入门指南#

这个快速入门展示了 Ray 集群的功能。使用 Ray 集群，我们将一个设计为在笔记本电脑上运行的示例应用程序扩展到云端。Ray 只需几个命令即可启动集群并扩展 Python。

要手动启动 Ray 集群，您可以参考本地集群设置指南。

创建一个（基本的）Python应用程序#

我们将编写一个简单的Python应用程序，用于跟踪在其任务执行的机器的IP地址：

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))

将此应用程序保存为 script.py 并通过运行命令 python script.py 来执行它。该应用程序应运行10秒并输出类似于 Counter({'127.0.0.1': 10000}) 的内容。

通过一些小的改动，我们可以让这个应用在 Ray 上运行（关于如何做到这一点，请参考 Ray 核心演练）：

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))

最后，让我们添加一些代码，使输出更有趣：

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))

运行 python script.py 现在应该输出类似的内容：

This cluster consists of
    1 nodes in total
    4.0 CPU resources in total

Tasks executed
    10000 tasks on 127.0.0.1

在云服务提供商上启动一个集群#

要启动一个 Ray 集群，首先我们需要定义集群配置。集群配置是在一个 YAML 文件中定义的，该文件将由集群启动器用于启动头节点，并由自动缩放器用于启动工作节点。

一个最小的集群配置文件示例如下：

Ray 团队支持

AWS

# An unique identifier for the head node and workers of this cluster.
cluster_name: aws-example-minimal

# Cloud-provider specific configuration.
provider:
    type: aws
    region: us-west-2

# The maximum number of workers nodes to launch in addition to the head
# node.
max_workers: 3

# Tell the autoscaler the allowed node types and the resources they provide.
# The key is the name of the node type, which is for debugging purposes.
# The node config specifies the launch config and physical instance type.
available_node_types:
    ray.head.default:
        # The node type's CPU and GPU resources are auto-detected based on AWS instance type.
        # If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.
        # You can also set custom resources.
        # For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set
        # resources: {"CPU": 1, "GPU": 1, "custom": 5}
        resources: {}
        # Provider-specific config for this node type, e.g., instance type. By default
        # Ray auto-configures unspecified fields such as SubnetId and KeyName.
        # For more documentation on available fields, see
        # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
        node_config:
            InstanceType: m5.large
    ray.worker.default:
        # The minimum number of worker nodes of this type to launch.
        # This number should be >= 0.
        min_workers: 3
        # The maximum number of worker nodes of this type to launch.
        # This parameter takes precedence over min_workers.
        max_workers: 3
        # The node type's CPU and GPU resources are auto-detected based on AWS instance type.
        # If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.
        # You can also set custom resources.
        # For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set
        # resources: {"CPU": 1, "GPU": 1, "custom": 5}
        resources: {}
        # Provider-specific config for this node type, e.g., instance type. By default
        # Ray auto-configures unspecified fields such as SubnetId and KeyName.
        # For more documentation on available fields, see
        # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
        node_config:
            InstanceType: m5.large

GCP

# 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

社区支持

Azure

# 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

阿里云

请参考 example-full.yaml。

确保您的账户余额不少于100元，否则您将收到错误 InvalidAccountStatus.NotEnoughBalance。

vSphere

# An unique identifier for the head node and workers of this cluster.
cluster_name: minimal

# Cloud-provider specific configuration.
provider:
    type: vsphere

将此配置文件保存为 config.yaml。您可以在配置文件中指定更多详细信息：要使用的实例类型、要启动的最小和最大工作节点数、自动扩展策略、要同步的文件等。有关可用配置属性的完整参考，请参阅集群 YAML 配置选项参考。

在定义了我们的配置之后，我们将使用 Ray 集群启动器在云上启动一个集群，创建一个指定的“头节点”和工作节点。要启动 Ray 集群，我们将使用 Ray CLI。运行以下命令：

$ ray up -y config.yaml

在 Ray 集群上运行应用程序#

我们现在准备好在我们创建的Ray集群上执行一个应用程序。ray.init() 现在将自动连接到新创建的集群。

作为一个快速示例，我们在 Ray 集群上执行一个连接到 Ray 并退出的 Python 命令：

$ 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.

你也可以选择使用 ray attach 获取远程shell并在集群上直接运行命令。此命令将创建一个到Ray集群头节点的SSH连接。

# From a remote client:
$ ray attach config.yaml

# Now on the head node...
$ python -c "import ray; ray.init()"

有关 Ray 集群 CLI 工具的完整参考，请参阅集群命令参考。

虽然这些工具对于在 Ray 集群上的临时执行很有用，但在 Ray 集群上执行应用程序的推荐方法是使用 Ray Jobs。查看快速入门指南以开始使用！

删除一个 Ray 集群#

要关闭您的集群，请运行以下命令：

$ ray down -y config.yaml

入门指南#

关于演示#

要求#

设置#

创建一个（基本的）Python应用程序#

在云服务提供商上启动一个集群#

在 Ray 集群上运行应用程序#

删除一个 Ray 集群#