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 references 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 Ray DAG with multiple MultiOutputNode Reuse Ray Actors in DAGs 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 Benchmark Authenticating Remote URIs in runtime_env Authenticating Remote URIs Running on VMs: the netrc File Running on KubeRay: Secrets with netrc Lifetimes of a User-Spawn Process User-Spawned Process Killed on Worker Exit Enabling the feature ⚠️ Caution: Core worker now reaps zombies, toggle back if you wait to waitpid Under the hood