add vectorization options as a goal target #2483
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Signed-off-by: Mike Brown <brownwm@us.ibm.com>
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/2483
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svekars
reviewed
Jun 23, 2023
beginner_source/fgsm_tutorial.py
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| # | ||
| # A further direction to go, depending on available resources, is to modify | ||
| # the code to support processing work in batch, in parallel, and or distributed | ||
| # vs. working on one attack at a time in the above for each epsilon test() loop. |
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Do you want to add examples of those?
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thought about it and was thinking this example is more a beginner example and is currently the appropriate size for this section.. Perhaps a second example addressing one or more of the above concerns is warranted and we could link from here?
svekars
reviewed
Jun 27, 2023
svekars
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Jun 27, 2023
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Consider closing #810 in concert with fix #2419
The original issue mentioned a performance problem and a suggestion for going beyond the simple test loop to vectorizing the processing in some form, batch / parallel etc.
However, fix 2419 linked above, addressed the performance problem by switching from always cpu to device and by correcting other inconsistencies. Thanks!
Further, this is targeted as a beginner example, and with the above corrections I believe this example now stands on it's own as is.
Description
This change merely proposes to the reader, in the where to go section, that:
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