[Doc] Add data ingestion clarification for AIR converting existing pytorch code example #32058
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Signed-off-by: Yunxuan Xiao <yunxuanx@Yunxuans-MBP.local.meter>
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| "Then we download the data:" | ||
| "Then we download the data: \n", | ||
| "\n", | ||
| "Assumption for this tutorial: your existing code is using the `torchvision.datasets` native to PyTorch. This tutorial continues to use `torchvision.datasets` to allow you to make as few code changes as possible. **Everything in this tutorial is also possible if you choose to use Ray Data, and you will also get the benefits of efficient preprocessing and multi-worker batch prediction.** See [here](train-datasets) for resources to get started with Ray Data." |
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- It can be any PyTorch DataLoader, not necessarily torchvision datasets
- The benefit is for parallel preprocessing-- you can still use Ray Data for batch prediction without using for training (as this tutorial already does)
- Maybe link to this tutorial instead: https://docs.ray.io/en/latest/ray-air/examples/torch_image_example.html?
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Thanks for the points! I have modified the description accordingly.
Signed-off-by: Yunxuan Xiao <yunxuanx@Yunxuans-MBP.local.meter>
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…ay_air.ipynb Co-authored-by: angelinalg <122562471+angelinalg@users.noreply.github.com> Signed-off-by: Yunxuan Xiao <xiaoyunxuan1998@gmail.com>
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…torch code example (ray-project#32058) The example under Ray AI Runtime/Example section directly used native PyTorch datasets for data loading. It's good to clarify that the current approach is for simplicity, the more recommended approach is to use the Ray dataset. Signed-off-by: Yunxuan Xiao <yunxuanx@Yunxuans-MBP.local.meter> Signed-off-by: Yunxuan Xiao <xiaoyunxuan1998@gmail.com> Co-authored-by: Yunxuan Xiao <yunxuanx@Yunxuans-MBP.local.meter> Co-authored-by: angelinalg <122562471+angelinalg@users.noreply.github.com> Co-authored-by: Yunxuan Xiao <yunxuanx@Yunxuans-MacBook-Pro.local> Signed-off-by: Edward Oakes <ed.nmi.oakes@gmail.com>
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Why are these changes needed?
The example under Ray AI Runtime/Example section directly used native PyTorch datasets for data loading. It's good to clarify that the current approach is for simplicity, the more recommended approach is to use the Ray dataset.
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