wehub-resource-sync
330 lines
10 KiB
ReStructuredText
330 lines
10 KiB
ReStructuredText
.. _vm-cluster-quick-start:
|
|
|
|
Getting Started
|
|
===============
|
|
|
|
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.
|
|
|
|
For launching a Ray cluster manually, you can refer to the :ref:`on-premise cluster setup <on-prem>` guide.
|
|
|
|
About the demo
|
|
--------------
|
|
|
|
This demo will walk through an end-to-end flow:
|
|
|
|
1. Create a (basic) Python application.
|
|
2. Launch a cluster on a cloud provider.
|
|
3. Run the application in the cloud.
|
|
|
|
Requirements
|
|
~~~~~~~~~~~~
|
|
|
|
To run this demo, you will need:
|
|
|
|
* Python installed on your development machine (typically your laptop), and
|
|
* an account at your preferred cloud provider (AWS, GCP, Azure, Aliyun, or vSphere).
|
|
|
|
Setup
|
|
~~~~~
|
|
|
|
Before we start, you will need to install some Python dependencies as follows:
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: Ray Team Supported
|
|
:sync: Ray Team Supported
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: AWS
|
|
:sync: AWS
|
|
|
|
.. code-block:: shell
|
|
|
|
$ pip install -U "ray[default]" boto3
|
|
|
|
.. tab-item:: Azure
|
|
:sync: Azure
|
|
|
|
.. code-block:: shell
|
|
|
|
$ pip install -U "ray[default]" azure-cli azure-core
|
|
|
|
.. tab-item:: GCP
|
|
:sync: GCP
|
|
|
|
.. code-block:: shell
|
|
|
|
$ pip install -U "ray[default]" google-api-python-client
|
|
|
|
.. tab-item:: Community Supported
|
|
:sync: Community Supported
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: Aliyun
|
|
:sync: Aliyun
|
|
|
|
.. code-block:: shell
|
|
|
|
$ pip install -U "ray[default]" aliyun-python-sdk-core aliyun-python-sdk-ecs
|
|
|
|
Aliyun Cluster Launcher Maintainers (GitHub handles): @zhuangzhuang131419, @chenk008
|
|
|
|
.. tab-item:: vSphere
|
|
:sync: vSphere
|
|
|
|
.. code-block:: shell
|
|
|
|
$ pip install -U "ray[default]"
|
|
|
|
vSphere Cluster Launcher Maintainers (GitHub handles): @roshankathawate, @ankitasonawane30, @VamshikShetty
|
|
|
|
|
|
Next, if you're not set up to use your cloud provider from the command line, you'll have to configure your credentials:
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: Ray Team Supported
|
|
:sync: Ray Team Supported
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: AWS
|
|
:sync: AWS
|
|
|
|
Configure your credentials in ``~/.aws/credentials`` as described in `the AWS docs <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html>`_.
|
|
|
|
.. tab-item:: Azure
|
|
:sync: Azure
|
|
|
|
Log in using ``az login``, then configure your credentials with ``az account set -s <subscription_id>``.
|
|
|
|
.. tab-item:: GCP
|
|
:sync: GCP
|
|
|
|
Set the ``GOOGLE_APPLICATION_CREDENTIALS`` environment variable as described in `the GCP docs <https://cloud.google.com/docs/authentication/getting-started>`_.
|
|
|
|
.. tab-item:: Community Supported
|
|
:sync: Community Supported
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: Aliyun
|
|
:sync: Aliyun
|
|
|
|
Obtain and set the AccessKey pair of the Aliyun account as described in `the docs <https://www.alibabacloud.com/help/en/doc-detail/175967.htm>`__.
|
|
|
|
Make sure to grant the necessary permissions to the RAM user and set the AccessKey pair in your cluster config file.
|
|
Refer to the provided `aliyun/example-full.yaml </ray/python/ray/autoscaler/aliyun/example-full.yaml>`__ for a sample cluster config.
|
|
|
|
.. tab-item:: vSphere
|
|
:sync: vSphere
|
|
|
|
Make sure Ray supervisor service is up and running as per `the Ray-on-VCF docs <https://github-vcf.devops.broadcom.net/vcf/vmray>`
|
|
|
|
|
|
Create a (basic) Python application
|
|
-----------------------------------
|
|
|
|
We will write a simple Python application that tracks the IP addresses of the machines that its tasks are executed on:
|
|
|
|
.. code-block:: python
|
|
|
|
from collections import Counter
|
|
import socket
|
|
import time
|
|
|
|
def f():
|
|
time.sleep(0.001)
|
|
# Return IP address.
|
|
return socket.gethostbyname("localhost")
|
|
|
|
ip_addresses = [f() for _ in range(10000)]
|
|
print(Counter(ip_addresses))
|
|
|
|
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})``.
