Comments (1)
We haven't personally experimented but there is no reason why it wouldn't. Indeed because when the loader produces a batch it's already on the GPU and copied, you can just convert that tensor and do what you feel like as long as you work in the default CUDA stream there should be no issues.
https://docs.cupy.dev/en/stable/user_guide/interoperability.html contains useful information on how to convert GPU tensors cost-free (disclaimer there might be better ways)
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Related Issues (20)
- Changing Indices during training leads to much slower training HOT 2
- Memory Leak in Ffcv Loader? HOT 4
- Import error - libopencv_impgproc missing HOT 2
- Installation issues HOT 6
- Small bug (improvement suggestion) in the quickstart doc HOT 1
- stuck in the loader when using only cpu HOT 4
- Grayscale Image Datasets HOT 2
- Top-1 accuracy on ImageNet drops between runs -- only difference is FFCV HOT 2
- Large .beton files slow down or even freeze learning during loading [possible bug] HOT 2
- [General question] FFCV scope
- ModuleNotFoundError: No module named 'ffcv.compiler
- doubt about mutli-gpu train when use imagenet 4 gpus HOT 1
- Default num_workers is incompatible with SLURM
- Installing FFCV on CPU-only node HOT 1
- Exact performance improvement
- Unable to save anything in the Fields HOT 1
- Compression error causes performance drop
- Reuse memory?
- Installing ffcv fails HOT 1
- Unexpected output with BytesField, question for genomics
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