Comments (1)
Hello!
Thank you very much. There are many reasons but on the top of my head:
- Single file vs many smaller files (less stress on the file system)
- Using the knowledge about which samples will be needed in the future to start reading the data ahead of time and absorb the latency of slow storage
- The use of threads instead of processes remove the need of any inter-process communication (shared memory etc...) and reduces memory consumption
- Compilation of the CPU augmentations transforms in machine code at the start of training (JIT).
- Copying the next batches while the compute is happening instead of simply asynchronously (but still on the critical path)
- Ability to use to move augmentations to CPU or GPU depending on where is your bottleneck.
- The use of pre-resized datasets
- In the case of systems with a lot of RAM, leveraging operating system level caching to implicitly store the entire dataset in RAM
- Being able to use alternative or a mix of different encoding for the data. JPEG can be slow to decode, if you have enough storage/RAM but not enough compute using uncompressed images might be faster, and FFCV enables that simply by switching a flag.
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Related Issues (20)
- CenterCropRGBImageDecoder vs torchvision.transforms.CenterCrop HOT 1
- Speedup on Image to Image problems? HOT 1
- Indexing HOT 1
- 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
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