|
|
|
|
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>`):
|
|
|
|
.. code-block:: python
|
|
|
|
from collections import Counter
|
|
import socket
|
|
import time
|
|
|
|
import ray
|
|
|
|
ray.init()
|
|
|
|
@ray.remote
|
|
def f():
|
|
time.sleep(0.001)
|
|
# Return IP address.
|
|
return socket.gethostbyname("localhost")
|
|
|
|
object_ids = [f.remote() for _ in range(10000)]
|
|
ip_addresses = ray.get(object_ids)
|
|
print(Counter(ip_addresses))
|
|
|
|
Finally, let's add some code to make the output more interesting:
|
|
|
|
.. code-block:: python
|
|
|
|
from collections import Counter
|
|
import socket
|
|
import time
|
|
|
|
import ray
|
|
|
|
ray.init()
|
|
|
|
print('''This cluster consists of
|
|
{} nodes in total
|
|
{} CPU resources in total
|
|
'''.format(len(ray.nodes()), ray.cluster_resources()['CPU']))
|
|
|
|
@ray.remote
|
|
def f():
|
|
time.sleep(0.001)
|
|
# Return IP address.
|
|
return socket.gethostbyname("localhost")
|
|
|
|
object_ids = [f.remote() for _ in range(10000)]
|
|
ip_addresses = ray.get(object_ids)
|
|
|
|
print('Tasks executed')
|
|
for ip_address, num_tasks in Counter(ip_addresses).items():
|
|
print(' {} tasks on {}'.format(num_tasks, ip_address))
|
|
|
|
Running ``python script.py`` should now output something like:
|
|
|
|
.. parsed-literal::
|
|
|
|
This cluster consists of
|
|
1 nodes in total
|
|
4.0 CPU resources in total
|
|
|
|
Tasks executed
|
|
10000 tasks on 127.0.0.1
|
|
|
|
Launch a cluster on a cloud provider
|
|
------------------------------------
|
|
|
|
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.
|
|
|
|
A minimal sample cluster configuration file looks as follows:
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: Ray Team Supported
|
|
:sync: Ray Team Supported
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: AWS
|
|
:sync: AWS
|
|
|
|
.. literalinclude:: ../../../../python/ray/autoscaler/aws/example-minimal.yaml
|
|
:language: yaml
|
|
|
|
.. tab-item:: Azure
|
|
:sync: Azure
|
|
|
|
.. code-block:: yaml
|
|
|
|
# An unique identifier for the head node and workers of this cluster.
|
|
cluster_name: minimal
|
|
|
|
# Cloud-provider specific configuration.
|
|
provider:
|
|
type: azure
|
|
location: westus2
|
|
resource_group: ray-cluster
|
|
|
|
# How Ray will authenticate with newly launched nodes.
|
|
auth:
|
|
ssh_user: ubuntu
|
|
# you must specify paths to matching private and public key pair files
|
|
# use `ssh-keygen -t rsa -b 4096` to generate a new ssh key pair
|
|
ssh_private_key: ~/.ssh/id_rsa
|
|
# changes to this should match what is specified in file_mounts
|
|
ssh_public_key: ~/.ssh/id_rsa.pub
|
|
|
|
.. tab-item:: GCP
|
|
:sync: GCP
|
|
|
|
.. code-block:: yaml
|
|
|
|
# A unique identifier for the head node and workers of this cluster.
|
|
cluster_name: minimal
|
|
|
|
# Cloud-provider specific configuration.
|
|
provider:
|
|
type: gcp
|
|
region: us-west1
|
|
|
|
.. tab-item:: Community Supported
|
|
:sync: Community Supported
|
|
|
|
.. tab-set::
|
|
|
|
.. tab-item:: Aliyun
|
|
:sync: Aliyun
|
|
|
|
Please refer to `example-full.yaml </ray/python/ray/autoscaler/aliyun/example-full.yaml>`__.
|
|
|
|
Make sure your account balance is not less than 100 RMB, otherwise you will receive the error `InvalidAccountStatus.NotEnoughBalance`.
|
|
|
|
.. tab-item:: vSphere
|
|
:sync: vSphere
|
|
|
|
.. literalinclude:: ../../../../python/ray/autoscaler/vsphere/example-minimal.yaml
|
|
:language: yaml
|
|
|
|
|
|
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>`.
|
|
|
|
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:
|
|
|
|
.. code-block:: shell
|
|
|
|
$ ray up -y config.yaml
|
|
|
|
Running applications on a Ray Cluster
|
|
-------------------------------------
|
|
|
|
We are now ready to execute an application on our Ray Cluster.
|
|
``ray.init()`` will now automatically connect to the newly created cluster.
|
|
|
|
As a quick example, we execute a Python command on the Ray Cluster that connects to Ray and exits:
|
|
|
|
.. code-block:: shell
|
|
|
|
$ ray exec config.yaml 'python -c "import ray; ray.init()"'
|
|
2022-08-10 11:23:17,093 INFO worker.py:1312 -- Connecting to existing Ray cluster at address: <remote IP address>:6379...
|
|
2022-08-10 11:23:17,097 INFO worker.py:1490 -- Connected to Ray cluster.
|
|
|
|
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.
|
|
|
|
.. code-block:: shell
|
|
|
|
# From a remote client:
|
|
$ ray attach config.yaml
|
|
|
|
# Now on the head node...
|
|
$ python -c "import ray; ray.init()"
|
|
|
|
For a full reference on the Ray Cluster CLI tools, please refer to :ref:`the cluster commands reference <cluster-commands>`.
|
|
|
|
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!
|
|
|
|
Deleting a Ray Cluster
|
|
----------------------
|
|
|
|
To shut down your cluster, run the following command:
|
|
|
|
.. code-block:: shell
|
|
|
|
$ ray down -y config.yaml
|