Anyscale

Introduction to Anyscale and Ray

The AI Complexity Wall and How Ray Helps

Overview of the Anyscale Unified AI Platform

Introduction to Ray Turbo

Understanding Anyscale Workspaces and compute resources

Monitoring and debugging Ray applications

Configurations

Production Jobs

Production Services

Overview of the Ray AI Libraries

end-to-end example with XGBoost

Single GPU PyTorch

Overview of the training loop in Ray Train

Migrating the model to Ray Train

Migrating the dataset to Ray Train

Reporting metrics and checkpoints

Launching the distributed training job

Accessing training results

Ray Train in production

Loading the data

Starting out with vanilla PyTorch

Hyperparameter tuning with Ray Tune

Ray Tune in production

When to use Ray Data

Loading Data

Transforming Data

Materializing Data

Data Operations: Grouping, Aggregation, and Shuffling

Persisting Data

Ray Data in Production