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# Copyright (C) 2023, Xorbits Inc.
# This file is distributed under the same license as the Xinference package.
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#: ../../source/getting_started/using_docker_image.rst:5
msgid "Xinference Docker Image"
msgstr "Docker 镜像"

#: ../../source/getting_started/using_docker_image.rst:7
msgid "Xinference provides official images for use on Dockerhub."
msgstr "Xinference 在 Dockerhub 和 阿里云容器镜像服务 中上传了官方镜像。"

#: ../../source/getting_started/using_docker_image.rst:11
msgid "Prerequisites"
msgstr "准备工作"

#: ../../source/getting_started/using_docker_image.rst:12
msgid ""
"The image can only run in an environment with GPUs and CUDA installed, "
"because Xinference in the image relies on Nvidia GPUs for acceleration."
msgstr ""
"Xinference 使用 GPU 加速推理，该镜像需要在有 GPU 显卡并且安装 CUDA 的机器"
"上运行。"

#: ../../source/getting_started/using_docker_image.rst:13
msgid ""
"CUDA must be successfully installed on the host machine. This can be "
"determined by whether you can successfully execute the ``nvidia-smi`` "
"command."
msgstr "保证 CUDA 在机器上正确安装。可以使用 ``nvidia-smi`` 检查是否正确运行。"

#: ../../source/getting_started/using_docker_image.rst:14
msgid ""
"The CUDA version in the docker image is ``12.4``, and the CUDA version on"
" the host machine should be ``12.4`` or above, and the NVIDIA driver "
"version should be ``550`` or above."
msgstr ""
"镜像中的 CUDA 版本为 ``12.4`` 。为了不出现预期之外的问题，请将宿主机的 CUDA 版本和 NVIDIA Driver 版本分别"
"升级到 ``12.4`` 和 ``550`` 以上。"

#: ../../source/getting_started/using_docker_image.rst:18
msgid "Docker Image"
msgstr "Docker 镜像"

#: ../../source/getting_started/using_docker_image.rst:19
msgid ""
"The official image of Xinference is available on DockerHub in the "
"repository ``xprobe/xinference``. Available tags include:"
msgstr ""
"当前，可以通过两个渠道拉取 Xinference 的官方镜像。"
"1. 在 Dockerhub 的 ``xprobe/xinference`` 仓库里。"
"2. Dockerhub 中的镜像会同步上传一份到阿里云公共镜像仓库中，供访问 Dockerhub 有困难的用户拉取。"
"拉取命令：``docker pull registry.cn-hangzhou.aliyuncs.com/xprobe_xinference/xinference:<tag>`` 。目前可用的标签包括："

#: ../../source/getting_started/using_docker_image.rst:22
msgid ""
"``nightly-main``: This image is built daily from the `GitHub main branch "
"<https://github.com/xorbitsai/inference>`_ and generally does not "
"guarantee stability."
msgstr ""
"``nightly-main``: 这个镜像会每天从 GitHub main 分支更新制作，不保证稳定"
"可靠。"

#: ../../source/getting_started/using_docker_image.rst:23
msgid ""
"``v<release version>``: This image is built each time a Xinference "
"release version is published, and it is typically more stable."
msgstr ""
"``v<release version>``: 这个镜像会在 Xinference 每次发布的时候制作，通常"
"可以认为是稳定可靠的。"

#: ../../source/getting_started/using_docker_image.rst:24
msgid ""
"``latest``: This image is built with the latest Xinference release "
"version."
msgstr "``latest``: 这个镜像会在 Xinference 发布时指向最新的发布版本"

#: ../../source/getting_started/using_docker_image.rst:25
msgid "For CPU version, add ``-cpu`` suffix, e.g. ``nightly-main-cpu``."
msgstr "对于 CPU 版本，增加 ``-cpu`` 后缀，如 ``nightly-main-cpu``。"

#: ../../source/getting_started/using_docker_image.rst:29
msgid "Dockerfile for custom build"
msgstr "自定义镜像"

#: ../../source/getting_started/using_docker_image.rst:30
msgid ""
"If you need to build the Xinference image according to your own "
"requirements, the source code for the Dockerfile is located at "
"`xinference/deploy/docker/Dockerfile "
"<https://github.com/xorbitsai/inference/tree/main/xinference/deploy/docker/Dockerfile>`_"
" for reference. Please make sure to be in the top-level directory of "
"Xinference when using this Dockerfile. For example:"
msgstr ""
"如果需要安装额外的依赖，可以参考 `xinference/deploy/docker/Dockerfile <"
"https://github.com/xorbitsai/inference/tree/main/xinference/deploy/docker"
"/Dockerfile>`_ 。请确保使用 Dockerfile 制作镜像时在 Xinference 项目的"
"根目录下。比如："

