在 Azure Batch 上运行容器应用程序Run container applications on Azure Batch

可以通过 Azure Batch 在 Azure 上运行和缩放大量批处理计算作业。Azure Batch lets you run and scale large numbers of batch computing jobs on Azure. Batch 任务可直接在 Batch 池中的虚拟机（节点）上运行；但也可以设置一个 Batch 池，以便在节点上的 Docker 兼容容器中运行任务。Batch tasks can run directly on virtual machines (nodes) in a Batch pool, but you can also set up a Batch pool to run tasks in Docker-compatible containers on the nodes. 本文介绍如何创建支持运行容器任务的计算节点池，然后在池中运行容器任务。This article shows you how to create a pool of compute nodes that support running container tasks, and then run container tasks on the pool.

此处的代码示例使用 Batch .NET 和 Python SDK。The code examples here use the Batch .NET and Python SDKs. 也可以使用其他 Batch SDK 和工具，包括 Azure 门户，来创建支持容器的 Batch 池，以及运行容器任务。You can also use other Batch SDKs and tools, including the Azure portal, to create container-enabled Batch pools and to run container tasks.

为何使用容器？Why use containers?

使用容器可以方便地运行 Batch 任务，无需管理环境和依赖项即可运行应用程序。Using containers provides an easy way to run Batch tasks without having to manage an environment and dependencies to run applications. 容器将应用程序部署为轻量级、可移植、自给自足的单元，可以在各种不同的环境中运行。Containers deploy applications as lightweight, portable, self-sufficient units that can run in several different environments. 例如，在本地构建和测试容器，然后将容器映像上传到 Azure 或其他位置的注册表中。For example, build and test a container locally, then upload the container image to a registry in Azure or elsewhere. 容器部署模型可确保始终正确安装和配置应用程序的运行时环境，而不考虑在何处托管应用程序。The container deployment model ensures that the runtime environment of your application is always correctly installed and configured wherever you host the application. Batch 中基于容器的任务也可利用非容器任务的功能，包括应用程序包以及资源文件和输出文件的管理。Container-based tasks in Batch can also take advantage of features of non-container tasks, including application packages and management of resource files and output files.

先决条件Prerequisites

读者应熟悉容器的概念，并知道如何创建 Batch 池和作业。You should be familiar with container concepts and how to create a Batch pool and job.

  • SDK 版本 ：Batch SDK 支持到以下版本为止的容器映像：SDK versions : The Batch SDKs support container images as of the following versions:

    • Batch REST API 版本 2017-09-01.6.0Batch REST API version 2017-09-01.6.0
    • Batch .NET SDK 版本 8.0.0Batch .NET SDK version 8.0.0
    • Batch Python SDK 版本 4.0Batch Python SDK version 4.0
    • Batch Java SDK 版本 3.0Batch Java SDK version 3.0
    • Batch Node.js SDK 版本 3.0Batch Node.js SDK version 3.0

  • 帐户 ：在 Azure 订阅中，需要创建 Batch 帐户和 Azure 存储帐户（后者为可选）。Accounts : In your Azure subscription, you need to create a Batch account and optionally an Azure Storage account.

  • 受支持的 VM 映像 ：容器仅在使用来自映像的虚拟机配置创建的池中受支持，有关详细信息，请参阅下一节“受支持的虚拟机映像”。A supported VM image : Containers are only supported in pools created with the Virtual Machine Configuration, from images detailed in the following section, "Supported virtual machine images." 如果提供自定义映像，请参阅以下部分所述的注意事项，以及使用托管的自定义映像创建虚拟机池中所述的要求。If you provide a custom image, see the considerations in the following section and the requirements in Use a managed custom image to create a pool of virtual machines.

请记住以下限制：Keep in mind the following limitations:

  • 批处理仅对在 Linux 池上运行的容器提供 RDMA 支持。Batch provides RDMA support only for containers running on Linux pools.

  • 对于 Windows 容器工作负荷，建议为你的池选择多核 VM 大小。For Windows container workloads, we recommend choosing a multicore VM size for your pool.

受支持的虚拟机映像Supported virtual machine images

使用以下受支持的 Windows 或 Linux 映像之一，为容器工作负荷创建 VM 计算节点池。Use one of the following supported Windows or Linux images to create a pool of VM compute nodes for container workloads. 有关与 Batch 兼容的市场映像的详细信息，请参阅虚拟机映像列表。For more information about Marketplace images that are compatible with Batch, see List of virtual machine images.

