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![]() ## What do these changes do? It saves checkpoint if needed regardless of what the scheduler have returned. Until now, it have not saved the checkpoint when scheduler returned TrialScheduler.PAUSE, which caused PopulationBasedTraining preventing to save any checkpoints in certain cases. See issue #4041 for more details. ## Related issue number #4041 |
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.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png .. image:: https://travis-ci.com/ray-project/ray.svg?branch=master :target: https://travis-ci.com/ray-project/ray .. image:: https://readthedocs.org/projects/ray/badge/?version=latest :target: http://ray.readthedocs.io/en/latest/?badge=latest .. image:: https://img.shields.io/badge/pypi-0.6.3-blue.svg :target: https://pypi.org/project/ray/ | **Ray is a flexible, high-performance distributed execution framework.** Ray is easy to install: ``pip install ray`` Example Use ----------- +------------------------------------------------+----------------------------------------------------+ | **Basic Python** | **Distributed with Ray** | +------------------------------------------------+----------------------------------------------------+ |.. code-block:: python |.. code-block:: python | | | | | # Execute f serially. | # Execute f in parallel. | | | | | | @ray.remote | | def f(): | def f(): | | time.sleep(1) | time.sleep(1) | | return 1 | return 1 | | | | | | | | | ray.init() | | results = [f() for i in range(4)] | results = ray.get([f.remote() for i in range(4)]) | +------------------------------------------------+----------------------------------------------------+ Ray comes with libraries that accelerate deep learning and reinforcement learning development: - `Tune`_: Hyperparameter Optimization Framework - `RLlib`_: Scalable Reinforcement Learning - `Distributed Training <http://ray.readthedocs.io/en/latest/distributed_sgd.html>`__ .. _`Tune`: http://ray.readthedocs.io/en/latest/tune.html .. _`RLlib`: http://ray.readthedocs.io/en/latest/rllib.html Installation ------------ Ray can be installed on Linux and Mac with ``pip install ray``. To build Ray from source or to install the nightly versions, see the `installation documentation`_. .. _`installation documentation`: http://ray.readthedocs.io/en/latest/installation.html More Information ---------------- - `Documentation`_ - `Tutorial`_ - `Blog`_ - `Ray paper`_ - `Ray HotOS paper`_ .. _`Documentation`: http://ray.readthedocs.io/en/latest/index.html .. _`Tutorial`: https://github.com/ray-project/tutorial .. _`Blog`: https://ray-project.github.io/ .. _`Ray paper`: https://arxiv.org/abs/1712.05889 .. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924 Getting Involved ---------------- - `ray-dev@googlegroups.com`_: For discussions about development or any general questions. - `StackOverflow`_: For questions about how to use Ray. - `GitHub Issues`_: For reporting bugs and feature requests. - `Pull Requests`_: For submitting code contributions. .. _`ray-dev@googlegroups.com`: https://groups.google.com/forum/#!forum/ray-dev .. _`GitHub Issues`: https://github.com/ray-project/ray/issues .. _`StackOverflow`: https://stackoverflow.com/questions/tagged/ray .. _`Pull Requests`: https://github.com/ray-project/ray/pulls