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add section for setting Resources for ray head & worker pods #1775

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add section for setting Resources for ray head & worker pods #1775

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3f58b60
add section for setting k8s_pod for ray head & worker pods
fiedlerNr9 Jan 3, 2025
13c7847
ruff format
fiedlerNr9 Jan 3, 2025
33167e1
ruff check fix
fiedlerNr9 Jan 3, 2025
e2ca35f
update instructions to set resources
fiedlerNr9 Jan 31, 2025
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24 changes: 21 additions & 3 deletions examples/ray_plugin/ray_plugin/ray_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,9 +85,27 @@ def ray_task(n: int) -> typing.List[int]:

# %% [markdown]
# :::{note}
# The `Resources` section here is utilized to specify the resources allocated to the worker nodes.
# :::
#
# The `Resources` section here is utilized to specify the resources allocated to all Ray components like head, worker & driver pods.
# %% [markdown]
# For more fine grain resource allocation, you are able to set the resources for Ray head & worker pods by setting `requests` and/or `limits` for the respective config.
# %%
ray_config = RayJobConfig(
head_node_config=HeadNodeConfig(ray_start_params={"log-color": "True"}, requests=Resources(cpu="1", mem="2Gi")),
worker_node_config=[
WorkerNodeConfig(
group_name="ray-group",
replicas=1,
requests=Resources(cpu="2", mem="2Gi"),
limits=Resources(cpu="3", mem="6Gi"),
)
],
runtime_env={"pip": ["numpy", "pandas"]}, # or runtime_env="./requirements.txt"
enable_autoscaling=True,
shutdown_after_job_finishes=True,
ttl_seconds_after_finished=3600,
)


# Lastly, define a workflow to call the Ray task.
# %%
@workflow
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