Windows 支持Windows support

Batch 支持被指派了容器支持的 Windows Server 映像。Batch supports Windows server images that have container support designations. 通常，这些映像 sku 名称的后缀为 -with-containers 或 -with-containers-smalldisk。Typically these image sku names are suffixed with -with-containers or -with-containers-smalldisk. 此外，如果映像支持 Docker 容器，则列出 Batch 中所有受支持映像的 API 将指出 DockerCompatible 功能。Additionally, the API to list all supported images in Batch will denote a DockerCompatible capability if the image supports Docker containers.

也可从 Windows 上运行 Docker 的 VM 创建自定义映像。You can also create custom images from VMs running Docker on Windows.

Linux 支持Linux support

对于 Linux 容器工作负荷，Batch 目前支持 Azure Batch 在 Azure 市场中发布的下列 Linux 映像，无需自定义映像。For Linux container workloads, Batch currently supports the following Linux images published by Azure Batch in the Azure Marketplace without the need for a custom image.

没有 RDMA 的 VM 大小VM sizes without RDMA

  • 发布者：microsoft-azure-batchPublisher: microsoft-azure-batch
    • 产品/服务：centos-containerOffer: centos-container
    • 产品/服务：ubuntu-server-containerOffer: ubuntu-server-container

带有 RDMA 的 VM 大小VM sizes with RDMA

  • 发布者：microsoft-azure-batchPublisher: microsoft-azure-batch
    • 产品/服务：centos-container-rdmaOffer: centos-container-rdma
    • 产品/服务：ubuntu-server-container-rdmaOffer: ubuntu-server-container-rdma

这些映像只能在 Azure Batch 池中使用，适用于 Docker 容器执行。These images are only supported for use in Azure Batch pools and are geared for Docker container execution. 这些映像具有以下特性：They feature:

  • 预装了与 Docker 兼容的 Moby 容器运行时A pre-installed Docker-compatible Moby container runtime

  • 预装了 NVIDIA GPU 驱动程序和 NVIDIA 容器运行时，可简化 Azure N 系列 VM 上的部署Pre-installed NVIDIA GPU drivers and NVIDIA container runtime, to streamline deployment on Azure N-series VMs

  • 预安装/预配置了映像，支持后缀为 -rdma 的映像的 Infiniband RDMA VM 大小。Pre-installed/pre-configured image with support for Infiniband RDMA VM sizes for images with the suffix of -rdma. 这些映像目前不支持 SR-IOV IB/RDMA VM 大小。Currently these images do not support SR-IOV IB/RDMA VM sizes.

也可以从与 Batch 兼容的 Linux 分发版之一上运行 Docker 的 VM 创建自定义映像。You can also create custom images from VMs running Docker on one of the Linux distributions that is compatible with Batch. 如果选择提供你自己的自定义 Linux 映像，请参阅使用托管自定义映像创建虚拟机池中的说明。If you choose to provide your own custom Linux image, see the instructions in Use a managed custom image to create a pool of virtual machines.

若要在自定义映像中获取 Docker 支持，请安装 Docker Community Edition (CE) 或 Docker Enterprise Edition (EE)。For Docker support on a custom image, install Docker Community Edition (CE) or Docker Enterprise Edition (EE).

使用自定义 Linux 映像时的其他注意事项：Additional considerations for using a custom Linux image:

  • 若要在使用自定义映像时利用 Azure N 系列大小的 GPU 性能，请预装 NVIDIA 驱动程序。To take advantage of the GPU performance of Azure N-series sizes when using a custom image, pre-install NVIDIA drivers. 此外，还需要安装 NVIDIA GPU 的 Docker 引擎实用工具 NVIDIA Docker。Also, you need to install the Docker Engine Utility for NVIDIA GPUs, NVIDIA Docker.

  • 若要访问 Azure RDMA 网络，请使用支持 RDMA 的 VM 大小。To access the Azure RDMA network, use an RDMA-capable VM size. Batch 支持的 CentOS HPC 和 Ubuntu 映像中已安装所需的 RDMA 驱动程序。Necessary RDMA drivers are installed in the CentOS HPC and Ubuntu images supported by Batch. 可能还需要其他配置来运行 MPI 工作负载。Additional configuration may be needed to run MPI workloads. 请参阅在批处理池中使用支持 RDMA 或启用了 GPU 的实例。See Use RDMA-capable or GPU-enabled instances in Batch pool.

