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docs/release-procedure.md

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Distributed follows a similar procedure for releasing as the core Dask project.
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See https://github.com/dask/dask/blob/master/docs/release-procedure.md for instructions.
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See https://github.com/dask/dask/blob/main/docs/release-procedure.md for instructions.

docs/source/priority.rst

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3. **Graph Structure**: Within any given computation (a compute or persist
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call) Dask orders tasks in such a way as to minimize the memory-footprint
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of the computation. This is discussed in more depth in the
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`task ordering documentation <https://github.com/dask/dask/blob/master/dask/order.py>`_.
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`task ordering documentation <https://github.com/dask/dask/blob/main/dask/order.py>`_.
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If multiple tasks each have exactly the same priorities outlined above, then
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the order in which tasks arrive at a worker, in a last in first out manner,

docs/source/scheduling-policies.rst

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paths and nodes with many children. The actual algorithms used are somewhat
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more complex and are described in detail in `dask/order.py`_
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.. _`dask/order.py`: https://github.com/dask/dask/blob/master/dask/order.py
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.. _`dask/order.py`: https://github.com/dask/dask/blob/main/dask/order.py
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Initial Task Placement
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~~~~~~~~~~~~~~~~~~~~~~
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computing deeply before broadly, preferring critical paths, preferring
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nodes with many dependencies, etc.. This is the same logic used by the
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single-machine scheduler and lives in `dask/order.py
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<https://github.com/dask/dask/blob/master/dask/order.py>`_.
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<https://github.com/dask/dask/blob/main/dask/order.py>`_.
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2. When the graph reaches the scheduler the scheduler changes each of these
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numeric priorities into a tuple of two numbers, the first of which is an
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increasing counter, the second of which is the client-generated priority

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