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[tune/sgd] Document func_trainable and add checkpoint context (#9739)

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Co-authored-by: krfricke <krfricke@users.noreply.github.com>
Co-authored-by: Amog Kamsetty <amogkam@users.noreply.github.com>
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Richard Liaw
2020-07-30 09:46:37 -07:00
committed by GitHub
co-authored by krfricke Amog Kamsetty
parent e540e425e4
commit 0c3b9ebeef
23 changed files with 619 additions and 452 deletions
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doc/source/tune/_tutorials/tune-distributed.rst
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@@ -213,16 +213,7 @@ In GCP, you can use the following configuration modification:
scheduling:
- preemptible: true
Spot instances may be removed suddenly while trials are still running. Often times this may be difficult to deal with when using other distributed hyperparameter optimization frameworks. Tune allows users to mitigate the effects of this by preserving the progress of your model training through checkpointing.
The easiest way to do this is to subclass the pre-defined ``Trainable`` class and implement ``save_checkpoint``, and ``load_checkpoint`` abstract methods, as seen in the example below:
.. literalinclude:: /../../python/ray/tune/examples/mnist_pytorch_trainable.py
:language: python
:start-after: __trainable_example_begin__
:end-before: __trainable_example_end__
This can then be used similarly to the Function API as before:
Spot instances may be removed suddenly while trials are still running. Often times this may be difficult to deal with when using other distributed hyperparameter optimization frameworks. Tune allows users to mitigate the effects of this by preserving the progress of your model training through :ref:`checkpointing <tune-function-checkpointing>`.
.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
:language: python