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For discussions on distributed computing fundamentals. Ask about tasks, actors, scheduling, resource management, and fault tolerance.

For questions on large-scale data loading, preprocessing, and batch transformations in distributed pipelines. Ray Datasets are the standard way to load and exchange data in Ray applications and provide transformations such as map, filter, and repartition.

For questions on distributed model training. Use Train to scale a model’s training process across CPUs, GPUs, or multiple nodes.

For questions on hyperparameter tuning and experiment management. Use Tune to search for the best model configurations by running and scheduling many parallel trials.

Topics include model serving and inference. Use Serve to deploy and scale machine learning models with built-in support for APIs, batching, and multi-GPU inference.

For questions on reinforcement learning. Use RLlib to build, train, and scale RL models with built-in algorithms and distributed training support.

For questions on setting up and managing Ray clusters across multiple machines or cloud providers. Use Clusters to scale Ray applications across distributed infrastructure.

For all question related to monitoring and debugging your Ray applications. Don’t be shy - all questions are welcome!