Ray Core provides simple primitives for building distributed Python applications. It’s great for parallelizing single-machine Python applications with minimal code changes. Built on top of Ray Core is a rich ecosystem of high-level libraries and frameworks for scaling specific workloads like reinforcement learning and model serving.
Ray is packaged with the following libraries for accelerating machine learning workloads:
- Tune: Scalable Hyperparameter Tuning
- RLlib: Scalable Reinforcement Learning
- Train: Distributed Deep Learning (alpha)
- Datasets: Distributed Data Loading and Compute (beta)
As well as libraries for taking ML and distributed apps to production:
There are also many community integrations with Ray, including Dask, MARS, Modin, Horovod, Hugging Face, Scikit-learn, and others. Check out the full list of Ray distributed libraries here.
https://github.com/ray-project/ray
https://www.anyscale.com/blog/writing-your-first-distributed-python-application-with-ray
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