Add OSS Train Loop #563
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Add OSS Train Loop #563
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Summary: Pull Request resolved: pytorch#563 * Add simple train loop example of dataloader2 with a toy model in the torchdata examples. This demonstrates the dataloader2 usage with train step (forward + backward + optimize) in the train loop of multiple epochs. * This basic example skipped the dataset and datapipe creation part, only focused on the train loop usage part with the DLv2. We will have more examples to showcase the advantages: (1) The usage of the DLv2 with popular open source dataset. (2) Integrate datasets/datapipes with different reading service. (3) Use popular open source dataset in the E2E training. (4) Datapipe manipulation for example batch, collate, map. (5) Dist usage and examples with features such as sharding_filter for the sharding feature. (6) Eventually add those examples to the pytorch tutorial. Reviewed By: ejguan Differential Revision: D37366122 fbshipit-source-id: 38863b70d5c8e99928ccbfcd8aa4e50de4d536f5
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This pull request was exported from Phabricator. Differential Revision: D37366122 |
ejguan
approved these changes
Jun 30, 2022
NivekT
approved these changes
Jun 30, 2022
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Summary:
Add simple train loop example of dataloader2 with a toy model in the torchdata
examples. This demonstrates the dataloader2 usage with train step (forward +
backward + optimize) in the train loop of multiple epochs.
This basic example skipped the dataset and datapipe creation part, only focused
on the train loop usage part with the DLv2. We will have more examples to
showcase the advantages:
(1) The usage of the DLv2 with popular open source dataset.
(2) Integrate datasets/datapipes with different reading service.
(3) Use popular open source dataset in the E2E training.
(4) Datapipe manipulation for example batch, collate, map.
(5) Dist usage and examples with features such as sharding_filter for the sharding feature.
(6) Eventually add those examples to the pytorch tutorial.
Reviewed By: ejguan
Differential Revision: D37366122