model_flow\\\\mlflow_run.py', \n '.\\\\model_keras\\\\0_modeltest.py', \n '.\\\\model_rank\\\\LambdaRank.py', \n '.\\\\model_rank\\\\load_mslr.py', \n '.\\\\model_rank\\\\metrics.py', \n '.\\\\model_rank\\\\RankNet.py', \n '.\\\\model_rank\\\\utils.py', \n '.\\\\model_rank\\\\__init__.py', \n '.\\\\model_rank\\\\irgan_tf\\\\item_recommendation\\\\cf_dns.py', \n '.\\\\model_rank\\\\irgan_tf\\\\item_recommendation\\\\cf_gan.py', \n '.\\\\model_rank\\\\irgan_tf\\\\item_recommendation\\\\dis_model.py', \n '.\\\\model_rank\\\\irgan_tf\\\\item_recommendation\\\\dis_model_dns.py', \n '.\\\\model_rank\\\\irgan_tf\\\\item_recommendation\\\\gen_model.py', \n '.\\\\model_rank\\\\irgan_tf\\\\item_recommendation\\\\utils.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\dis_model_pairwise_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\gen_model_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\ltr_gan_d_nn_g_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\run.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\utils.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\eval\\\\map.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\eval\\\\mrr.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\eval\\\\ndcg.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\eval\\\\precision.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pairwise\\\\eval\\\\__init__.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\dis_model_pairwise_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\dis_model_pointwise_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\gen_model_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\ltr_dns_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\ltr_gan_d_nn_g_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\ltr_mle_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\ltr_rns_nn.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\run.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\utils.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\eval\\\\map.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\eval\\\\mrr.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\eval\\\\ndcg.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\eval\\\\precision.py', \n '.\\\\model_rank\\\\irgan_tf\\\\ltr-gan\\\\ltr-gan-pointwise\\\\eval\\\\__init__.py', \n '.\\\\model_rank\\\\irgan_tf\\\\Question-Answer\\\\baseline.py', \n '.\\\\model_rank\\\\irgan_tf\\\\Question-Answer\\\\dataPrepare.py', \n '.\\\\model_rank\\\\irgan_tf\\\\Question-Answer\\\\Discriminator.py', \n '.\\\\model_rank\\\\irgan_tf\\\\Question-Answer\\\\Generator.py', \n '.\\\\model_rank\\\\irgan_tf\\\\Question-Answer\\\\insurance_qa_data_helpers.py', \n '.\\\\model_rank\\\\irgan_tf\\\\Question-Answer\\\\irgan.py', \n '.\\\\model_rank\\\\irgan_tf\\\\Question-Answer\\\\QACNN.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\Eval.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\ListMLE_conv2d.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\plot_ground_random_level_1.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\plot_ListMLE.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\plot_ndcg_log.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\plot_random_level.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\RankSVM.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\reinforce.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\reinforce_cartpole_example.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\utils.py', \n '.\\\\model_rank\\\\rl_ltr\\\\loss_vs_metric\\\\__init__.py', \n '.\\\\model_sklearn\\\\model.py', \n '.\\\\model_tch\\\\1_lstm.py', \n '.\\\\model_tch\\\\cnn_classifier.py', \n '.\\\\model_tch\\\\mlp.py', \n '.\\\\model_tch\\\\nbeats\\\\data.py', \n '.