Automated tutorials push

pytorch · Jan 22, 2025 · 7abbd83 · 7abbd83
1 parent e4aef6b
commit 7abbd83
Show file tree

Hide file tree

Showing 186 changed files with 12,971 additions and 12,687 deletions.
diff --git a/_images/sphx_glr_char_rnn_classification_tutorial_001.png b/_images/sphx_glr_char_rnn_classification_tutorial_001.png
diff --git a/_images/sphx_glr_char_rnn_classification_tutorial_002.png b/_images/sphx_glr_char_rnn_classification_tutorial_002.png
diff --git a/_images/sphx_glr_coding_ddpg_001.png b/_images/sphx_glr_coding_ddpg_001.png
diff --git a/_images/sphx_glr_dqn_with_rnn_tutorial_001.png b/_images/sphx_glr_dqn_with_rnn_tutorial_001.png
diff --git a/_images/sphx_glr_neural_style_tutorial_004.png b/_images/sphx_glr_neural_style_tutorial_004.png
diff --git a/_images/sphx_glr_pinmem_nonblock_001.png b/_images/sphx_glr_pinmem_nonblock_001.png
diff --git a/_images/sphx_glr_pinmem_nonblock_002.png b/_images/sphx_glr_pinmem_nonblock_002.png
diff --git a/_images/sphx_glr_pinmem_nonblock_003.png b/_images/sphx_glr_pinmem_nonblock_003.png
diff --git a/_images/sphx_glr_pinmem_nonblock_004.png b/_images/sphx_glr_pinmem_nonblock_004.png
diff --git a/_images/sphx_glr_reinforcement_ppo_001.png b/_images/sphx_glr_reinforcement_ppo_001.png
diff --git a/_images/sphx_glr_reinforcement_q_learning_001.png b/_images/sphx_glr_reinforcement_q_learning_001.png
diff --git a/_images/sphx_glr_semi_structured_sparse_001.png b/_images/sphx_glr_semi_structured_sparse_001.png
diff --git a/_images/sphx_glr_semi_structured_sparse_002.png b/_images/sphx_glr_semi_structured_sparse_002.png
diff --git a/_images/sphx_glr_spatial_transformer_tutorial_001.png b/_images/sphx_glr_spatial_transformer_tutorial_001.png
diff --git a/_images/sphx_glr_torchvision_tutorial_002.png b/_images/sphx_glr_torchvision_tutorial_002.png
diff --git a/_sources/advanced/coding_ddpg.rst.txt b/_sources/advanced/coding_ddpg.rst.txt
@@ -1634,26 +1634,26 @@ modules we need.
 