批处理池的容器配置Container configuration for Batch pool

若要启用运行容器工作负荷的 Batch 池，必须在池的 VirtualMachineConfiguration 对象中指定 ContainerConfiguration 设置。To enable a Batch pool to run container workloads, you must specify ContainerConfiguration settings in the pool's VirtualMachineConfiguration object. （本文已提供 Batch .NET API 参考文章的链接。(This article provides links to the Batch .NET API reference. Batch Python API 中提供了相应的设置。）Corresponding settings are in the Batch Python API.)

可以创建启用容器的池，可以带或不带预提取容器映像，如以下示例所示。You can create a container-enabled pool with or without prefetched container images, as shown in the following examples. 可以通过拉取（或预提取）过程从 Docker 中心预加载容器映像，或在 Internet 上预加载另一个容器注册表。The pull (or prefetch) process lets you pre-load container images from either Docker Hub or another container registry on the Internet. 为获得最佳性能，请使用 Batch 帐户所在的同一区域中的 Azure 容器注册表。For best performance, use an Azure container registry in the same region as the Batch account.

预提取容器映像的优点是，当任务开始运行时，它们不必等待要下载的容器映像。The advantage of prefetching container images is that when tasks first start running they don't have to wait for the container image to download. 容器配置在创建池时将容器映像拉取到 VM。The container configuration pulls container images to the VMs when the pool is created. 然后，在池中运行的任务即可引用容器映像和容器运行选项列表。Tasks that run on the pool can then reference the list of container images and container run options.

不带预提取容器映像的池Pool without prefetched container images

若要配置启用了容器但不带预提取容器映像的池，请定义 ContainerConfiguration 和 VirtualMachineConfiguration 对象，如以下示例所示。To configure a container-enabled pool without prefetched container images, define ContainerConfiguration and VirtualMachineConfiguration objects as shown in the following examples. 这些示例使用市场中提供的适用于 Azure Batch 容器池映像的 Ubuntu Server。These examples use the Ubuntu Server for Azure Batch container pools image from the Marketplace.

image_ref_to_use = batch.models.ImageReference(
    publisher='microsoft-azure-batch',
    offer='ubuntu-server-container',
    sku='16-04-lts',
    version='latest')

"""
Specify container configuration. This is required even though there are no prefetched images.
"""

container_conf = batch.models.ContainerConfiguration()

new_pool = batch.models.PoolAddParameter(
    id=pool_id,
    virtual_machine_configuration=batch.models.VirtualMachineConfiguration(
        image_reference=image_ref_to_use,
        container_configuration=container_conf,
        node_agent_sku_id='batch.node.ubuntu 16.04'),
    vm_size='STANDARD_D1_V2',
    target_dedicated_nodes=1)
...

ImageReference imageReference = new ImageReference(
    publisher: "microsoft-azure-batch",
    offer: "ubuntu-server-container",
    sku: "16-04-lts",
    version: "latest");

// Specify container configuration. This is required even though there are no prefetched images.
ContainerConfiguration containerConfig = new ContainerConfiguration();

// VM configuration
VirtualMachineConfiguration virtualMachineConfiguration = new VirtualMachineConfiguration(
    imageReference: imageReference,
    nodeAgentSkuId: "batch.node.ubuntu 16.04");
virtualMachineConfiguration.ContainerConfiguration = containerConfig;

// Create pool
CloudPool pool = batchClient.PoolOperations.CreatePool(
    poolId: poolId,
    targetDedicatedComputeNodes: 1,
    virtualMachineSize: "STANDARD_D1_V2",
    virtualMachineConfiguration: virtualMachineConfiguration);

容器配置的预提取映像Prefetch images for container configuration

若要在池中预提取容器映像，请将容器映像列表（在 Python 中为 container_image_names）添加到 ContainerConfiguration。To prefetch container images on the pool, add the list of container images (container_image_names, in Python) to the ContainerConfiguration.

以下基本 Python 示例演示如何从 Docker 中心预提取一个标准的 Ubuntu 容器映像。The following basic Python example shows how to prefetch a standard Ubuntu container image from Docker Hub.

image_ref_to_use = batch.models.ImageReference(
    publisher='microsoft-azure-batch',
    offer='ubuntu-server-container',
    sku='16-04-lts',
    version='latest')

"""
Specify container configuration, fetching the official Ubuntu container image from Docker Hub.
"""

container_conf = batch.models.ContainerConfiguration(
    container_image_names=['ubuntu'])

new_pool = batch.models.PoolAddParameter(
    id=pool_id,
    virtual_machine_configuration=batch.models.VirtualMachineConfiguration(
        image_reference=image_ref_to_use,
        container_configuration=container_conf,
        node_agent_sku_id='batch.node.ubuntu 16.04'),
    vm_size='STANDARD_D1_V2',
    target_dedicated_nodes=1)
...