\\\\model_tch\\\\nbeats\\\\nbeats.py', \n '.\\\\model_tch\\\\nbeats\\\\nbeats_sample.py', \n '.\\\\model_tch\\\\nbeats\\\\trainer.py', \n '.\\\\model_tch\\\\nbeats\\\\__init__.py', \n '.\\\\model_tch\\\\nbeats\\\\n_beats\\\\model.py', \n '.\\\\model_tf\\\\10_encoder_vanilla.py', \n '.\\\\model_tf\\\\11_bidirectional_vanilla.py', \n '.\\\\model_tf\\\\12_vanilla_2path.py', \n '.\\\\model_tf\\\\13_lstm_seq2seq.py', \n '.\\\\model_tf\\\\14_lstm_attention.py', \n '.\\\\model_tf\\\\15_lstm_seq2seq_attention.py', \n '.\\\\model_tf\\\\16_lstm_seq2seq_bidirectional.py', \n '.\\\\model_tf\\\\17_lstm_seq2seq_bidirectional_attention.py', \n '.\\\\model_tf\\\\18_lstm_attention_scaleddot.py', \n '.\\\\model_tf\\\\19_lstm_dilated.py', \n '.\\\\model_tf\\\\1_lstm.py', \n '.\\\\model_tf\\\\20_only_attention.py', \n '.\\\\model_tf\\\\21_multihead_attention.py', \n '.\\\\model_tf\\\\22_lstm_bahdanau.py', \n '.\\\\model_tf\\\\23_lstm_luong.py', \n '.\\\\model_tf\\\\24_lstm_luong_bahdanau.py', \n '.\\\\model_tf\\\\25_dnc.py', \n '.\\\\model_tf\\\\26_lstm_residual.py', \n '.\\\\model_tf\\\\27_byte_net.py', \n '.\\\\model_tf\\\\28_attention_is_all_you_need.py', \n '.\\\\model_tf\\\\29_fairseq.py', \n '.\\\\model_tf\\\\2_encoder_lstm.py', \n '.\\\\model_tf\\\\3_bidirectional_lstm.py', \n '.\\\\model_tf\\\\4_lstm_2path.py', \n '.\\\\model_tf\\\\50lstm attention.py', \n '.\\\\model_tf\\\\5_gru.py', \n '.\\\\model_tf\\\\6_encoder_gru.py', \n '.\\\\model_tf\\\\7_bidirectional_gru.py', \n '.\\\\model_tf\\\\8_gru_2path.py', \n '.\\\\model_tf\\\\9_vanilla.py', \n '.\\\\model_tf\\\\access.py', \n '.\\\\model_tf\\\\addressing.py', \n '.\\\\model_tf\\\\autoencoder.py', \n '.\\\\model_tf\\\\dnc.py', \n '.\\\\model_tf\\\\util.py', \n '.\\\\model_tf\\\\__init__.py', \n '.\\\\model_tf\\\\deepar\\\\settings.py', \n '.\\\\model_tf\\\\deepar\\\\__init__.py', \n '.\\\\model_tf\\\\deepar\\\\dataset\\\\time_series.py', \n '.\\\\model_tf\\\\deepar\\\\dataset\\\\__init__.py', \n '.\\\\model_tf\\\\deepar\\\\model\\\\layers.py', \n '.\\\\model_tf\\\\deepar\\\\model\\\\loss.py', \n '.\\\\model_tf\\\\deepar\\\\model\\\\lstm.py', \n '.\\\\model_tf\\\\deepar\\\\model\\\\__init__.py', \n '.\\\\model_tf\\\\deepar\\\\tests\\\\test_dataset.py', \n '.\\\\model_tf\\\\deepar\\\\utils\\\\__init__.py', \n '.\\\\model_tf\\\\rl\\\\0_template_rl.py', \n '.\\\\model_tf\\\\rl\\\\1.turtle-agent.py', \n '.\\\\model_tf\\\\rl\\\\10.duel-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\11.double-duel-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\12.duel-recurrent-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\13.double-duel-recurrent-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\14.actor-critic-agent.py', \n '.\\\\model_tf\\\\rl\\\\15.actor-critic-duel-agent.py', \n '.\\\\model_tf\\\\rl\\\\16.actor-critic-recurrent-agent.py', \n '.\\\\model_tf\\\\rl\\\\17.actor-critic-duel-recurrent-agent.py', \n '.\\\\model_tf\\\\rl\\\\18.curiosity-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\19.recurrent-curiosity-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\2.moving-average-agent.py', \n '.\\\\model_tf\\\\rl\\\\20.duel-curiosity-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\21.neuro-evolution-agent.py', \n '.\\\\model_tf\\\\rl\\\\22.neuro-evolution-novelty-search-agent.py', \n '.\\\\model_tf\\\\rl\\\\3.signal-rolling-agent.py', \n '.\\\\model_tf\\\\rl\\\\4.policy-gradient-agent_old.py', \n '.\\\\model_tf\\\\rl\\\\4_policy-gradient-agent.py', \n '.\\\\model_tf\\\\rl\\\\5_q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\6_evolution-strategy-agent.py', \n '.\\\\model_tf\\\\rl\\\\7.double-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\8.recurrent-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\9.double-recurrent-q-learning-agent.py', \n '.\\\\model_tf\\\\rl\\\\updated-NES-google.py', \n '.