 
       0%|          | 0/10000 [00:00<?, ?it/s]
-      8%|8         | 800/10000 [00:00<00:05, 1649.92it/s]
-     16%|#6        | 1600/10000 [00:02<00:17, 481.22it/s]
-     24%|##4       | 2400/10000 [00:03<00:11, 679.82it/s]
-     32%|###2      | 3200/10000 [00:04<00:07, 854.25it/s]
-     40%|####      | 4000/10000 [00:04<00:06, 991.60it/s]
-     48%|####8     | 4800/10000 [00:05<00:04, 1103.97it/s]
-     56%|#####6    | 5600/10000 [00:05<00:03, 1186.81it/s]
-    reward: -2.36 (r0 = -2.46), reward eval: reward: -0.00, reward normalized=-2.17/6.02, grad norm= 70.85, loss_value= 259.91, loss_actor= 13.11, target value: -12.55:  56%|#####6    | 5600/10000 [00:06<00:03, 1186.81it/s]
-    reward: -2.36 (r0 = -2.46), reward eval: reward: -0.00, reward normalized=-2.17/6.02, grad norm= 70.85, loss_value= 259.91, loss_actor= 13.11, target value: -12.55:  64%|######4   | 6400/10000 [00:07<00:04, 828.88it/s] 
-    reward: -0.11 (r0 = -2.46), reward eval: reward: -0.00, reward normalized=-2.21/5.41, grad norm= 48.15, loss_value= 220.38, loss_actor= 13.06, target value: -14.67:  64%|######4   | 6400/10000 [00:08<00:04, 828.88it/s]
-    reward: -0.11 (r0 = -2.46), reward eval: reward: -0.00, reward normalized=-2.21/5.41, grad norm= 48.15, loss_value= 220.38, loss_actor= 13.06, target value: -14.67:  72%|#######2  | 7200/10000 [00:09<00:04, 680.34it/s]
-    reward: -2.34 (r0 = -2.46), reward eval: reward: -0.00, reward normalized=-2.42/5.53, grad norm= 124.03, loss_value= 233.88, loss_actor= 14.89, target value: -15.42:  72%|#######2  | 7200/10000 [00:09<00:04, 680.34it/s]
-    reward: -2.34 (r0 = -2.46), reward eval: reward: -0.00, reward normalized=-2.42/5.53, grad norm= 124.03, loss_value= 233.88, loss_actor= 14.89, target value: -15.42:  80%|########  | 8000/10000 [00:10<00:03, 600.90it/s]
-    reward: -4.42 (r0 = -2.46), reward eval: reward: -0.00, reward normalized=-2.45/4.73, grad norm= 72.59, loss_value= 179.06, loss_actor= 14.14, target value: -16.28:  80%|########  | 8000/10000 [00:11<00:03, 600.90it/s] 
-    reward: -4.42 (r0 = -2.46), reward eval: reward: -0.00, reward normalized=-2.45/4.73, grad norm= 72.59, loss_value= 179.06, loss_actor= 14.14, target value: -16.28:  88%|########8 | 8800/10000 [00:12<00:02, 560.50it/s]
-    reward: -4.65 (r0 = -2.46), reward eval: reward: -4.50, reward normalized=-2.63/5.20, grad norm= 170.41, loss_value= 308.90, loss_actor= 11.66, target value: -17.58:  88%|########8 | 8800/10000 [00:15<00:02, 560.50it/s]
-    reward: -4.65 (r0 = -2.46), reward eval: reward: -4.50, reward normalized=-2.63/5.20, grad norm= 170.41, loss_value= 308.90, loss_actor= 11.66, target value: -17.58:  96%|#########6| 9600/10000 [00:15<00:01, 391.02it/s]
-    reward: -3.49 (r0 = -2.46), reward eval: reward: -4.50, reward normalized=-2.54/4.64, grad norm= 94.25, loss_value= 200.86, loss_actor= 13.08, target value: -17.75:  96%|#########6| 9600/10000 [00:16<00:01, 391.02it/s] 
-    reward: -3.49 (r0 = -2.46), reward eval: reward: -4.50, reward normalized=-2.54/4.64, grad norm= 94.25, loss_value= 200.86, loss_actor= 13.08, target value: -17.75: : 10400it [00:18, 355.20it/s]                        
-    reward: -3.71 (r0 = -2.46), reward eval: reward: -4.50, reward normalized=-2.84/4.12, grad norm= 143.58, loss_value= 128.55, loss_actor= 17.95, target value: -20.42: : 10400it [00:19, 355.20it/s]
