composer
alpa
| composer | alpa | |
|---|---|---|
| 19 | 4 | |
| 5,002 | 2,986 | |
| 1.8% | 0.8% | |
| 9.8 | 5.1 | |
| 1 day ago | 5 months 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.
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?
alpa
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How to Train Large Models on Many GPUs?
- Alpa does training and serving with 175B parameter models https://github.com/alpa-projects/alpa
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how much does it actually cost in terms of computer power for open AI to respond
alpa.ai states "You will need at least 350GB GPU memory on your entire cluster to serve the OPT-175B model. For example, you can use 4 x AWS p3.16xlarge instances, which provide 4 (instance) x 8 (GPU/instance) x 16 (GB/GPU) = 512 GB memory."
- Alpa: Auto-parallelizing large model training and inference (by UC Berkeley)
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Alpa: Automated Model-Parallel Deep Learning
GitHub code: https://github.com/alpa-projects/alpa
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]
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
ffcv - FFCV: Fast Forward Computer Vision (and other ML workloads!)
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
FedML - FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale.
cifar10-fast
awesome-tensor-compilers - A list of awesome compiler projects and papers for tensor computation and deep learning.
pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers
adaptdl - Resource-adaptive cluster scheduler for deep learning training.