composer
llrt
| composer | llrt | |
|---|---|---|
| 19 | 5 | |
| 5,002 | 0 | |
| 1.8% | - | |
| 9.8 | 0.0 | |
| 1 day ago | almost 2 years ago | |
| Python | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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?
llrt
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Connectionist simulation tool
Local learning rule tensors: https://github.com/bparkis/llrt
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Framework for investigating local neuron learning rules
Local learning rule tensors: https://github.com/bparkis/llrt It's a framework to allow you to flexibly specify how an individual neuron works, such as its learning rule, and then connect many of these neurons together in connectivity patterns such as Dense or Local2D. Unlike other ML frameworks, the connectivity pattern can be applied independently from the neuron behavior. So, you can easily test a proposed local learning rule across a variety of architectures.
- Framework for investigating neuron local learning rules
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[D] Is anyone working on interesting ML libraries and looking for contributors?
See if https://github.com/bparkis/llrt interests you.
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[Project] Framework for testing out different local learning rules
I made this project: https://github.com/bparkis/llrt It is a framework to allow you to flexibly specify how an individual neuron works, such as its learning rule, and then connect many of these neurons together in connectivity patterns such as Dense or Local2D. Unlike other ML frameworks, the connectivity pattern can be applied independently from the neuron behavior.
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]
hamilton - A scalable general purpose micro-framework for defining dataflows. THIS REPOSITORY HAS BEEN MOVED TO www.github.com/dagworks-inc/hamilton
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
grouphug - Multi-task modelling extensions for huggingface transformers
ffcv - FFCV: Fast Forward Computer Vision (and other ML workloads!)
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
cifar10-fast
pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers
open_lth - A repository in preparation for open-sourcing lottery ticket hypothesis code.
pytorch-accelerated - A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases, and capable of utilizing different hardware options with no code changes required. Docs: https://pytorch-accelerated.readthedocs.io/en/latest/
apex - A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch