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This adds the structure described here, namely adding a new section under Ray Clusters which is focused on running applications on Ray clusters. Signed-off-by: Cade Daniel <cade@anyscale.com> Co-authored-by: Stephanie Wang <swang@cs.berkeley.edu>
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3.3 KiB
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88 lines
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.. warning::
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This page is under construction!
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.. _jobs-overview-under-construction:
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===========================
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Ray Job Submission Overview
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===========================
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.. note::
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This component is in **beta**. APIs may change before becoming stable. This feature requires a full installation of Ray using ``pip install "ray[default]"``.
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Ray Job submission is a mechanism to submit locally developed and tested applications to a remote Ray cluster. It simplifies the experience of packaging, deploying, and managing a Ray application.
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Jump to the :ref:`API Reference<ray-job-submission-api-ref>`, or continue reading for a quick overview.
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Concepts
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--------
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- **Job**: A Ray application submitted to a Ray cluster for execution. Consists of (1) an entrypoint command and (2) a :ref:`runtime environment<runtime-environments>`, which may contain file and package dependencies.
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- **Job Lifecycle**: When a job is submitted, it runs once to completion or failure. Retries or different runs with different parameters should be handled by the submitter. Jobs are bound to the lifetime of a Ray cluster, so if the cluster goes down, all running jobs on that cluster will be terminated.
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- **Job Manager**: An entity external to the Ray cluster that manages the lifecycle of a job (scheduling, killing, polling status, getting logs, and persisting inputs/outputs), and potentially also manages the lifecycle of Ray clusters. Can be any third-party framework with these abilities, such as Apache Airflow or Kubernetes Jobs.
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Quick Start Example
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-------------------
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Let's start with a sample job that can be run locally. The following script uses Ray APIs to increment a counter and print its value, and print the version of the ``requests`` module it's using:
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.. code-block:: python
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# script.py
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import ray
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import requests
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ray.init()
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@ray.remote
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class Counter:
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def __init__(self):
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self.counter = 0
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def inc(self):
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self.counter += 1
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def get_counter(self):
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return self.counter
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counter = Counter.remote()
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for _ in range(5):
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ray.get(counter.inc.remote())
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print(ray.get(counter.get_counter.remote()))
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print(requests.__version__)
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Put this file in a local directory of your choice, with filename ``script.py``, so your working directory will look like:
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.. code-block:: bash
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| your_working_directory ("./")
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| ├── script.py
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Next, start a local Ray cluster:
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.. code-block:: bash
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❯ ray start --head
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Local node IP: 127.0.0.1
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INFO services.py:1360 -- View the Ray dashboard at http://127.0.0.1:8265
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Note the address and port returned in the terminal---this will be where we submit job requests to, as explained further in the examples below. If you do not see this, ensure the Ray Dashboard is installed by running :code:`pip install "ray[default]"`.
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At this point, the job is ready to be submitted by one of the :ref:`Ray Job APIs<ray-job-apis>`.
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Continue on to see examples of running and interacting with this sample job.
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Job Submission Architecture
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----------------------------
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The following diagram shows the underlying structure and steps for each submitted job.
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.. image:: https://raw.githubusercontent.com/ray-project/images/master/docs/job/job_submission_arch_v2.png
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