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I ran into Tiledb's ML repo (https://github.com/TileDB-Inc/TileDB-ML/blob/master/tiledb/ml/readers/pytorch.py), which seems to implement a pytorch based dataloader. It looks like we can speed up our dataloaders without setting threads to 1.
Mostly exploration and then figuring out if we can adopt the same logic to our implementations.
The text was updated successfully, but these errors were encountered:
Setting context to "spawn" will remove the need to setting threads to 1.
Sorry, something went wrong.
tony-kuo
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I ran into Tiledb's ML repo (https://github.com/TileDB-Inc/TileDB-ML/blob/master/tiledb/ml/readers/pytorch.py), which seems to implement a pytorch based dataloader. It looks like we can speed up our dataloaders without setting threads to 1.
Mostly exploration and then figuring out if we can adopt the same logic to our implementations.
The text was updated successfully, but these errors were encountered: