Ray: Accelerate AI
Get accelerated ML workloads with Ray, a compute engine for founders in AI and machine learning, backed by 43k+ GitHub stars.
43,149 stars7,770 forksPythonQuality 8/10Updated 7/7/2026100% free ยท open source Ray accelerates machine learning workloads by providing a distributed compute engine for scalable AI applications
- โขYou need to scale your ML model training across multiple machines
- โขYou want to accelerate your AI workflows with a high-performance compute engine
- โขYou're building a real-time AI application that requires low-latency processing
- 1Import Ray in your Python script with `import ray`
- 2Initialize Ray with `ray.init()` to start the Ray runtime
- 3Use the `@ray.remote` decorator to define a remote function, e.g. `@ray.remote def my_function(x): return x * 2`
- 4Call the remote function with `my_function.remote(2)` to execute it on a remote worker
- 5Shutdown Ray with `ray.shutdown()` when you're finished
Ready-to-paste prompt ray.init(); @ray.remote; def train_model(data): # your model training code here; train_model.remote([1, 2, 3])
Heads up: Make sure you have the correct version of Python installed, as Ray supports Python 3.7, 3.8, and 3.9, but not Python 3.6 or earlier
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Topics
data-science
deep-learning
deployment
distributed
hyperparameter-optimization
hyperparameter-search
large-language-models
llm
llm-inference
llm-serving
machine-learning
optimization
parallel
python
pytorch
ray
reinforcement-learning
rllib
serving
tensorflow
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