ffcv
composer
| ffcv | composer | |
|---|---|---|
| 8 | 19 | |
| 2,742 | 4,991 | |
| 0.7% | 1.6% | |
| 3.5 | 9.8 | |
| 8 days ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ffcv
- Question: TIFF image dataset - size in RAM.
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[P] Composer: a new PyTorch library to train models ~2-4x faster with better algorithms
PyTorch Lightning is also very slow compared to Composer. You don't have to believe us: our friends who wrote the FFCV library benchmarked us against PTL (see the lower left plot in the first cluster of graphs) , and you can see the difference for yourself. For the same accuracy, the FFCV folks found that Composer is about 5x faster than PTL on ResNet-50 on ImageNet.
- FFCV: Fast Forward Computer Vision
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Does anyone know where I can find research papers for preprocessing large image datasets?
maybe something like this? https://github.com/libffcv/ffcv
- Ffcv: Train models at a fraction of the cost with accelerated data loading
- Show HN: FFCV – Accelerated machine learning via fast data loading
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[P] FFCV: Accelerated Model Training via Fast Data Loading
Hi! You can join the slack directly from the link on the homepage! (ffcv.io)
composer
- Composer – A PyTorch Library for Efficient Neural Network Training
- Train neural networks up to 7x faster
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How to Train Large Models on Many GPUs?
Mosaic's open source library is excellent: Composer https://github.com/mosaicml/composer.
* It gives you PyTorch DDP for free. Makes FSDP about as easy as can be, and provides best in class performance monitoring tools. https://docs.mosaicml.com/en/v0.12.1/notes/distributed_train...
Here's a nice intro to using Huggingface models: https://docs.mosaicml.com/en/v0.12.1/examples/finetune_huggi...
I'm just a huge fan of their developer experience. It's up there with Transformers and Datasets as the nicest tools to use.
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[D] Am I stupid for avoiding high level frameworks?
You may consider using Composer Composer by MosaicML.
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[P] Farewell, CUDA OOM: Automatic Gradient Accumulation
Which is why I'm excited to announce that we (MosaicML) just released an automatic way to avoid these errors. Namely, we just added automatic gradient accumulation to Composer, our open source library for faster + easier neural net training.
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I highly and genuinely recommend Fast.ai course to beginners
I would love to know your thoughts on PyTorch Lightning vs. other, even more lightweight libraries, if you have the time. PL strikes me as being less idiosyncratic than FastAI, but I'm still not sure whether it would be better in engineering work to go even more lightweight (when I'm not just writing the code myself) -- something that offers up just optimizations and a trainer, a la MosaicML's [Composer](https://github.com/mosaicml/composer) or Chris Hughes's [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) .
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10x faster matrix and vector operations
This master's thesis sort of does it, but it doesn't have any fine-tuning yet so it completely wrecks the accuracy: https://github.com/joennlae/halutmatmul.
If someone worked on contributing this to Composer [1] I'd be down to help out. I can't justify building it all on my own right now since we're 100% focused on training speedup, but I could definitely meet and talk through it, help code tricky parts, review PRs, etc.
[1] https://github.com/mosaicml/composer
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[D] Is anyone working on interesting ML libraries and looking for contributors?
We're always looking for contributors for Composer. tl;dr it speeds up neural net training by a lot (e.g., 7x faster ResNet-50).
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[R] Blazingly Fast Computer Vision Training with the Mosaic ResNet and Composer
Looking at this: https://github.com/mosaicml/composer
- [D] Where do we currently stand at in lottery ticket hypothesis research?
What are some alternatives?
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
apex - A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
array_storage_benchmark - Compare some methods of array storage in Python (numpy)
cifar10-fast
ffcv-imagenet - Train ImageNet *fast* in 500 lines of code with FFCV
pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers
pillow-simd - The friendly PIL fork
open_lth - A repository in preparation for open-sourcing lottery ticket hypothesis code.