以下 C# 示例假设要从 Docker 中心预提取一个 TensorFlow 映像。The following C# example assumes that you want to prefetch a TensorFlow image from Docker Hub. 本示例包含一个在池节点的 VM 主机中运行的启动任务。This example includes a start task that runs in the VM host on the pool nodes. 可以在主机中运行具有特定目的（例如，装载可以从容器访问的文件服务器）的启动任务。You might run a start task in the host, for example, to mount a file server that can be accessed from the containers.

ImageReference imageReference = new ImageReference(
    publisher: "microsoft-azure-batch",
    offer: "ubuntu-server-container",
    sku: "16-04-lts",
    version: "latest");

ContainerRegistry containerRegistry = new ContainerRegistry(
    registryServer: "https://hub.docker.com",
    userName: "UserName",
    password: "YourPassword"                
);

// Specify container configuration, prefetching Docker images
ContainerConfiguration containerConfig = new ContainerConfiguration();
containerConfig.ContainerImageNames = new List<string> { "tensorflow/tensorflow:latest-gpu" };
containerConfig.ContainerRegistries = new List<ContainerRegistry> { containerRegistry };

// VM configuration
VirtualMachineConfiguration virtualMachineConfiguration = new VirtualMachineConfiguration(
    imageReference: imageReference,
    nodeAgentSkuId: "batch.node.ubuntu 16.04");
virtualMachineConfiguration.ContainerConfiguration = containerConfig;

// Set a native host command line start task
StartTask startTaskContainer = new StartTask( commandLine: "<native-host-command-line>" );

// Create pool
CloudPool pool = batchClient.PoolOperations.CreatePool(
    poolId: poolId,
    virtualMachineSize: "Standard_NC6s_v3",
    virtualMachineConfiguration: virtualMachineConfiguration);

// Start the task in the pool
pool.StartTask = startTaskContainer;
...

从专用容器注册表中预提取映像Prefetch images from a private container registry

另外，也可以通过对专有容器注册表服务器进行身份验证来预提取容器映像。You can also prefetch container images by authenticating to a private container registry server. 在以下示例中，ContainerConfiguration 和 VirtualMachineConfiguration 对象从专用的 Azure 容器注册表预提取专用的 TensorFlow 映像。In the following examples, the ContainerConfiguration and VirtualMachineConfiguration objects prefetch a private TensorFlow image from a private Azure container registry. 图像中的参考信息与前面的示例相同。The image reference is the same as in the previous example.

image_ref_to_use = batch.models.ImageReference(
        publisher='microsoft-azure-batch',
        offer='ubuntu-server-container',
        sku='16-04-lts',
        version='latest')

# Specify a container registry
container_registry = batch.models.ContainerRegistry(
        registry_server="myRegistry.azurecr.cn",
        user_name="myUsername",
        password="myPassword")

# Create container configuration, prefetching Docker images from the container registry
container_conf = batch.models.ContainerConfiguration(
        container_image_names = ["myRegistry.azurecr.cn/samples/myImage"],
        container_registries =[container_registry])

new_pool = batch.models.PoolAddParameter(
            id="myPool",
            virtual_machine_configuration=batch.models.VirtualMachineConfiguration(
                image_reference=image_ref_to_use,
                container_configuration=container_conf,
                node_agent_sku_id='batch.node.ubuntu 16.04'),
            vm_size='STANDARD_D1_V2',
            target_dedicated_nodes=1)

// Specify a container registry
ContainerRegistry containerRegistry = new ContainerRegistry(
    registryServer: "myContainerRegistry.azurecr.cn",
    userName: "myUserName",
    password: "myPassword");

// Create container configuration, prefetching Docker images from the container registry
ContainerConfiguration containerConfig = new ContainerConfiguration();
containerConfig.ContainerImageNames = new List<string> {
        "myContainerRegistry.azurecr.cn/tensorflow/tensorflow:latest-gpu" };
containerConfig.ContainerRegistries = new List<ContainerRegistry> { containerRegistry } );

// VM configuration
VirtualMachineConfiguration virtualMachineConfiguration = new VirtualMachineConfiguration(
    imageReference: imageReference,
    nodeAgentSkuId: "batch.node.ubuntu 16.04");
virtualMachineConfiguration.ContainerConfiguration = containerConfig;

// Create pool
CloudPool pool = batchClient.PoolOperations.CreatePool(
    poolId: poolId,
    targetDedicatedComputeNodes: 4,
    virtualMachineSize: "Standard_NC6s_v3",
    virtualMachineConfiguration: virtualMachineConfiguration);
...

