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173 lines
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173 lines
5.5 KiB
Markdown
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[#]: subject: "MLCube and Podman"
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[#]: via: "https://fedoramagazine.org/mlcube-and-podman/"
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[#]: author: "Benson Muite https://fedoramagazine.org/author/fed500/"
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[#]: collector: "lujun9972"
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[#]: translator: "geekpi"
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[#]: reviewer: "wxy"
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[#]: publisher: "wxy"
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[#]: url: "https://linux.cn/article-14515-1.html"
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使用 Podman 运行一个 “hello world” MLCube
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======
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[MLCube][2] 是一个新的基于基础设施规范的开源容器，被引入到基于 Python 的机器学习工作流程中，以实现可重复性。它可以利用诸如 [Podman][3]、[Singularity][4] 和 [Docker][5] 等工具。也支持在远程平台上的执行。开发 MLCube 的 MLCommons 最佳实践工作组的主席之一是来自 Red Hat 的 [Diane Feddema][6]。这篇介绍性文章解释了如何在 Fedora Linux 上使用 Podman 运行 [“hello world” MLCube 例子][7]。
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[Yazan Monshed][8] 写了一篇关于 [Fedora 上的 Podman][9] 的非常有用的介绍，对这里使用的一些步骤给出了更多细节。
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首先安装必要的依赖项。
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```
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sudo dnf -y update
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sudo dnf -y install podman git virtualenv \
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policycoreutils-python-utils
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```
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然后，按照文档的要求，设置一个虚拟环境并获得示例代码。为了确保可重复性，使用一个特定的提交，因为该项目正在积极改进。
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```
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virtualenv -p python3 ./env_mlcube
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source ./env_mlcube/bin/activate
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git clone https://github.com/mlcommons/mlcube_examples.git
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cd ./mlcube_examples/hello_world
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git checkout 5fe69bd
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pip install mlcube mlcube-docker
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mlcube describe
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```
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现在，通过编辑 $HOME/mlcube.yaml 文件，将运行器命令从 `docker` 改为 `podman`，即：
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```
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docker: docker
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```
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改为：
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```
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docker: podman
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```
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如果你使用的是 x86_64 架构的电脑，你可以用以下方式获取容器：
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```
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mlcube configure --mlcube=. --platform=docker
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```
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你会看到一些选项：
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```
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? Please select an image:
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▸ registry.fedoraproject.org/mlcommons/hello_world:0.0.1
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registry.access.redhat.com/mlcommons/hello_world:0.0.1
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docker.io/mlcommons/hello_world:0.0.1
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quay.io/mlcommons/hello_world:0.0.1
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```
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选择 `docker.io/mlcommons/hello_world:0.0.1` 来获取容器。
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如果你的电脑不是 x86_64 架构的，你需要构建容器。改变文件 `$HOME/mlcube.yaml`，将这一行：
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```
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build_strategy: pull
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```
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变为：
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```
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build_strategy: auto
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```
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然后用以下方法构建容器：
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```
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mlcube configure --mlcube=. --platform=docker
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```
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要运行测试，你可能需要在目录中适当地设置 SELinux 权限。你可以通过输入以下内容来检查 SELinux 是否已经启用：
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```
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sudo sestatus
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```
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应该会有类似这样的输出：
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```
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SELinux status: enabled
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...
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```
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[Josphat Mutai][10]、[Christopher Smart][11] 和 [Daniel Walsh][12] 解释说，在为容器使用的文件设置适当的 SELinux 策略时，你需要谨慎。在这里，你将允许容器读取和写入 `workspace` 目录。
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```
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sudo semanage fcontext -a -t container_file_t "$PWD/workspace(/.*)?"
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sudo restorecon -Rv $PWD/workspace
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```
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现在检查目录策略：
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```
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ls -Z
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```
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输出结果类似于：
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```
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unconfined_u:object_r:user_home_t:s0 Dockerfile
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unconfined_u:object_r:user_home_t:s0 README.md
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unconfined_u:object_r:user_home_t:s0 mlcube.yaml
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unconfined_u:object_r:user_home_t:s0 requirements.txt
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unconfined_u:object_r:container_file_t:s0 workspace
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```
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现在运行这个例子：
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```
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mlcube run --mlcube=. --task=hello --platform=docker
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mlcube run --mlcube=. --task=bye --platform=docker
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```
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最后，检查输出：
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```
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cat workspace/chats/chat_with_alice.txt
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```
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有类似于以下的文字：
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```
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Hi, Alice! Nice to meet you.
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Bye, Alice! It was great talking to you.
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```
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你可以按照 [这里][13] 的描述创建你自己的 MLCube。欢迎对 [MLCube 示例库][14] 做出贡献。[Udica][15] 是一个新项目，它承诺为容器提供更精细的 SELinux 策略控制，便于系统管理员应用。这些项目的积极开发正在进行中。对它们进行测试并提供反馈，将有助于使带有 SELinux 的系统上的安全数据管理更容易、更有效。
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--------------------------------------------------------------------------------
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via: https://fedoramagazine.org/mlcube-and-podman/
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作者：[Benson Muite][a]
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选题：[lujun9972][b]
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译者：[geekpi](https://github.com/geekpi)
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校对：[wxy](https://github.com/wxy)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://fedoramagazine.org/author/fed500/
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[b]: https://github.com/lujun9972
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[1]: https://fedoramagazine.org/wp-content/uploads/2022/04/MLCubePodman-816x345.jpg
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[2]: https://mlcommons.org/en/mlcube/
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[3]: https://podman.io/
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[4]: https://sylabs.io/singularity/
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[5]: https://www.docker.com/
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[6]: https://www.redhat.com/en/authors/diane-feddema
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[7]: https://mlcommons.github.io/mlcube/getting-started/hello-world/
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[8]: https://fedoramagazine.org/author/yazanalmonshed/
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[9]: https://fedoramagazine.org/getting-started-with-podman-in-fedora/
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[10]: https://computingforgeeks.com/set-selinux-context-label-for-podman-graphroot-directory/
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[11]: https://blog.christophersmart.com/2021/01/31/podman-volumes-and-selinux/
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[12]: https://opensource.com/article/18/2/selinux-labels-container-runtimes
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[13]: https://mlcommons.github.io/mlcube/tutorials/create-mlcube/
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[14]: https://github.com/mlcommons/mlcube_examples
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[15]: https://github.com/containers/udica
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