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[WIP] Modernize the Ray Tune tutorial #3694
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…ate code block formatting from text to bash.
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3694
Note: Links to docs will display an error until the docs builds have been completed. This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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| # Set the number of trials, epochs, and GPUs per trial here: | ||
| # The following configuration is for a quick run (1 trial, 1 epoch, CPU only) for demonstration purposes. | ||
| main(num_trials=1, max_num_epochs=1, gpus_per_trial=0) |
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TODO: add GPUs back when I am done
| def load_data(data_dir="./data"): | ||
| transform = transforms.Compose( | ||
| [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] | ||
| [transforms.ToTensor(), transforms.Normalize((0.4914, 0.48216, 0.44653), (0.2022, 0.19932, 0.20086))] |
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I wrote a blog post about this: https://www.ricardodecal.com/guides/use-these-normalization-values-for-torchvision-datasets/
This is the first of several PRs against the Pytorch Tutorials. This PR unstales the Ray Tune tutorial:
Ray-Distributedtag.Opening this as a draft PR so that I can get feedback from the Ray Tune core devs first
cc @subramen @albanD @jbschlosser