Advanced Topics

This section covers extended topics on how to use Ray.

• Tips for first-time users
  • Tip 1: Delay ray.get()
  • Tip 2: Avoid tiny tasks
  • Tip 3: Avoid passing same object repeatedly to remote tasks
  • Tip 4: Pipeline data processing
• Starting Ray
  • What is the Ray runtime?
  • Starting Ray on a single machine
  • Starting Ray via the CLI (ray start)
  • Launching a Ray cluster (ray up)
  • What’s next?
• Ray Generators
  • Getting started
  • Error handling
  • Generator from Actor Tasks
  • Using the Ray generator with asyncio
  • Garbage collection of object referneces
  • Fault tolerance
  • Cancellation
  • How to wait for generator without blocking a thread (compatibility to ray.wait and ray.get)
  • Thread safety
  • Limitation
• Using Namespaces
  • Specifying namespace for named actors
  • Anonymous namespaces
  • Getting the current namespace
• Cross-Language Programming
  • Setup the driver
  • Python calling Java
  • Java calling Python
  • Cross-language data serialization
  • Cross-language exception stacks
• Working with Jupyter Notebooks & JupyterLab
  • Setting Up Notebook
• Lazy Computation Graphs with the Ray DAG API
  • Ray DAG with functions
  • Ray DAG with classes and class methods
  • Ray DAG with custom InputNode
  • More Resources
• Miscellaneous Topics
  • Dynamic Remote Parameters
  • Overloaded Functions
  • Inspecting Cluster State
    • Node Information
    • Resource Information
  • Running Large Ray Clusters
    • Tuning Operating System Settings
      • Maximum open files
      • ARP cache
    • Tuning Ray Settings
      • Resource broadcasting
    • Benchmark
• Authenticating Remote URIs in runtime_env
  • Authenticating Remote URIs
  • Running on VMs: the netrc File
  • Running on KubeRay: Secrets with netrc