\\\\model_tf\\\\rl\\\\__init__.py', \n '.\\\\template\\\\model_tf_sequential.py', \n '.\\\\zdocs\\\\zcode\\\\adress_na_handling.py', \n '.\\\\zdocs\\\\zcode\\\\api_design.py', \n '.\\\\zdocs\\\\zcode\\\\cli_predict.py'] \n \nIn [4]: path = \".\" \n ...: files = glob.glob(path + '*model_*.py', recursive=True) \n ...: files \nOut[4]: [] \n \nIn [5]: import glob \n ...: \n ...: path = \".\" \n ...: files = glob.glob(path + '*model*.py', recursive=True) \n ...: files \nOut[5]: [] \n \nIn [6]: import glob \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n '.\\\\model_rank\\\\__init__.py', \n '.\\\\model_sklearn\\\\model.py', \n '.\\\\model_tch\\\\1_lstm.py', \n '.\\\\model_tch\\\\cnn_classifier.py', \n '.\\\\model_tch\\\\mlp.py', \n '.\\\\model_tf\\\\10_encoder_vanilla.py', \n '.\\\\model_tf\\\\11_bidirectional_vanilla.py', \n '.\\\\model_tf\\\\12_vanilla_2path.py', \n '.\\\\model_tf\\\\13_lstm_seq2seq.py', \n '.\\\\model_tf\\\\14_lstm_attention.py', \n '.\\\\model_tf\\\\15_lstm_seq2seq_attention.py', \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n '.\\\\model_rank\\\\__init__.py', \n '.\\\\model_sklearn\\\\model.py', \n '.\\\\model_tch\\\\1_lstm.py', \n '.\\\\model_tch\\\\cnn_classifier.py', \n '.\\\\model_tch\\\\mlp.py', \n '.\\\\model_tf\\\\10_encoder_vanilla.py', \n '.\\\\model_tf\\\\11_bidirectional_vanilla.py', \n '.\\\\model_tf\\\\12_vanilla_2path.py', \n '.\\\\model_tf\\\\13_lstm_seq2seq.py', \n '.\\\\model_tf\\\\14_lstm_attention.py', \n '.\\\\model_tf\\\\15_lstm_seq2seq_attention.py', \n '.\\\\model_tf\\\\16_lstm_seq2seq_bidirectional.py', \n '.\\\\model_tf\\\\17_lstm_seq2seq_bidirectional_attention.py', \n '.\\\\model_tf\\\\18_lstm_attention_scaleddot.py', \n '.\\\\model_tf\\\\19_lstm_dilated.py', \n '.\\\\model_tf\\\\1_lstm.py', \n '.\\\\model_tf\\\\20_only_attention.py', \n '.\\\\model_tf\\\\21_multihead_attention.py', \n '.\\\\model_tf\\\\22_lstm_bahdanau.py', \n '.\\\\model_tf\\\\23_lstm_luong.py', \n '.\\\\model_tf\\\\24_lstm_luong_bahdanau.py', \n '.\\\\model_tf\\\\25_dnc.py', \n \n \n py', \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n '.\\\\model_tf\\\n\\16_lstm_seq2seq_bidirectional.py', \n '.\\\\model_tf\\\\17_lstm_seq2seq_bidirectional\n_attention.py', \n '.\\\\model_tf\\\\18_lstm_attention_scaleddot.p\ny', \n '.\\\\model_tf\\\\19_lstm_dilated.py', \n '.\\\\model_tf\\\\1_lstm.py', \n '.\\\\model_tf\\\\20_only_attention.py', \n '.\\\\model_tf\\\\21_multihead_attention.py', \n '.\\\\model_tf\\\\22_lstm_bahdanau.py', \n '.\\\\model_tf\\\\23_lstm_luong.py', \n '.\\\\model_tf\\\\24_lstm_luong_bahdanau.py', \n '.\\\\model_tf\\\\25_dnc.py', \n '.\\\\model_tf\\\\26_lstm_residual.py', \n '.\\\\model_tf\\\\27_byte_net.py', \n '.\\\\model_tf\\\\28_attention_is_all_you_need.\npy', \n '.\\\\model_tf\\\\29_fairseq.py', \n '.\\\\model_tf\\\\2_encoder_lstm.py', \n '.\\\\model_tf\\\\3_bidirectional_lstm.py', \n '.\\\\model_tf\\\\4_lstm_2path.py', \n '.\\\\model_tf\\\\50lstm attention.py', \n '.\\\\model_tf\\\\5_gru.py', \n '.\\\\model_tf\\\\6_encoder_gru.py', \n '.\\\\model_tf\\\\7_bidirectional_gru.py', \n '.\\\\model_tf\\\\8_gru_2path.py', \n '.\\\\model_tf\\\\9_vanilla.py', \n '.\\\\model_tf\\\\access.py', \n '.\\\\model_tf\\\\addressing.py', \n '.\\\\model_tf\\\\autoencoder.py', \n '.\\\\model_tf\\\\dnc.py', \n '.\\\\model_tf\\\\util.py', \n '.\\\\model_tf\\\\__init__.py'] \n \nIn [8]: \n \nIn [8]: \n \nIn [8]: \n '.\\\\model_tf\\\\3_bidirectional_lstm.py \n', \n '.\\\\model_tf\\\\4_lstm_2path.py', ', \n '.\\\\model_tf\\\\50lstm attention.py', \n '.\\\\model_tf\\\\5_gru.py', \n '.\\\\model_tf\\\\6_encoder_gru.py', \n '.\\\\model_tf\\\\7_bidirectional_gru.py' \n, , \n '.\\\\model_tf\\\\8_gru_2path.py', \n '.\\\\model_tf\\\\9_vanilla.py', \n '.\\\\model_tf\\\\access.py', \n '.\\\\model_tf\\\\addressing.py', \n '.\\\\model_tf\\\\autoencoder.py', \n '.\\\\model_tf\\\\dnc.py', \n '.\\\\model_tf\\\\util.py', \n '.\\\\model_tf\\\\__init__.py'] \n \nIn [8]: \n \nIn [8]: \n \nIn [8]: \nIn [8]: \n In [8]: \n",
