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Thanks for starting this discussion Brian! Some context: Lenses will be downloaded in jpeg and FITS format. Conversion can be made to h5 of npy, but that will be as a subsequent process after download is complete. Space needs are estimated to be 10s of GBs within the next two weeks. Must be on spinning disk. Direct accessibility from GPU resources is required. Very preferable if the system also has deep learning packages pre-installed/documented, so training can occur easily. The competitor is Google Colab, where I have ~infinite free GDrive space through UChicago. |
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For those with DES access, here is the format used for the DES and DELVE cutouts: |
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we’re open to learning from and adjusting toward best practices — viz the choice of CSV, Pandas, Fits, H5. Our current deep skies standard (but it's very new) is H5, because it seems to be the most versatile in terms of internal data structures, and I posit that there can be one h5 file per data set to minimize file management. |
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@jsv1206 You just uploaed some lens data to EAF. What format did you use? Also, how is our space looking? Should we get more? |
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Let's discuss here a bit where we will store our data this summer and in the long run.
Current options
Major questions
--> forever
Maybe RAID is enough?
H5 is currently the idea.
We have an immediate need to start putting strong lensing data sets somewhere, both for basic storage and for machine learning runs in the next 1 weeks.
@AleksCipri @kadrlica @jsv1206 @AeRabelais @voetberg @MakesiP @antomaco @3ileenolan
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