Workflow:Ray project Ray Remote Task Execution
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Task_Parallelism |
| Last Updated | 2026-02-13 16:00 GMT |
Overview
End-to-end process for executing Python or Java functions as distributed remote tasks on a Ray cluster, returning asynchronous object references for result retrieval.
Description
This workflow covers the standard procedure for submitting stateless functions as remote tasks to a Ray cluster. It begins with initializing the Ray runtime, continues through defining and submitting remote function calls, and concludes with collecting results from the distributed object store. The process leverages Ray's task scheduler to distribute work across available cluster resources, with each task execution returning an ObjectRef that acts as a future/promise for the result. Tasks can be submitted in parallel for concurrent execution, and results can be selectively waited on using the wait API.
Usage
Execute this workflow when you need to parallelize stateless computations across a Ray cluster. Typical triggers include batch processing workloads, embarrassingly parallel computations, or any scenario where independent function calls can benefit from distributed execution across multiple CPUs or GPUs.
Execution Steps
Step 1: Initialize Ray Runtime
Start the Ray runtime by calling the initialization entry point. This bootstraps the runtime singleton, loads the native libraries, establishes a connection to the cluster (or starts a local one), and registers a JVM/process shutdown hook for cleanup. The runtime factory pattern allows plugging in different backend implementations (native cluster mode vs. local development mode).
Key considerations:
- Initialization is idempotent; calling init on an already-initialized runtime is a no-op
- Configuration can specify cluster address, resource requirements, and runtime environment
- A shutdown hook is automatically registered to clean up on process exit
Step 2: Define Remote Functions
Identify the functions to be executed remotely. In Java, these are static methods referenced via method references (e.g., MyClass::myMethod) that conform to the RayFunc interface family. In Python, these are functions decorated with @ray.remote. The function manager resolves method references to function descriptors containing class name, method name, and parameter types.
Key considerations:
- Functions must be serializable and accessible by all workers
- Overloaded methods require explicit type casting to disambiguate
- Return types must be serializable through the object store
Step 3: Submit Tasks to Cluster
Submit function calls as remote tasks using the task submission API. Each call is non-blocking and returns immediately with an ObjectRef pointing to the eventual result. The runtime serializes arguments through the ArgumentsBuilder, resolves the function descriptor via the FunctionManager, and delegates to the TaskSubmitter which communicates with the cluster scheduler. Resource requirements (CPU, GPU) can be specified per task.
Key considerations:
- Arguments can be raw values or ObjectRef references to previously stored objects
- Each task submission pre-allocates return object IDs for result tracking
- Resource constraints influence task placement across cluster nodes
Step 4: Collect Results from Object Store
Retrieve task results by calling get on the returned ObjectRef instances. This blocks until the referenced object becomes available in the distributed object store. For batch operations, multiple ObjectRefs can be collected in a single call. The wait API provides a non-blocking alternative that returns subsets of ready and unready references, enabling progressive result processing.
Key considerations:
- Single get blocks until result is available or timeout expires
- Batch get retrieves multiple results in one call
- Wait returns a WaitResult partitioning references into ready and unready sets
- Timeout parameters prevent indefinite blocking
Step 5: Shutdown Runtime
Terminate the Ray runtime when all work is complete. This releases cluster resources, disconnects from the scheduler, and cleans up local state. The shutdown is synchronized to prevent concurrent access during teardown.
Key considerations:
- Shutdown is also triggered automatically by the registered shutdown hook
- After shutdown, the runtime must be re-initialized before use
- Outstanding ObjectRefs become invalid after shutdown