#: ../../source/getting_started/using_docker_image.rst:41
msgid "Image usage"
msgstr "使用镜像"

#: ../../source/getting_started/using_docker_image.rst:42
msgid ""
"You can start Xinference in the container like this, simultaneously "
"mapping port 9997 in the container to port 9998 on the host, enabling "
"debug logging, and downloading models from modelscope."
msgstr ""
"你可以使用如下方式在容器内启动 Xinference，同时将 9997 端口映射到宿主机的"
" 9998 端口，并且指定日志级别为 DEBUG，也可以指定需要的环境变量。"

#: ../../source/getting_started/using_docker_image.rst:50
msgid ""
"The option ``--gpus`` is essential and cannot be omitted, because as "
"mentioned earlier, the image requires the host machine to have a GPU. "
"Otherwise, errors will occur."
msgstr ""
"``--gpus`` 必须指定，正如前文描述，镜像必须运行在有 GPU 的机器上，否则会"
"出现错误。"

#: ../../source/getting_started/using_docker_image.rst:51
msgid ""
"The ``-H 0.0.0.0`` parameter after the ``xinference-local`` command "
"cannot be omitted. Otherwise, the host machine may not be able to access "
"the port inside the container."
msgstr "``-H 0.0.0.0`` 也是必须指定的，否则在容器外无法连接到 Xinference 服务。"

#: ../../source/getting_started/using_docker_image.rst:52
msgid ""
"You can add multiple ``-e`` options to introduce multiple environment "
"variables."
msgstr "可以指定多个 ``-e`` 选项赋值多个环境变量。"

#: ../../source/getting_started/using_docker_image.rst:55
msgid ""
"Certainly, if you prefer, you can also manually enter the docker "
"container and start Xinference in any desired way."
msgstr "当然，也可以运行容器后，进入容器内手动拉起 Xinference。"

#: ../../source/getting_started/using_docker_image.rst:59
msgid "Mount your volume for loading and saving models"
msgstr "挂载模型目录"

#: ../../source/getting_started/using_docker_image.rst:60
msgid ""
"The image does not contain any model files by default, and it downloads "
"the models into the container. Typically, you would need to mount a "
"directory on the host machine to the docker container, so that Xinference"
" can download the models onto it, allowing for reuse. In this case, you "
"need to specify a volume when running the Docker image and configure "
"environment variables for Xinference:"
msgstr ""
"默认情况下，镜像中不包含任何模型文件，使用过程中会在容器内下载模型。如果"
"需要使用已经下载好的模型，需要将宿主机的目录挂载到容器内。这种情况下，"
"需要在运行容器时指定本地卷，并且为 Xinference 配置环境变量。"

#: ../../source/getting_started/using_docker_image.rst:69
msgid ""
"The principle behind the above command is to mount the specified "
"directory from the host machine into the container, and then set the "
"``XINFERENCE_HOME`` environment variable to point to that directory "
"inside the container. This way, all downloaded model files will be stored"
" in the directory you specified on the host machine. You don't have to "
"worry about losing them when the Docker container stops, and the next "
"time you run it, you can directly use the existing models without the "
"need for repetitive downloads."
msgstr ""
"上述命令的原理是将主机上指定的目录挂载到容器中，并设置 ``XINFERENCE_HOME`"
"` 环境变量指向容器内的该目录。这样，所有下载的模型文件将存储在您在主机上"
"指定的目录中。您无需担心在 Docker 容器停止时丢失这些文件，下次运行容器时"
"，您可以直接使用现有的模型，无需重复下载。"

#: ../../source/getting_started/using_docker_image.rst:73
msgid ""
"If you downloaded the model using the default path on the host machine, "
"and since the xinference cache directory stores the model using symbolic "
"links, you need to mount the directory where the original file is located"
" into the container as well. For example, if you are using HuggingFace "
"and Modelscope as model hub, you would need to mount the corresponding "
"directories into the container. Generally, the cache directories for "
"HuggingFace and Modelscope are located at <home_path>/.cache/huggingface "
"and <home_path>/.cache/modelscope. The command would be like:"
msgstr ""
"如果你在宿主机使用的默认路径下载的模型，由于 xinference cache 目录是用的"
"软链的方式存储模型，需要将原文件所在的目录也挂载到容器内。例如你使用 "
"huggingface 和 modelscope 作为模型仓库，那么需要将这两个对应的目录挂载到"
"容器内，一般对应的 cache 目录分别在 <home_path>/.cache/huggingface 和 <"
"home_path>/.cache/modelscope，使用的命令如下："

