Add the Latest Features For Basics Autograd Tutorial #3395
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3395
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 00b4978 with merge base efd3430 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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@pytorchbot label core |
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soulitzer
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Jun 25, 2025
| @@ -160,6 +161,39 @@ | |||
| # - accumulates them in the respective tensor’s ``.grad`` attribute | |||
| # - using the chain rule, propagates all the way to the leaf tensors. | |||
| # | |||
| # We can also visualize the computational graph by the following 2 methods: | |||
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I think for this section, we can just keep it short for now, and link to the relevant resource:
To get a sense of what this computational graph looks like we can use the following tools:
1. torchviz is a package to visualize computational graphs
https://github.com/szagoruyko/pytorchviz
2. TORCH_LOGS="+autograd" enables logging for the backward pass.
https://dev-discuss.pytorch.org/t/highlighting-a-few-recent-autograd-features-h2-2023/1787
(for the links use the proper hyperlink syntax)
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Fixes #3119
As described in the issue, I have updated the tutorial to address the latest visualization tools and add the autograd mechanics tutorial link to it as well. Could you please review?
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cc @subramen @albanD @jbschlosser @svekars