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* Google Cloud Platform scaffolding * Add minimal gcp config example * Add googleapiclient discoveries, update gcp.config constants * Rename and update gcp.config key pair name function * Implement gcp.config._configure_project * Fix the create project get project flow * Implement gcp.config._configure_iam_role * Implement service account iam binding * Implement gcp.config._configure_key_pair * Implement rsa key pair generation * Implement gcp.config._configure_subnet * Save work-in-progress gcp.config._configure_firewall_rules. These are likely to be not needed at all. Saving them if we happen to need them later. * Remove unnecessary firewall configuration * Update example-minimal.yaml configuration * Add new wait_for_compute_operation, rename old wait_for_operation * Temporarily rename autoscaler tags due to gcp incompatibility * Implement initial gcp.node_provider.nodes * Still missing filter support * Implement initial gcp.node_provider.create_node * Implement another compute wait operation (wait_For_compute_zone_operation). TODO: figure out if we can remove the function. * Implement initial gcp.node_provider._node and node status functions * Implement initial gcp.node_provider.terminate_node * Implement node tagging and ip getter methods for nodes * Temporarily rename tags due to gcp incompatibility * Tiny tweaks for autoscaler.updater * Remove unused config from gcp node_provider * Add new example-full example to gcp, update load_gcp_example_config * Implement label filtering for gcp.node_provider.nodes * Revert unnecessary change in ssh command * Revert "Temporarily rename tags due to gcp incompatibility" This reverts commit e2fe634c5d11d705c0f5d3e76c80c37394bb23fb. * Revert "Temporarily rename autoscaler tags due to gcp incompatibility" This reverts commit c938ee435f4b75854a14e78242ad7f1d1ed8ad4b. * Refactor autoscaler tagging to support multiple tag specs * Remove missing cryptography imports * Update quote function import * Fix threading issue in gcp.config with the compute discovery object * Add gcs support for log_sync * Fix the labels/tags naming discrepancy * Add expanduser to file_mounts hashing * Fix gcp.node_provider.internal_ip * Add uuid to node name * Remove 'set -i' from updater ssh command * Also add TODO with the context and reason for the change. * Update ssh key creation in autoscaler.gcp.config * Fix wait_for_compute_zone_operation's threading issue Google discovery api's compute object is not thread safe, and thus needs to be recreated for each thread. This moves the `wait_for_compute_zone_operation` under `autoscaler.gcp.config`, and adds compute as its argument. * Address pr feedback from @ericl * Expand local file mount paths in NodeUpdater * Add ssh_user name to key names * Update updater ssh to attempt 'set -i' and fall back if that fails * Update gcp/example-full.yaml * Fix wait crm operation in gcp.config * Update gcp/example-minimal.yaml to match aws/example-minimal.yaml * Fix gcp/example-full.yaml comment indentation * Add gcp/example-full.yaml to setup files * Update example-full.yaml command * Revert "Refactor autoscaler tagging to support multiple tag specs" This reverts commit 9cf48409ca2e5b66f800153853072c706fa502f6. * Update tag spec to only use characters [0-9a-z_-] * Change the tag values to conform gcp spec * Add project_id in the ssh key name * Replace '_' with '-' in autoscaler tag names * Revert "Update updater ssh to attempt 'set -i' and fall back if that fails" This reverts commit 23a0066c5254449e49746bd5e43b94b66f32bfb4. * Revert "Remove 'set -i' from updater ssh command" This reverts commit 5fa034cdf79fa7f8903691518c0d75699c630172. * Add fallback to `set -i` in force_interactive command * Update autoscaler tests to match current implementation * Update GCPNodeProvider.create_node to include hash in instance name * Add support for creating multiple instance on one create_node call * Clean TODOs * Update styles * Replace single quotes with double quotes * Some minor indentation fixes etc. * Remove unnecessary comment. Fix indentation. * Yapfify files that fail flake8 test * Yapfify more files * Update project_id handling in gcp node provider * temporary yapf mod * Revert "temporary yapf mod" This reverts commit b6744e4e15d4d936d1a14f4bf155ed1d3bb14126. * Fix autoscaler/updater.py lint error, remove unused variable
Implement object table notification subscriptions and switch to using Redis modules for object table. (#134)
Ray
===
.. 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
|
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:
- `Ray Tune`_: Hyperparameter Optimization Framework
- `Ray RLlib`_: Scalable Reinforcement Learning
.. _`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 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
----------------
- 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
Description
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
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