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@crypdick crypdick commented Dec 11, 2025

This is the first of several PRs against the Pytorch Tutorials. This PR unstales the Ray Tune tutorial:

  • bumps Ray version from 2.7.2 -> 2.52.1
  • update the tutorial with the latest code from the Pytorch CIFAR10 tutorial which it is based off of
  • update to use the latest Ray Tune APIs
  • fixed the norm values for CIFAR10
  • rewrote most of the prose
  • added new sections for sampling, observability, conclusion, additional links
  • updated tags in the doc indices. Will need approval on the new Ray-Distributed tag.

Opening this as a draft PR so that I can get feedback from the Ray Tune core devs first

cc @subramen @albanD @jbschlosser

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pytorch-bot bot commented Dec 11, 2025

🔗 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.

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@meta-cla meta-cla bot added the cla signed label Dec 11, 2025

# 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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@svekars svekars added core Tutorials of any level of difficulty related to the core pytorch functionality intro labels Dec 11, 2025
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