"settings":
{
"buffer_size": 51245,
"line_ending": "Windows",
"name": "IPython: D:mlmodels/mlmodels",
"scratch": true
}
}
],
"build_system": "",
"build_system_choices":
[
],
"build_varint": "",
"command_palette":
{
"height": 0.0,
"last_filter": "",
"selected_items":
[
[
"repl",
"SublimeREPL: Python"
],
[
"send",
"SendCode: Choose Program"
],
[
"Package Control: ",
"Package Control: Install Package"
],
[
"Package Control:dis",
"Package Control: Disable Package"
]
],
"width": 0.0
},
"console":
{
"height": 414.0,
"history":
[
"import urllib.request,os,hashlib; h = '6f4c264a24d933ce70df5dedcf1dcaee' + 'ebe013ee18cced0ef93d5f746d80ef60'; pf = 'Package Control.sublime-package'; ipp = sublime.installed_packages_path(); urllib.request.install_opener( urllib.request.build_opener( urllib.request.ProxyHandler()) ); by = urllib.request.urlopen( 'http://packagecontrol.io/' + pf.replace(' ', '%20')).read(); dh = hashlib.sha256(by).hexdigest(); print('Error validating download (got %s instead of %s), please try manual install' % (dh, h)) if dh != h else open(os.path.join( ipp, pf), 'wb' ).write(by)"
]
},
"distraction_free":
{
"menu_visible": true,
"show_minimap": false,
"show_open_files": false,
"show_tabs": false,
"side_bar_visible": false,
"status_bar_visible": false
},
"expanded_folders":
[
"/D/Dropbox/__research_paper"
],
"file_history":
[
"/D/Dropbox/__research_paper/a0_share_publication/JSAI_2020_march/jsaiac_tex_en-2020-20191025/jsaiac_tex_en-2020/guide-2e_en.tex",
"/D/_devs/Python01/gitdev/mlmodels/requirements.txt",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/models_config.json",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/1_lstm.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/util.py",
"/D/_devs/Python01/gitdev/cli_code/pypi.py",
"/C/Users/dellbook/AppData/Roaming/Sublime Text 3/Packages/tgit-st3/tgit-st3.sublime-settings",
"/C/Users/dellbook/AppData/Roaming/Sublime Text 3/Packages/SublimeREPL/SublimeREPL.sublime-settings",
"/C/Users/dellbook/AppData/Roaming/Sublime Text 3/Packages/User/SublimeREPL.sublime-settings",
"/D/_devs/Python01/gitdev/mlmodels/setup.py",
"/C/Users/dellbook/AppData/Roaming/Sublime Text 3/Packages/User/All Autocomplete.sublime-settings",
"/C/Users/dellbook/AppData/Roaming/Sublime Text 3/Packages/All Autocomplete/All Autocomplete.sublime-settings",
"/D/_devs/Python01/gitdev/mlmodels/pypi.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/3_bidirectional_lstm.py",
"/D/_devs/Python01/gitdev/ztest.py",
"/D/_devs/Python01/gitdev/__generator/models/Beta-VAE/README.md",
"/D/_devs/Python01/gitdev/__generator/models/Beta-VAE/dataset.py",
"/D/_devs/Python01/gitdev/__generator/models/util.py",
"/D/_devs/Python01/gitdev/_rl_hoken/util.py",
"/D/Dropbox/aJOB/__point72/docs.txt",
"/D/_devs/Python01/gitdev/cli_code/cli_code/clI_build_check_repo2.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/cli_git.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/cli_docs.py",
"/C/Users/dellbook/AppData/Roaming/Sublime Text 3/Packages/User/Package Control.sublime-settings",
"/D/_devs/Python01/gitdev/cli_code/cli_code/ztmp/doc_os.txt",
"/D/_devs/Python01/gitdev/cli_code/cli_code/ztmp/doc_json.txt",
"/D/_devs/Python01/gitdev/cli_code/cli_code/ztmp/list_json.csv",
"/D/_devs/Python01/gitdev/cli_code/cli_code/ztmp/doc_numpy.txt",
"/D/_devs/Python01/gitdev/cli_code/cli_code/ztmp/zz_unitest_run_json.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/zz_unitest_run_numpy.txt",
"/D/_devs/Python01/gitdev/cli_code/cli_code/zz_unitest_run_os2.txt",
"/D/_devs/Python01/gitdev/cli_code/cli_code/zz_unitest_run_os.txt",
