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Signed-off-by: Yi Cheng 74173148+iycheng@users.noreply.github.com Why are these changes needed? This PR update workflow doc to reflect the recent change. Focusing on position change and others.
32 lines
1.4 KiB
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32 lines
1.4 KiB
ReStructuredText
.. _workflows:
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Ray Workflows: Durable Ray Task Graphs
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======================================
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.. warning::
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Ray Workflows is available as **alpha** in Ray 2.0+. Expect rough corners and
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for its APIs and storage format to change. Please file feature requests and
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bug reports on GitHub Issues or join the discussion on the
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`Ray Slack <https://forms.gle/9TSdDYUgxYs8SA9e8>`__.
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Ray Workflows implements high-performance, *durable* application workflows using
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Ray tasks as the underlying execution engine. It enables task-based Ray jobs
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to seamlessly resume execution even in the case of entire-cluster failure.
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Why Ray Workflows?
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------------------
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**Flexibility:** Combine the flexibility of Ray's dynamic task graphs with
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strong durability guarantees. Branch or loop conditionally based on runtime
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data. Use Ray distributed libraries seamlessly within workflow tasks.
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**Performance:** Ray Workflows offers sub-second overheads for task launch and
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supports workflows with hundreds of thousands of tasks. Take advantage of the
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Ray object store to pass distributed datasets between tasks with zero-copy
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overhead.
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You might find that Ray Workflows is *lower level* compared to engines such as
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`AirFlow <https://www.astronomer.io/blog/airflow-ray-data-science-story>`__
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(which can also run on Ray). This is because Ray Workflows focuses more on core
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durability primitives as opposed to tools and integrations.
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