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Eric Liang
b6c42f96be
This adds (experimental) auto-scaling support for Ray clusters based on GCS load metrics. The auto-scaling algorithm is as follows: Based on current (instantaneous) load information, we compute the approximate number of "used workers". This is based on the bottleneck resource, e.g. if 8/8 GPUs are used in a 8-node cluster but all the CPUs are idle, the number of used nodes is still counted as 8. This number can also be fractional. We scale that number by 1 / target_utilization_fraction and round up to determine the target cluster size (subject to the max_workers constraint). The autoscaler control loop takes care of launching new nodes until the target cluster size is met. When a node is idle for more than idle_timeout_minutes, we remove it from the cluster if that would not drop the cluster size below min_workers. Note that we'll need to update the wheel in the example yaml file after this PR is merged.
Ray
===
.. image:: https://travis-ci.org/ray-project/ray.svg?branch=master
:target: https://travis-ci.org/ray-project/ray
.. image:: https://readthedocs.org/projects/ray/badge/?version=latest
:target: http://ray.readthedocs.io/en/latest/?badge=latest
|
Ray is a flexible, high-performance distributed execution framework.
Ray comes with libraries that accelerate deep learning and reinforcement learning development:
- `Ray.tune`_: Efficient Distributed Hyperparameter Search
- `Ray RLlib`_: A Composable and Scalable Reinforcement Learning Library
.. _`Ray.tune`: http://ray.readthedocs.io/en/latest/tune.html
.. _`Ray 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, see the instructions for `Ubuntu`_ and `Mac`_.
.. _`Ubuntu`: http://ray.readthedocs.io/en/latest/install-on-ubuntu.html
.. _`Mac`: http://ray.readthedocs.io/en/latest/install-on-macosx.html
Example Program
---------------
+------------------------------------------------+----------------------------------------------+
| **Basic Python** | **Distributed with Ray** |
+------------------------------------------------+----------------------------------------------+
|.. code:: python |.. code-block:: python |
| | |
| import time | import time |
| | import ray |
| | |
| | ray.init() |
| | |
| | @ray.remote |
| def f(): | def f(): |
| time.sleep(1) | time.sleep(1) |
| return 1 | return 1 |
| | |
| # Execute f serially. | # Execute f in parallel. |
| results = [f() for i in range(4)] | object_ids = [f.remote() for i in range(4)] |
| | results = ray.get(object_ids) |
+------------------------------------------------+----------------------------------------------+
More Information
----------------
- `Documentation`_
- `Tutorial`_
- `Blog`_
- `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 HotOS paper`: https://arxiv.org/abs/1703.03924
Getting Involved
----------------
- Ask questions on our mailing list `ray-dev@googlegroups.com`_.
- Please report bugs by submitting a `GitHub issue`_.
- Submit contributions using `pull requests`_.
.. _`ray-dev@googlegroups.com`: https://groups.google.com/forum/#!forum/ray-dev
.. _`GitHub issue`: https://github.com/ray-project/ray/issues
.. _`pull requests`: https://github.com/ray-project/ray/pulls