feat: Adding cuda:n device allocation #694
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Signed-off-by: ahn <[email protected]>
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Signed-off-by: ahn <[email protected]>
| the same functionality. In case the alias envvar is set and the user tries to override the | ||
| parameter in settings initialization, Pydantic treats the parameter provided in __init__() | ||
| as an extra input instead of simply overwriting the evvar value for that parameter. | ||
| """ |
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I think this comment is useful to stay. Why to delete it?
| device: AcceleratorDevice = AcceleratorDevice.AUTO | ||
| device: str = "auto" | ||
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| @validator("device") |
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Better to avoid using the @validator because it is deprecated in Pydantic v2. Use instead the newer "Field Validators" (https://docs.pydantic.dev/latest/concepts/validators/#field-validators)
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This PR is WIP enables the allocation of cuda devices as the current implementation defaults to cuda:0. This enables launching on multi-gpu and not being restricted on cluster systems with device allocation.
Note: Further investigation is needed to make easyocr work with cuda:n as easyocr when using GPU it wraps the models in torch.DataParallel, which causes cuda:0 to be utilised.