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https://github.com/vale981/ray
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78 lines
2.8 KiB
ReStructuredText
78 lines
2.8 KiB
ReStructuredText
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Ray Projects (Experimental)
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===========================
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Ray projects make it easy to package a Ray application so it can be
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rerun later in the same environment. They allow for the sharing and
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reliable reuse of existing code.
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Quick start (CLI)
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-----------------
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.. code-block:: bash
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# Creates a project in the current directory. It will create a
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# project.yaml defining the code and environment and a cluster.yaml
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# describing the cluster configuration. Both will be created in the
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# .rayproject subdirectory of the current directory.
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$ ray project create <project-name>
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# Create a new session from the given project.
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# Launch a cluster and run the appropriate command.
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$ ray session start
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# Open a console for the given session.
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$ ray session attach
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# Stop the given session and all of its worker nodes. The nodes/clusters
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# are not actually terminated.
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$ ray session stop
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Examples
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--------
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- `Open Tacotron <https://github.com/ray-project/ray/blob/master/python/ray/projects/examples/open-tacotron/.rayproject/project.yaml>`__:
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A TensorFlow implementation of Google's Tacotron speech synthesis with pre-trained model (unofficial)
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- `PyTorch Transformers <https://github.com/ray-project/ray/blob/master/python/ray/projects/examples/pytorch-transformers/.rayproject/project.yaml>`__:
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A library of state-of-the-art pretrained models for Natural Language Processing (NLP)
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Project file format (project.yaml)
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----------------------------------
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A project file contains everything required to run a project.
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This includes a cluster configuration, the environment and dependencies
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for the application, and the specific inputs used to run the project.
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Here is an example for a minimal project format:
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.. code-block:: yaml
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name: test-project
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description: "This is a simple test project"
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repo: https://github.com/ray-project/ray
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# Cluster to be instantiated by default when starting the project.
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cluster: .rayproject/cluster.yaml
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# Commands/information to build the environment, once the cluster is
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# instantiated. This can include the versions of python libraries etc.
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# It can be specified as a Python requirements.txt, a conda environment,
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# a Dockerfile, or a shell script to run to set up the libraries.
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environment:
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requirements: requirements.txt
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# List of commands that can be executed once the cluster is instantiated
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# and the environment is set up.
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# A command can also specify a cluster that overwrites the default cluster.
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commands:
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- name: test
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command: python test.py
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Project files have to adhere to the following schema:
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.. jsonschema:: ../../python/ray/projects/schema.json
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Cluster file format (cluster.yaml)
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----------------------------------
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This is the same as for the autoscaler, see
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`Cluster Launch page <autoscaling.html>`_.
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