任务容器设置Container settings for the task

若要在启用了容器的池上运行容器任务，请指定特定于容器的设置。To run a container task on a container-enabled pool, specify container-specific settings. 设置包括要使用的映像、注册表和容器运行选项。Settings include the image to use, registry, and container run options.

  • 使用任务类中的 ContainerSettings 属性来配置特定于容器的设置。Use the ContainerSettings property of the task classes to configure container-specific settings. 这些设置由 TaskContainerSettings 类定义。These settings are defined by the TaskContainerSettings class. 请注意，--rm 容器选项由 Batch 处理，因此它不需要其他 --runtime 选项。Note that the --rm container option doesn't require an additional --runtime option since it is taken care of by Batch.

  • 如果在容器映像上运行任务，云任务和作业管理器任务将需要容器设置。If you run tasks on container images, the cloud task and job manager task require container settings. 但是，启动任务、作业准备任务和作业发布任务都不需要容器设置（即，它们可以在容器上下文中或直接在节点上运行）。However, the start task, job preparation task, and job release task do not require container settings (that is, they can run within a container context or directly on the node).

  • 对于 Windows，任务必须在 ElevationLevel 设置为 admin 的情况下运行。For Windows, tasks must be run with ElevationLevel set to admin.

  • 对于 Linux，Batch 会将用户/组权限映射到容器。For Linux, Batch will map the user/group permission to the container. 如果访问容器中的任何文件夹都需要管理员权限，则可能需要以管理员提升级别在池范围内运行任务。If access to any folder within the container requires Administrator permission, you may need to run the task as pool scope with admin elevation level. 这将确保 Batch 在容器上下文中以 root 身份运行任务。This will ensure Batch runs the task as root in the container context. 否则，非管理员用户可能无权访问这些文件夹。Otherwise, a non-admin user may not have access to those folders.

  • 对于启用了 GPU 的硬件的容器池，Batch 会自动为容器任务启用 GPU，因此不应包含 -gpus 参数。For container pools with GPU-enabled hardware, Batch will automatically enable GPU for container tasks, so you shouldn't include the -gpus argument.

容器任务命令行Container task command line

运行容器任务时，Batch 自动使用 docker create 命令通过任务中指定的映像创建容器。When you run a container task, Batch automatically uses the docker create command to create a container using the image specified in the task. 之后，Batch 会控制容器中的任务执行。Batch then controls task execution in the container.

与非容器 Batch 任务一样，为容器任务设置命令行。As with non-container Batch tasks, you set a command line for a container task. 由于 Batch 自动创建容器，因此命令行仅指定将在容器中运行的命令。Because Batch automatically creates the container, the command line only specifies the command or commands that will run in the container.

如果 Batch 任务的容器映像配置了 ENTRYPOINT 脚本，则可以将命令行设置为使用默认的 ENTRYPOINT 或覆盖它：If the container image for a Batch task is configured with an ENTRYPOINT script, you can set your command line to either use the default ENTRYPOINT or override it:

  • 若要使用容器映像的默认 ENTRYPOINT，请将任务命令行设置为空字符串 ""。To use the default ENTRYPOINT of the container image, set the task command line to the empty string "".

  • 若要覆盖默认的 ENTRYPOINT，或者如果映像没有 ENTRYPOINT，请设置适用于容器的命令行，例如 /app/myapp 或 /bin/sh -c python myscript.py。To override the default ENTRYPOINT, or if the image doesn't have an ENTRYPOINT, set a command line appropriate for the container, for example, /app/myapp or /bin/sh -c python myscript.py.

可选的 ContainerRunOptions 是为 Batch 用于创建和运行容器的 docker create 命令提供的附加参数。Optional ContainerRunOptions are additional arguments you provide to the docker create command that Batch uses to create and run the container. 例如，若要为容器设置工作目录，请设置 --workdir <directory> 选项。For example, to set a working directory for the container, set the --workdir <directory> option. 有关其他选项，请参阅 docker create 参考。See the docker create reference for additional options.

