Source code for nemo_rl.distributed.ray_actor_environment_registry

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from nemo_rl.distributed.virtual_cluster import PY_EXECUTABLES

ACTOR_ENVIRONMENT_REGISTRY = {
    "nemo_rl.models.generation.vllm.VllmGenerationWorker": PY_EXECUTABLES.VLLM,
    "nemo_rl.models.policy.dtensor_policy_worker.DTensorPolicyWorker": PY_EXECUTABLES.BASE,
    "nemo_rl.models.policy.fsdp1_policy_worker.FSDP1PolicyWorker": PY_EXECUTABLES.BASE,
    "nemo_rl.environments.math_environment.MathEnvironment": PY_EXECUTABLES.SYSTEM,
    "nemo_rl.environments.games.sliding_puzzle.SlidingPuzzleEnv": PY_EXECUTABLES.SYSTEM,
}




def get_actor_python_env(actor_class_fqn: str) -> PY_EXECUTABLES:
    if actor_class_fqn in ACTOR_ENVIRONMENT_REGISTRY:
        return ACTOR_ENVIRONMENT_REGISTRY[actor_class_fqn]
    else:
        raise ValueError(
            f"No actor environment registered for {actor_class_fqn}"
            f"You're attempting to create an actor ({actor_class_fqn})"
            "without specifying a python environment for it. Please either"
            "specify a python environment in the registry "
            "(nemo_rl.distributed.ray_actor_environment_registry.ACTOR_ENVIRONMENT_REGISTRY) "
            "or pass a py_executable to the RayWorkerBuilder. If you're unsure about which "
            "environment to use, a good default is PY_EXECUTABLES.SYSTEM for ray actors that "
            "don't have special dependencies. If you do have special dependencies (say, you're "
            "adding a new generation framework or training backend), you'll need to specify the "
            "appropriate environment. See uv.md for more details."
        )