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Fixes ray initialization to correctly direct subprocess output #3533

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Fixes ray initialization to correctly direct subprocess output #3533

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98257d0
Splits ray init from get_gpu_node_resources
ozhanozen Sep 23, 2025
15c60db
Correctly inits ray for invoke_tuning_run
ozhanozen Sep 23, 2025
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14 changes: 7 additions & 7 deletions scripts/reinforcement_learning/ray/tuner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,17 +217,17 @@ def invoke_tuning_run(cfg: dict, args: argparse.Namespace) -> None:
print("[WARNING]: Not saving checkpoints, just running experiment...")
print("[INFO]: Model parameters and metrics will be preserved.")
print("[WARNING]: For homogeneous cluster resources only...")

# Initialize Ray
util.ray_init(
ray_address=args.ray_address,
log_to_driver=True,
)

# Get available resources
resources = util.get_gpu_node_resources()
print(f"[INFO]: Available resources {resources}")

if not ray.is_initialized():
ray.init(
address=args.ray_address,
log_to_driver=True,
num_gpus=len(resources),
)

print(f"[INFO]: Using config {cfg}")

# Configure the search algorithm and the repeater
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4 changes: 3 additions & 1 deletion scripts/reinforcement_learning/ray/util.py
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Expand Up @@ -320,6 +320,8 @@ def ray_init(ray_address: str = "auto", runtime_env: dict[str, Any] | None = Non
f" runtime_env={runtime_env}"
)
ray.init(address=ray_address, runtime_env=runtime_env, log_to_driver=log_to_driver)
else:
print("[WARNING]: Attempting to initialize Ray but it is already initialized!")


def get_gpu_node_resources(
Expand All @@ -343,7 +345,7 @@ def get_gpu_node_resources(
or simply the resource for a single node if requested.
"""
if not ray.is_initialized():
ray_init()
raise RuntimeError("Ray must be initialized before calling get_gpu_node_resources().")
nodes = ray.nodes()
node_resources = []
total_cpus = 0
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