"/D/_devs/Python01/gitdev/cli_code/cli_code/zz_unitest_run_json.txt",
"/D/Downloads/cli_docs.py",
"/D/_devs/Python01/gitdev/autoscale_aws/src/autoscale_aws/batch_daemon_autoscale_cli.py",
"/D/_devs/Python01/gitdev/_generator_private/models/cli.py",
"/D/_devs/Python01/gitdev/_generator_private/models/util.py",
"/D/_devs/Python01/gitdev/_generator_private/models/New File.txt",
"/D/_devs/Python01/gitdev/_generator_private/models/Beta_VAE/dataset.py",
"/D/_devs/Python01/gitdev/_generator_private/models/Beta_VAE/main.py",
"/D/_devs/Python01/gitdev/_generator_private/models/Beta_VAE/utils.py",
"/D/_devs/Python01/gitdev/_generator_private/README.md",
"/D/_devs/Python01/gitdev/_generator_private/models/adversarial-on-disentangled/run_factorvae.py",
"/D/_devs/Python01/gitdev/_generator_private/models/beta-tcvae/disentanglement_metrics.py",
"/D/_devs/Python01/gitdev/_generator_private/models/beta-tcvae/plot_latent_vs_true.py",
"/D/_devs/Python01/gitdev/_generator_private/models/beta-tcvae/README.md",
"/D/_devs/Python01/gitdev/_generator_private/models/beta-vaegan/README.md",
"/D/_devs/Python01/gitdev/_generator_private/models/script.py",
"/D/_devs/Python01/gitdev/_rl_hoken/script (1).py",
"/D/_devs/Python01/gitdev/_rl_hoken/script.py",
"/D/_devs/Python01/gitdev/__generator/models/Beta-VAE/run_1sin_H_beta4_z10.sh",
"/C/Users/zenbook/AppData/Roaming/Sublime Text 3/Packages/User/Anaconda.sublime-settings",
"/D/_devs/Python01/gitdev/__generator/models/Beta-VAE/main.py",
"/D/_devs/Python01/gitdev/__generator/models/Beta-VAE/solver.py",
"/D/_devs/Python01/gitdev/__generator/models/cli.py",
"/D/_devs/Python01/gitdev/__generator/.gitignore",
"/D/_devs/Python01/gitdev/__generator/models/script.py",
"/D/_devs/Python01/gitdev/control/main.py",
"/D/_devs/Python01/gitdev/control/solver.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tch/cnn_classifier.py",
"/D/_devs/Python01/gitdev/control/submodels.py",
"/D/_devs/Python01/gitdev/mlmodels/zconda/distri_model_tch.py",
"/D/tset.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/_old.py",
"/D/_devs/Python01/gitdev/__generator/models/disentangling-vae/main_viz.py",
"/D/_devs/Python01/gitdev/__generator/models/Beta-VAE/run_3dchairs_H_beta4_z10.sh",
"/D/_devs/Python01/gitdev/cli_code/cli_code/cli_repo_check.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tch/1_lstm.py",
"/D/_devs/Python01/gitdev/mlmodels/build/lib/mlmodels/models.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/util.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/util_log.py",
"/D/_devs/Python01/gitdev/rl_hoken/script.py",
"/D/_devs/Python01/gitdev/rl_hoken/script (1).py",
"/D/app/xplorer2/changes.txt",
"/D/app/xplorer2/licence.txt",
"/D/_devs/Python01/ana3/envs/tf14b/Scripts/mlmodels_cli.py",
"/D/_devs/Python01/gitdev/__generator/models/beta-vaegan/models.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/ztest/search_save/model_tf-1_lstm-py_best-params.json",
"/D/_devs/Python01/ana3/envs/tf14b/Scripts/models.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/9_vanilla.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/8_gru_2path.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/4_lstm_2path.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/2_encoder_lstm.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/29_fairseq.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/28_attention_is_all_you_need.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/27_byte_net.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/26_lstm_residual.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/25_dnc.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/24_lstm_luong_bahdanau.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/23_lstm_luong.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/22_lstm_bahdanau.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/21_multihead_attention.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/20_only_attention.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/19_lstm_dilated.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/18_lstm_attention_scaleddot.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/17_lstm_seq2seq_bidirectional_attention.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/16_lstm_seq2seq_bidirectional.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/15_lstm_seq2seq_attention.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/14_lstm_attention.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/13_lstm_seq2seq.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/12_vanilla_2path.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/11_bidirectional_vanilla.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/10_encoder_vanilla.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/5_gru.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/6_encoder_gru.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/model_tf/7_bidirectional_gru.py",
"/D/_devs/Python01/gitdev/mlmodels/mlmodels/models.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/cli_module_generate_docs.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/cli_github_search.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/util_filesearch.py",
"/D/_devs/Python01/gitdev/cli_code/cli_code/cli_module_autoinstall.py",
"/D/_devs/Python01/gitdev/_generator/models/disentangling-vae/README.md",
"/D/_devs/Python01/gitdev/_generator/models/chainer-disentanglement/requirements-cpu.txt",
"/D/_devs/Python01/gitdev/_generator/models/disentangling-vae/requirements.txt",
"/D/_devs/Python01/gitdev/_generator/models/chainer-disentanglement/net/betavae.py",
"/C/Users/zenbook/AppData/Roaming/Sublime Text 3/Packages/Anaconda/Anaconda.sublime-settings",
"/D/Dropbox/___interview/infos.txt",
"/D/Dropbox/___interview/Iterview_questions.txt",
"/D/Dropbox/___interview/nlp_related_topics.txt",
"/D/_devs/Python01/gitdev/json_api/mapper_keras.py",
"/D/_devs/Python01/gitdev/control/solver_new.py",
"/D/_devs/Python01/gitdev/_generator/models/Beta-VAE/solver.py",
"/C/tmp/scoped_dir12596_31728/ListaryTutorial/TextFile2.txt",
"/Y/tasks/t_github_mytask02/util_task.py",
"/E/My Drive/___from_windows/New File.txt",
"/Z/___interview/Iterview_questions.txt",
"/D/_devs/Python01/gitdev/_generator/models/wgan/constants.py",
"/D/_devs/Python01/gitdev/_generator/models/wgan/__init__.py"
],
"find":
{
"height": 36.0
},
"find_in_files":
{
"height": 619.0,
"where_history":
[
"D:\\_devs\\Python01\\gitdev\\mlmodels"
]
},
"find_state":
{
"case_sensitive": false,
"find_history":
[
"if arg.do == ",
"file_out",
"pprint",
"import_mo",
"gather",
"DataGather",
"parser.add_argument(",
"parser.add_argument",
"os",
"create(",
"self._conf",
"self._config",
"my_parser",
"need_to_install_package_set",
"args"
],
"highlight": true,
"in_selection": false,
"preserve_case": false,
"regex": false,
"replace_history":
[
"",
"outputfile",
"log",
"to(",
"to",
"create_model(",
"self._c",
"self._conf",
"p",
"package_list",
"arg"
],
"reverse": false,
"show_context": true,
"use_buffer2": true,
"whole_word": false,
"wrap": true
},
"groups":
[
{
"selected": 1,
"sheets":
[
{
"buffer": 0,
"file": "README.md",
"semi_transient": false,
"settings":
{
"buffer_size": 8355,
"regions":
{
},
"selection":
[
[
298,
298
],
[
795,
795
]
],
"settings":
{
"syntax": "Packages/Markdown/Markdown.sublime-syntax",
"tab_size": 2,
"translate_tabs_to_spaces": true
},
"translation.x": 0.0,
"translation.y": 0.0,
"zoom_level": 1.0
},
"stack_index": 1,
"type": "text"
},
{
"buffer": 1,
"file": "/D/Dropbox/__research_paper/a0_share_publication/JSAI_2020_march/jsaiac_tex_en-2020-20191025/jsaiac_tex_en-2020/guide-209_en.tex",
"semi_transient": false,
"settings":
{
"buffer_size": 75,
"regions":
{
},
"selection":
[
[
0,
0
]
],
"settings":
{
"syntax": "Packages/LaTeX/LaTeX.sublime-syntax"
},
"translation.x": 0.0,
"translation.y": 0.0,
"zoom_level": 1.0
},
"stack_index": 0,
"type": "text"
},
{
"buffer": 2,
"file": "requirements.txt",
"semi_transient": true,
"settings":
{
"buffer_size": 156,
"regions":
{
},
"selection":
[
[
156,
0
]
],
"settings":
{
"syntax": "Packages/Text/Plain text.tmLanguage"
},
"translation.x": 0.0,
"translation.y": 0.0,
"zoom_level": 1.0
},
"stack_index": 2,
"type": "text"
},
{
"buffer": 3,
"file": "mlmodels/model_keras/01_deepctr.py",
"semi_transient": false,
"settings":
{
"buffer_size": 7433,
"regions":
{
},
"selection":
[
[
5478,
5478
]
],
"settings":
{
"syntax": "Packages/Python/Python.sublime-syntax",
"tab_size": 4,
"translate_tabs_to_spaces": true
},
"translation.x": 0.0,
"translation.y": 360.0,
"zoom_level": 1.0
},
"stack_index": 3,
"type": "text"
},
{
"buffer": 4,
"file": "mlmodels/optim.py",
"semi_transient": false,
"settings":
{
"buffer_size": 13557,
"regions":
{
},
"selection":
[
[
1623,
1623
]
],
"settings":
{
"syntax": "Packages/Python/Python.sublime-syntax",
"tab_size": 4,
"translate_tabs_to_spaces": true
},
"translation.x": 0.0,
"translation.y": 180.0,
"zoom_level": 1.0
},
"stack_index": 10,
"type": "text"
},
{
"buffer": 5,
"file": "setup.py",
"semi_transient": false,
"settings":
{
"buffer_size": 9320,
"regions":
{
},
"selection":
[
[
1081,
1081
]
],
"settings":
{
"syntax": "Packages/Python/Python.sublime-syntax",
"tab_size": 2,
"translate_tabs_to_spaces": true
},
"translation.x": 0.0,
"translation.y": 360.0,
"zoom_level": 1.0
},
"stack_index": 8,
"type": "text"
},
{
"buffer": 6,
"file": "mlmodels/models.py",
"semi_transient": false,
"settings":
{
"buffer_size": 13457,
"regions":
{
},
"selection":
[
[
6065,
6065
]
],
"settings":
{
"syntax": "Packages/Python/Python.sublime-syntax",
"tab_size": 2,
"translate_tabs_to_spaces": true
},
"translation.x": 0.0,
"translation.y": 3509.0,
"zoom_level": 1.0
},
"stack_index": 7,
"type": "text"
},
{
"buffer": 7,
"file": "mlmodels/util.py",
"semi_transient": false,
"settings":
{
"buffer_size": 10505,
"regions":
{
},
"selection":
[
[
1834,
1834
]
],
"settings":
{
"syntax": "Packages/Python/Python.sublime-syntax",
"tab_size": 2,
"translate_tabs_to_spaces": true
},
"translation.x": 0.0,
"translation.y": 720.0,
"zoom_level": 1.0
},
"stack_index": 9,
"type": "text"
}
]
},
{
"selected": 1,
"sheets":
[
{
"buffer": 8,
"semi_transient": false,
"settings":
{
"buffer_size": 495436,
"regions":
{
},
"selection":
[
[
495436,
495436
]
],
"settings":
{
"__vi_external_disable": true,
"auto_complete": false,
"auto_complete_commit_on_tab": false,
"bracket_highlighter.clone_locations":
{
},
"bracket_highlighter.ignore": true,
"color_scheme": "Terminus.sublime-color-scheme",
"default_dir": "D:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels",
"draw_centered": false,
"draw_indent_guides": false,
"draw_unicode_white_space": false,
"draw_white_space": "none",
"gutter": false,
"highlight_line": false,
"is_widget": true,
"scroll_past_end": true,
"syntax": "Packages/Text/Plain text.tmLanguage",
"terminus.highlight_counter": 1,
"terminus_view": true,
"terminus_view.args":
{
"auto_close": true,
"cancellable": false,
"cmd":
[
"cmd.exe"
],
"config_name": "Command Prompt",
"cwd": "D:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels",
"env":
{
"TERMINUS_SUBLIME": "1",
"TERM_PROGRAM": "Terminus-Sublime"
},
"panel_name": null,
"tag": null,
"timeit": false,
"title": null
},
"terminus_view.cancellable": false,
"terminus_view.key.ctrl+k": true,
"terminus_view.key.ctrl+p": true,
"terminus_view.natural_keyboard": true,
"terminus_view.panel_name": null,