容器任务工作目录Container task working directory

Batch 容器任务在容器的工作目录中执行，该目录与 Batch 为常规（非容器）任务设置的目录非常相似。A Batch container task executes in a working directory in the container that is very similar to the directory Batch sets up for a regular (non-container) task. 请注意，此工作目录与映像中的 WORKDIR（如果配置）或默认容器工作目录（Windows容器上的 C:\ 或 Linux 容器上的 /）不同。Note that this working directory is different from the WORKDIR if configured in the image, or the default container working directory (C:\ on a Windows container, or / on a Linux container).

对于 Batch 容器任务：For a Batch container task:

  • 以递归方式位于主机节点上的 AZ_BATCH_NODE_ROOT_DIR（Azure Batch 目录的根）下的所有目录都映射到容器中All directories recursively below the AZ_BATCH_NODE_ROOT_DIR on the host node (the root of Azure Batch directories) are mapped into the container
  • 所有任务环境变量都映射到该容器All task environment variables are mapped into the container
  • 节点上的任务工作目录 AZ_BATCH_TASK_WORKING_DIR 设置为与常规任务相同并映射到容器中。The task working directory AZ_BATCH_TASK_WORKING_DIR on the node is set the same as for a regular task and mapped into the container.

这些映射允许以与处理非容器任务大致相同的方式处理容器任务。These mappings allow you to work with container tasks in much the same way as non-container tasks. 例如，使用应用程序包安装应用程序，从 Azure 存储访问资源文件，使用任务环境设置，以及在容器停止后保留任务输出文件。For example, install applications using application packages, access resource files from Azure Storage, use task environment settings, and persist task output files after the container stops.

对容器任务进行故障排除Troubleshoot container tasks

如果容器任务未按预期运行，则可能需要获取有关容器映像的 WORKDIR 或 ENTRYPOINT 配置的信息。If your container task doesn't run as expected, you might need to get information about the WORKDIR or ENTRYPOINT configuration of the container image. 若要查看配置，请运行 docker image inspect 命令。To see the configuration, run the docker image inspect command.

如果需要，请根据映像调整容器任务的设置：If needed, adjust the settings of the container task based on the image:

  • 在任务命令行中指定绝对路径。Specify an absolute path in the task command line. 如果映像的默认 ENTRYPOINT 用于任务命令行，请确保设置了绝对路径。If the image's default ENTRYPOINT is used for the task command line, ensure that an absolute path is set.
  • 在任务的容器运行选项中，更改工作目录以匹配映像中的 WORKDIR。In the task's container run options, change the working directory to match the WORKDIR in the image. 例如，设置 --workdir /app。For example, set --workdir /app.

容器任务示例Container task examples

以下 Python 代码片段展示了根据从 Docker 中心拉取的虚构映像创建的容器中运行的基本命令行。The following Python snippet shows a basic command line running in a container created from a fictitious image pulled from Docker Hub. 此处，--rm 容器选项用于在任务完成后删除容器，--workdir 选项用于设置工作目录。Here, the --rm container option removes the container after the task finishes, and the --workdir option sets a working directory. 该命令行使用一个简单的 shell 命令（可将小文件写入主机上的任务工作目录）覆盖容器 ENTRYPOINT。The command line overrides the container ENTRYPOINT with a simple shell command that writes a small file to the task working directory on the host.

task_id = 'sampletask'
task_container_settings = batch.models.TaskContainerSettings(
    image_name='myimage',
    container_run_options='--rm --workdir /')
task = batch.models.TaskAddParameter(
    id=task_id,
    command_line='/bin/sh -c \"echo \'hello world\' > $AZ_BATCH_TASK_WORKING_DIR/output.txt\"',
    container_settings=task_container_settings
)

以下 C# 示例演示某个云任务的基本容器设置：The following C# example shows basic container settings for a cloud task:

// Simple container task command
string cmdLine = "c:\\app\\myApp.exe";

TaskContainerSettings cmdContainerSettings = new TaskContainerSettings (
    imageName: "myimage",
    containerRunOptions: "--rm --workdir c:\\app"
    );

CloudTask containerTask = new CloudTask (
    id: "Task1",
    commandline: cmdLine);
containerTask.ContainerSettings = cmdContainerSettings;

后续步骤Next steps