+      8%|8         | 800/10000 [00:00<00:05, 1648.36it/s]
+     16%|#6        | 1600/10000 [00:02<00:17, 486.77it/s]
+     24%|##4       | 2400/10000 [00:03<00:10, 691.82it/s]
+     32%|###2      | 3200/10000 [00:04<00:07, 881.23it/s]
+     40%|####      | 4000/10000 [00:04<00:05, 1035.67it/s]
+     48%|####8     | 4800/10000 [00:05<00:04, 1163.73it/s]
+     56%|#####6    | 5600/10000 [00:05<00:03, 1259.53it/s]
+    reward: -2.42 (r0 = -2.48), reward eval: reward: -0.00, reward normalized=-2.79/6.18, grad norm= 44.37, loss_value= 264.02, loss_actor= 15.37, target value: -16.89:  56%|#####6    | 5600/10000 [00:06<00:03, 1259.53it/s]
+    reward: -2.42 (r0 = -2.48), reward eval: reward: -0.00, reward normalized=-2.79/6.18, grad norm= 44.37, loss_value= 264.02, loss_actor= 15.37, target value: -16.89:  64%|######4   | 6400/10000 [00:07<00:04, 869.73it/s] 
+    reward: -0.12 (r0 = -2.48), reward eval: reward: -0.00, reward normalized=-2.19/5.28, grad norm= 32.93, loss_value= 177.57, loss_actor= 11.78, target value: -13.67:  64%|######4   | 6400/10000 [00:07<00:04, 869.73it/s]
+    reward: -0.12 (r0 = -2.48), reward eval: reward: -0.00, reward normalized=-2.19/5.28, grad norm= 32.93, loss_value= 177.57, loss_actor= 11.78, target value: -13.67:  72%|#######2  | 7200/10000 [00:08<00:04, 699.25it/s]
+    reward: -3.01 (r0 = -2.48), reward eval: reward: -0.00, reward normalized=-2.92/6.06, grad norm= 348.61, loss_value= 345.16, loss_actor= 14.69, target value: -19.19:  72%|#######2  | 7200/10000 [00:09<00:04, 699.25it/s]
+    reward: -3.01 (r0 = -2.48), reward eval: reward: -0.00, reward normalized=-2.92/6.06, grad norm= 348.61, loss_value= 345.16, loss_actor= 14.69, target value: -19.19:  80%|########  | 8000/10000 [00:10<00:03, 621.57it/s]
+    reward: -4.36 (r0 = -2.48), reward eval: reward: -0.00, reward normalized=-2.66/4.94, grad norm= 124.14, loss_value= 198.18, loss_actor= 12.94, target value: -17.62:  80%|########  | 8000/10000 [00:11<00:03, 621.57it/s]
+    reward: -4.36 (r0 = -2.48), reward eval: reward: -0.00, reward normalized=-2.66/4.94, grad norm= 124.14, loss_value= 198.18, loss_actor= 12.94, target value: -17.62:  88%|########8 | 8800/10000 [00:11<00:02, 579.06it/s]
+    reward: -3.76 (r0 = -2.48), reward eval: reward: -5.85, reward normalized=-2.16/4.86, grad norm= 76.93, loss_value= 169.87, loss_actor= 14.40, target value: -15.06:  88%|########8 | 8800/10000 [00:14<00:02, 579.06it/s] 
+    reward: -3.76 (r0 = -2.48), reward eval: reward: -5.85, reward normalized=-2.16/4.86, grad norm= 76.93, loss_value= 169.87, loss_actor= 14.40, target value: -15.06:  96%|#########6| 9600/10000 [00:15<00:01, 399.67it/s]
+    reward: -4.49 (r0 = -2.48), reward eval: reward: -5.85, reward normalized=-2.41/5.07, grad norm= 69.34, loss_value= 210.28, loss_actor= 16.28, target value: -17.91:  96%|#########6| 9600/10000 [00:16<00:01, 399.67it/s]
+    reward: -4.49 (r0 = -2.48), reward eval: reward: -5.85, reward normalized=-2.41/5.07, grad norm= 69.34, loss_value= 210.28, loss_actor= 16.28, target value: -17.91: : 10400it [00:18, 366.73it/s]                        
+    reward: -4.88 (r0 = -2.48), reward eval: reward: -5.85, reward normalized=-3.59/4.17, grad norm= 147.86, loss_value= 198.34, loss_actor= 23.19, target value: -25.45: : 10400it [00:18, 366.73it/s]
 