"terminus_view.tag": null,
"terminus_view.viewport_y": 123174.0,
"word_wrap": false
},
"translation.x": 0.0,
"translation.y": 123174.0,
"zoom_level": 1.0
},
"stack_index": 6,
"type": "text"
},
{
"buffer": 9,
"semi_transient": false,
"settings":
{
"buffer_size": 382048,
"regions":
{
},
"selection":
[
[
379277,
379277
]
],
"settings":
{
"__vi_external_disable": true,
"auto_complete": false,
"auto_complete_commit_on_tab": false,
"bracket_highlighter.clone_locations":
{
},
"bracket_highlighter.ignore": true,
"color_scheme": "Terminus.sublime-color-scheme",
"default_dir": "D:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels",
"draw_centered": false,
"draw_indent_guides": false,
"draw_unicode_white_space": false,
"draw_white_space": "none",
"gutter": false,
"highlight_line": false,
"is_widget": true,
"scroll_past_end": true,
"syntax": "Packages/Text/Plain text.tmLanguage",
"tab_size": 2,
"terminus.highlight_counter": 48,
"terminus_view": true,
"terminus_view.args":
{
"auto_close": true,
"cancellable": false,
"cmd":
[
"cmd.exe"
],
"config_name": "Command Prompt",
"cwd": "D:\\_devs\\Python01\\gitdev\\mlmodels",
"env":
{
"TERMINUS_SUBLIME": "1",
"TERM_PROGRAM": "Terminus-Sublime"
},
"panel_name": null,
"tag": null,
"timeit": false,
"title": null
},
"terminus_view.cancellable": false,
"terminus_view.key.ctrl+k": true,
"terminus_view.key.ctrl+p": true,
"terminus_view.natural_keyboard": true,
"terminus_view.panel_name": null,
"terminus_view.tag": null,
"terminus_view.viewport_y": 85941.0,
"translate_tabs_to_spaces": true,
"word_wrap": false
},
"translation.x": 0.0,
"translation.y": 85941.0,
"zoom_level": 1.0
},
"stack_index": 4,
"type": "text"
},
{
"buffer": 10,
"semi_transient": false,
"settings":
{
"buffer_size": 51245,
"regions":
{
},
"selection":
[
[
51245,
51245
]
],
"settings":
{
"__vi_external_disable": true,
"auto_complete": false,
"auto_complete_commit_on_tab": false,
"bracket_highlighter.clone_locations":
{
},
"bracket_highlighter.ignore": true,
"color_scheme": "Terminus.sublime-color-scheme",
"default_dir": "D:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels",
"draw_centered": false,
"draw_indent_guides": false,
"draw_unicode_white_space": false,
"draw_white_space": "none",
"gutter": false,
"highlight_line": false,
"is_widget": true,
"scroll_past_end": true,
"syntax": "Packages/Text/Plain text.tmLanguage",
"terminus.highlight_counter": 842,
"terminus_view": true,
"terminus_view.args":
{
"auto_close": true,
"cancellable": false,
"cmd":
[
"cmd.exe"
],
"config_name": "Command Prompt",
"cwd": "D:\\_devs\\Python01\\gitdev\\mlmodels\\mlmodels",
"env":
{
"TERMINUS_SUBLIME": "1",
"TERM_PROGRAM": "Terminus-Sublime"
},
"panel_name": null,
"tag": null,
"timeit": false,
"title": null
},
"terminus_view.cancellable": false,
"terminus_view.key.ctrl+k": true,
"terminus_view.key.ctrl+p": true,
"terminus_view.natural_keyboard": true,
"terminus_view.panel_name": null,
"terminus_view.tag": null,
"terminus_view.viewport_y": 17307.0,
"word_wrap": false
},
"translation.x": 0.0,
"translation.y": 17307.0,
"zoom_level": 1.0
},
"stack_index": 5,
"type": "text"
}
]
}
],
"incremental_find":
{
"height": 36.0
},
"input":
{
"height": 209.0
},
"layout":
{
"cells":
[
[
0,
0,
1,
1
],
[
1,
0,
2,
1
]
],
"cols":
[
0.0,
0.532849263868,
1.0
],
"rows":
[
0.0,
1.0
]
},
"menu_visible": true,
"output.SublimeLinter":
{
"height": 0.0
},
"output.find_results":
{
"height": 0.0
},
"pinned_build_system": "",
"project": "zsublime.sublime-project",
"replace":
{
"height": 68.0
},
"save_all_on_build": true,
"select_file":
{
"height": 0.0,
"last_filter": "",
"selected_items":
[
],
"width": 0.0
},
"select_project":
{
"height": 500.0,
"last_filter": "",
"selected_items":
[
],
"width": 380.0
},
"select_symbol":
{
"height": 0.0,
"last_filter": "",
"selected_items":
[
],
"width": 0.0
},