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Take out the CLI reference from the core API subsection. It follows the same CLI reference pattern as other library (e.g., Serve has Serve CLI under Serve API section).
246 lines
7.8 KiB
ReStructuredText
246 lines
7.8 KiB
ReStructuredText
.. include:: /_includes/clusters/announcement.rst
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.. _vm-cluster-quick-start:
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Getting Started
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===============
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This quick start demonstrates the capabilities of the Ray cluster. Using the Ray cluster, we'll take a sample application designed to run on a laptop and scale it up in the cloud. Ray will launch clusters and scale Python with just a few commands.
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For launching a Ray cluster manually, you can refer to the :ref:`on-premise cluster setup <on-prem>` guide.
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About the demo
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--------------
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This demo will walk through an end-to-end flow:
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1. Create a (basic) Python application.
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2. Launch a cluster on a cloud provider.
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3. Run the application in the cloud.
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Requirements
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~~~~~~~~~~~~
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To run this demo, you will need:
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* Python installed on your development machine (typically your laptop), and
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* an account at your preferred cloud provider (AWS, Azure or GCP).
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Setup
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~~~~~
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Before we start, you will need to install some Python dependencies as follows:
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.. tabbed:: AWS
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.. code-block:: shell
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$ pip install -U "ray[default]" boto3
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.. tabbed:: Azure
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.. code-block:: shell
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$ pip install -U "ray[default]" azure-cli azure-core
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.. tabbed:: GCP
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.. code-block:: shell
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$ pip install -U "ray[default]" google-api-python-client
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Next, if you're not set up to use your cloud provider from the command line, you'll have to configure your credentials:
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.. tabbed:: AWS
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Configure your credentials in ``~/.aws/credentials`` as described in `the AWS docs <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html>`_.
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.. tabbed:: Azure
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Log in using ``az login``, then configure your credentials with ``az account set -s <subscription_id>``.
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.. tabbed:: GCP
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Set the ``GOOGLE_APPLICATION_CREDENTIALS`` environment variable as described in `the GCP docs <https://cloud.google.com/docs/authentication/getting-started>`_.
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Create a (basic) Python application
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-----------------------------------
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We will write a simple Python application that tracks the IP addresses of the machines that its tasks are executed on:
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.. code-block:: python
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from collections import Counter
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import socket
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import time
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def f():
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time.sleep(0.001)
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# Return IP address.
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return socket.gethostbyname(socket.gethostname())
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ip_addresses = [f() for _ in range(10000)]
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print(Counter(ip_addresses))
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Save this application as ``script.py`` and execute it by running the command ``python script.py``. The application should take 10 seconds to run and output something similar to ``Counter({'127.0.0.1': 10000})``.
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With some small changes, we can make this application run on Ray (for more information on how to do this, refer to :ref:`the Ray Core Walkthrough <core-walkthrough>`):
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.. code-block:: python
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from collections import Counter
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import socket
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import time
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import ray
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ray.init()
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@ray.remote
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def f():
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time.sleep(0.001)
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# Return IP address.
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return socket.gethostbyname(socket.gethostname())
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object_ids = [f.remote() for _ in range(10000)]
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ip_addresses = ray.get(object_ids)
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print(Counter(ip_addresses))
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Finally, let's add some code to make the output more interesting:
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.. code-block:: python
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from collections import Counter
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import socket
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import time
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import ray
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ray.init()
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print('''This cluster consists of
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{} nodes in total
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{} CPU resources in total
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'''.format(len(ray.nodes()), ray.cluster_resources()['CPU']))
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@ray.remote
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def f():
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time.sleep(0.001)
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# Return IP address.
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return socket.gethostbyname(socket.gethostname())
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object_ids = [f.remote() for _ in range(10000)]
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ip_addresses = ray.get(object_ids)
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print('Tasks executed')
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for ip_address, num_tasks in Counter(ip_addresses).items():
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print(' {} tasks on {}'.format(num_tasks, ip_address))
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Running ``python script.py`` should now output something like:
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.. parsed-literal::
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This cluster consists of
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1 nodes in total
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4.0 CPU resources in total
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Tasks executed
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10000 tasks on 127.0.0.1
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Launch a cluster on a cloud provider
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------------------------------------
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To start a Ray Cluster, first we need to define the cluster configuration. The cluster configuration is defined within a YAML file that will be used by the Cluster Launcher to launch the head node, and by the Autoscaler to launch worker nodes.
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A minimal sample cluster configuration file looks as follows:
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.. tabbed:: AWS
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.. code-block:: yaml
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# An unique identifier for the head node and workers of this cluster.
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cluster_name: minimal
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# Cloud-provider specific configuration.
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provider:
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type: aws
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region: us-west-2
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.. tabbed:: Azure
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.. code-block:: yaml
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# An unique identifier for the head node and workers of this cluster.
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cluster_name: minimal
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# Cloud-provider specific configuration.
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provider:
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type: azure
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location: westus2
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resource_group: ray-cluster
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# How Ray will authenticate with newly launched nodes.
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auth:
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ssh_user: ubuntu
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# you must specify paths to matching private and public key pair files
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# use `ssh-keygen -t rsa -b 4096` to generate a new ssh key pair
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ssh_private_key: ~/.ssh/id_rsa
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# changes to this should match what is specified in file_mounts
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ssh_public_key: ~/.ssh/id_rsa.pub
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.. tabbed:: GCP
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.. code-block:: yaml
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# A unique identifier for the head node and workers of this cluster.
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cluster_name: minimal
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# Cloud-provider specific configuration.
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provider:
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type: gcp
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region: us-west1
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Save this configuration file as ``config.yaml``. You can specify a lot more details in the configuration file: instance types to use, minimum and maximum number of workers to start, autoscaling strategy, files to sync, and more. For a full reference on the available configuration properties, please refer to the :ref:`cluster YAML configuration options reference <cluster-config>`.
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After defining our configuration, we will use the Ray cluster launcher to start a cluster on the cloud, creating a designated "head node" and worker nodes. To start the Ray cluster, we will use the :ref:`Ray CLI <ray-cluster-cli>`. Run the following command:
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.. code-block:: shell
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$ ray up -y config.yaml
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Running applications on a Ray Cluster
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-------------------------------------
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We are now ready to execute an application on our Ray Cluster.
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``ray.init()`` will now automatically connect to the newly created cluster.
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As a quick example, we execute a Python command on the Ray Cluster that connects to Ray and exits:
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.. code-block:: shell
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$ ray exec config.yaml 'python -c "import ray; ray.init()"'
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2022-08-10 11:23:17,093 INFO worker.py:1312 -- Connecting to existing Ray cluster at address: <remote IP address>:6379...
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2022-08-10 11:23:17,097 INFO worker.py:1490 -- Connected to Ray cluster.
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You can also optionally get a remote shell using ``ray attach`` and run commands directly on the cluster. This command will create an SSH connection to the head node of the Ray Cluster.
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.. code-block:: shell
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# From a remote client:
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$ ray attach config.yaml
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# Now on the head node...
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$ python -c "import ray; ray.init()"
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For a full reference on the Ray Cluster CLI tools, please refer to :ref:`the cluster commands reference <cluster-commands>`.
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While these tools are useful for ad-hoc execution on the Ray Cluster, the recommended way to execute an application on a Ray Cluster is to use :ref:`Ray Jobs <jobs-quickstart>`. Check out the :ref:`quickstart guide <jobs-quickstart>` to get started!
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Deleting a Ray Cluster
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----------------------
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To shut down your cluster, run the following command:
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.. code-block:: shell
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$ ray down -y config.yaml
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