PythonExecutorService

class torchrl.envs.llm.transforms.PythonExecutorService(pool_size: int = 32, timeout: float = 10.0)

Ray actor that manages a pool of persistent Python interpreters.

This service allows multiple environments to share a pool of Python interpreters, reducing resource usage and improving efficiency.

Parameters:

• pool_size (int) – Number of Python interpreter processes to maintain.

• timeout (float) – Timeout for code execution in seconds.

Examples

>>> # Register the service
>>> from torchrl.services import get_services
>>> services = get_services(backend="ray")
>>> services.register(
...     "python_executor",
...     PythonExecutorService,
...     pool_size=32,
...     timeout=10.0,
...     num_cpus=32,
...     max_concurrency=32
... )
>>>
>>> # Use in transform
>>> env = env.append_transform(
...     PythonInterpreter(services="ray")
... )

cleanup()

Cleanup all processes in the pool.

execute(code: str) → dict

Execute Python code using next available process (round-robin).

Parameters:

code – Python code to execute.

Returns:

Execution result with keys ‘success’, ‘stdout’, ‘stderr’, ‘returncode’.

Return type:

dict