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c8f207f746
This PR makes a number of major overhauls to the Ray core docs:
Add a key-concepts section for {Tasks, Actors, Objects, Placement Groups, Env Deps}.
Re-org the user guide to align with key concepts.
Rewrite the walkthrough to link to mini-walkthroughs in the key concept sections.
Minor tweaks and additional transition material.
56 lines
1.7 KiB
ReStructuredText
56 lines
1.7 KiB
ReStructuredText
Nested Remote Functions
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=======================
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Remote functions can call other remote functions, resulting in nested tasks.
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For example, consider the following.
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.. code:: python
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@ray.remote
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def f():
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return 1
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@ray.remote
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def g():
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# Call f 4 times and return the resulting object refs.
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return [f.remote() for _ in range(4)]
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@ray.remote
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def h():
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# Call f 4 times, block until those 4 tasks finish,
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# retrieve the results, and return the values.
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return ray.get([f.remote() for _ in range(4)])
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Then calling ``g`` and ``h`` produces the following behavior.
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.. code:: python
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>>> ray.get(g.remote())
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[ObjectRef(b1457ba0911ae84989aae86f89409e953dd9a80e),
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ObjectRef(7c14a1d13a56d8dc01e800761a66f09201104275),
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ObjectRef(99763728ffc1a2c0766a2000ebabded52514e9a6),
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ObjectRef(9c2f372e1933b04b2936bb6f58161285829b9914)]
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>>> ray.get(h.remote())
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[1, 1, 1, 1]
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**One limitation** is that the definition of ``f`` must come before the
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definitions of ``g`` and ``h`` because as soon as ``g`` is defined, it
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will be pickled and shipped to the workers, and so if ``f`` hasn't been
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defined yet, the definition will be incomplete.
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Yielding Resources
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------------------
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Consider the following remote function.
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.. code-block:: python
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@ray.remote(num_cpus=1, num_gpus=1)
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def g():
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return ray.get(f.remote())
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When a ``g`` task is executing, it will release its CPU resources when it gets
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blocked in the call to ``ray.get``. It will reacquire the CPU resources when
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``ray.get`` returns. It will retain its GPU resources throughout the lifetime of
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the task because the task will most likely continue to use GPU memory.
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