 
 
@@ -1723,7 +1723,7 @@ To iterate further on this loss module we might consider:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  29.589 seconds)
+   **Total running time of the script:** ( 0 minutes  28.592 seconds)
 
 
 .. _sphx_glr_download_advanced_coding_ddpg.py:

diff --git a/_sources/advanced/dynamic_quantization_tutorial.rst.txt b/_sources/advanced/dynamic_quantization_tutorial.rst.txt
@@ -517,9 +517,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 207.4
+    elapsed time (seconds): 204.1
     loss: 5.168
-    elapsed time (seconds): 119.2
+    elapsed time (seconds): 116.5
 
 
 
@@ -541,7 +541,7 @@ Thanks for reading! As always, we welcome any feedback, so please create an issu
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  37.532 seconds)
+   **Total running time of the script:** ( 5 minutes  31.304 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

diff --git a/_sources/advanced/neural_style_tutorial.rst.txt b/_sources/advanced/neural_style_tutorial.rst.txt
@@ -410,34 +410,32 @@ network to evaluation mode using ``.eval()``.
     Downloading: "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth
 
       0%|          | 0.00/548M [00:00<?, ?B/s]
-      2%|1         | 9.38M/548M [00:00<00:05, 97.7MB/s]
-      4%|4         | 23.4M/548M [00:00<00:04, 126MB/s] 
-      8%|8         | 43.9M/548M [00:00<00:03, 166MB/s]
-     12%|#1        | 64.5M/548M [00:00<00:02, 186MB/s]
-     16%|#5        | 85.4M/548M [00:00<00:02, 197MB/s]
-     19%|#9        | 106M/548M [00:00<00:02, 203MB/s] 
-     23%|##3       | 126M/548M [00:00<00:02, 207MB/s]
-     27%|##6       | 147M/548M [00:00<00:02, 209MB/s]
-     31%|###       | 168M/548M [00:00<00:01, 212MB/s]
-     34%|###4      | 188M/548M [00:01<00:01, 214MB/s]
-     38%|###8      | 209M/548M [00:01<00:01, 215MB/s]
-     42%|####1     | 230M/548M [00:01<00:01, 215MB/s]
-     46%|####5     | 251M/548M [00:01<00:01, 216MB/s]
-     50%|####9     | 272M/548M [00:01<00:01, 217MB/s]
-     53%|#####3    | 292M/548M [00:01<00:01, 217MB/s]
-     57%|#####7    | 313M/548M [00:01<00:01, 217MB/s]
-     61%|######    | 334M/548M [00:01<00:01, 217MB/s]
-     65%|######4   | 355M/548M [00:01<00:00, 217MB/s]
-     69%|######8   | 376M/548M [00:01<00:00, 218MB/s]
-     72%|#######2  | 397M/548M [00:02<00:00, 218MB/s]
-     76%|#######6  | 418M/548M [00:02<00:00, 218MB/s]
-     80%|#######9  | 438M/548M [00:02<00:00, 218MB/s]
-     84%|########3 | 459M/548M [00:02<00:00, 218MB/s]
-     88%|########7 | 480M/548M [00:02<00:00, 218MB/s]
-     91%|#########1| 501M/548M [00:02<00:00, 218MB/s]
-     95%|#########5| 522M/548M [00:02<00:00, 218MB/s]
-     99%|#########9| 543M/548M [00:02<00:00, 218MB/s]
-    100%|##########| 548M/548M [00:02<00:00, 210MB/s]
+      4%|3         | 20.6M/548M [00:00<00:02, 216MB/s]
+      8%|7         | 41.9M/548M [00:00<00:02, 220MB/s]
+     12%|#1        | 63.1M/548M [00:00<00:02, 221MB/s]
+     15%|#5        | 84.4M/548M [00:00<00:02, 221MB/s]
+     19%|#9        | 106M/548M [00:00<00:02, 222MB/s] 
+     23%|##3       | 127M/548M [00:00<00:01, 222MB/s]
+     27%|##7       | 148M/548M [00:00<00:01, 223MB/s]
+     31%|###       | 170M/548M [00:00<00:01, 223MB/s]
+     35%|###4      | 191M/548M [00:00<00:01, 223MB/s]
+     39%|###8      | 212M/548M [00:01<00:01, 223MB/s]
+     43%|####2     | 234M/548M [00:01<00:01, 223MB/s]
+     47%|####6     | 255M/548M [00:01<00:01, 223MB/s]
+     50%|#####     | 276M/548M [00:01<00:01, 223MB/s]
+     54%|#####4    | 298M/548M [00:01<00:01, 223MB/s]
+     58%|#####8    | 319M/548M [00:01<00:01, 223MB/s]
+     62%|######2   | 340M/548M [00:01<00:00, 223MB/s]
+     66%|######5   | 362M/548M [00:01<00:00, 223MB/s]
+     70%|######9   | 383M/548M [00:01<00:00, 223MB/s]
+     74%|#######3  | 404M/548M [00:01<00:00, 224MB/s]
+     78%|#######7  | 426M/548M [00:02<00:00, 223MB/s]
+     82%|########1 | 447M/548M [00:02<00:00, 224MB/s]
+     85%|########5 | 468M/548M [00:02<00:00, 224MB/s]
+     89%|########9 | 490M/548M [00:02<00:00, 224MB/s]
+     93%|#########3| 511M/548M [00:02<00:00, 223MB/s]
+     97%|#########7| 533M/548M [00:02<00:00, 224MB/s]
+    100%|##########| 548M/548M [00:02<00:00, 223MB/s]
 
 
 
@@ -758,22 +756,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 5.491051 Content Loss: 4.111195
+    Style Loss : 4.042071 Content Loss: 4.071409
 
     run [100]:
-    Style Loss : 1.128039 Content Loss: 3.038853
+    Style Loss : 1.110853 Content Loss: 3.019152
 
     run [150]:
-    Style Loss : 0.718506 Content Loss: 2.659447
+    Style Loss : 0.711853 Content Loss: 2.647382
 
     run [200]:
-    Style Loss : 0.482143 Content Loss: 2.494146
+    Style Loss : 0.475776 Content Loss: 2.486519
 
     run [250]:
-    Style Loss : 0.352943 Content Loss: 2.408322
+    Style Loss : 0.344838 Content Loss: 2.401526
 
     run [300]:
-    Style Loss : 0.270402 Content Loss: 2.352659
+    Style Loss : 0.261330 Content Loss: 2.346556
 
 
 
@@ -782,7 +780,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  38.614 seconds)
+   **Total running time of the script:** ( 0 minutes  38.513 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:

diff --git a/_sources/advanced/numpy_extensions_tutorial.rst.txt b/_sources/advanced/numpy_extensions_tutorial.rst.txt
@@ -303,7 +303,7 @@ The backward pass computes the gradient ``wrt`` the input and the gradient ``wrt
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.627 seconds)
+   **Total running time of the script:** ( 0 minutes  0.616 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py: