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Ray
Scale Python AI workloads from laptop to cluster — distributed training and serving.
0Open Source
MLOps
Overview
Ray is an open-source framework for scaling Python workloads from a laptop to a cluster, with specialized libraries for distributed training (Ray Train), serving (Ray Serve), and reinforcement learning (RLlib).
Key Features
- Ray Core — distributed task and actor execution
- Ray Train — distributed ML training with PyTorch, TensorFlow
- Ray Serve — scalable model serving with FastAPI integration
- Ray Tune — distributed hyperparameter tuning
- KubeRay operator for Kubernetes deployment
- Anyscale cloud for managed Ray clusters
Real-World Workflows
Distributed fine-tuning of a large model
- 1Wrap your training function with @ray.remote
- 2Use Ray Train's TorchTrainer with N workers
- 3Scale data loading with Ray Data pipelines
- 4Monitor training progress in the Ray Dashboard
Getting Started
pip install 'ray[serve,train]'
import ray
ray.init()
@ray.remote
def train_model(config):
# your training code
return accuracy
result = ray.get(train_model.remote({'lr': 0.01}))
print(result)Compare Alternatives
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