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# 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 映像"

#: ../../source/getting_started/using_docker_image.rst:7
msgid "Xinference provides official images for use on Dockerhub."
msgstr "Xinference 在 Dockerhub 和 阿里雲容器鏡像服務 中上傳了官方鏡像。"

#: ../../source/getting_started/using_docker_image.rst:11
msgid ""
"Starting from **Xinference v2.0**, to use the CUDA version of the image, the"
" minimum CUDA version must be **CUDA 12.9**."
msgstr "從 **Xinference v2.0** 開始，如果要使用cuda版本的映像檔，cuda版本最低要達到 **CUDA 12.9** 。"

#: ../../source/getting_started/using_docker_image.rst:14
msgid "Prerequisites"
msgstr "準備工作"

#: ../../source/getting_started/using_docker_image.rst:15
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，該鏡像需要在有 GPU 顯示卡且安裝 CUDA 的機器上運行。"

#: ../../source/getting_started/using_docker_image.rst:16
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 "保證 CUDA 在機器上正確安裝。可以使用 ``nvidia-smi`` 檢查是否正確執行。"

#: ../../source/getting_started/using_docker_image.rst:17
msgid ""
"For CUDA version >= 12.9, CUDA version in the docker image is ``12.9``, and "
"the CUDA version on the host machine should be ``12.9`` or above, and the "
"NVIDIA driver version should be ``575`` or above."
msgstr ""
"對於 CUDA 版本 >= 12.9，Docker 映像中使用的 CUDA 版本為 ``12.9``。主機上的 CUDA 版本需為 ``12.9`` "
"或以上，同時 NVIDIA 驅動程式版本需為 ``575`` 或以上。"

#: ../../source/getting_started/using_docker_image.rst:18
msgid ""
"Ensure `NVIDIA Container Toolkit <https://docs.nvidia.com/datacenter/cloud-"
"native/container-toolkit/latest/install-guide.html>`_ installed."
msgstr ""
"請確保已安裝 `NVIDIA Container Toolkit <https://docs.nvidia.com/datacenter/cloud-"
"native/container-toolkit/latest/install-guide.html>`_ 。"

#: ../../source/getting_started/using_docker_image.rst:22
msgid "Docker Image"
msgstr "Docker 映像"

#: ../../source/getting_started/using_docker_image.rst:23
msgid ""
"The official image of Xinference is available on DockerHub in the repository"
" ``xprobe/xinference``. Available tags include:"
msgstr "Xinference 官方映像已發布在 DockerHub 上的 ``xprobe/xinference`` 倉庫中。當前可用的標籤包括："

#: ../../source/getting_started/using_docker_image.rst:26
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``: 此映像會每日從 GitHub 主分支更新建立，不保證穩定性與可靠性。"

#: ../../source/getting_started/using_docker_image.rst:27
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>``：這個映像檔會在 Xinference 每次發布時製作，通常可以認為是穩定可靠的。"

#: ../../source/getting_started/using_docker_image.rst:28
msgid ""
"``latest``: This image is built with the latest Xinference release version."
msgstr "``latest``：此映像檔會在 Xinference 發佈時指向最新的發行版本。"

#: ../../source/getting_started/using_docker_image.rst:29
msgid "For CPU version, add ``-cpu`` suffix, e.g. ``nightly-main-cpu``."
msgstr "對於 CPU 版本，增加 ``-cpu`` 後綴，如 ``nightly-main-cpu``。"

#: ../../source/getting_started/using_docker_image.rst:33
msgid "Dockerfile for custom build"
msgstr "自訂映像檔"

#: ../../source/getting_started/using_docker_image.rst:34
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 ""
"如果需要安裝額外的依賴，可以參考 `xinference/deploy/docker/Dockerfile "
"<https://github.com/xorbitsai/inference/tree/main/xinference/deploy/docker/Dockerfile>`_"
" 。請確保使用 Dockerfile 製作映像檔時在 Xinference 專案的根目錄下。例如："

#: ../../source/getting_started/using_docker_image.rst:45
msgid "Image usage"
msgstr "使用鏡像"

#: ../../source/getting_started/using_docker_image.rst:46
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 ""
"你可以使用以下方式在容器內啟動 Xinference，同時將 9997 連接埠對應到宿主機的 9998 連接埠，並指定日誌層級為 "
"DEBUG，也可以指定所需的環境變數。"

#: ../../source/getting_started/using_docker_image.rst:54
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`` 必須指定，正如前文所述，鏡像必須運行在有 GPU 的機器上，否則會出現錯誤。"

#: ../../source/getting_started/using_docker_image.rst:55
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`` 也是必須指定的，否則在容器外無法連線到 Xinference 服務。"

#: ../../source/getting_started/using_docker_image.rst:56
msgid ""
"You can add multiple ``-e`` options to introduce multiple environment "
"variables."
msgstr "可以指定多個 ``-e`` 選項來賦值多個環境變數。"

#: ../../source/getting_started/using_docker_image.rst:59
msgid ""
"Certainly, if you prefer, you can also manually enter the docker container "
"and start Xinference in any desired way."
msgstr "當然，也可以運行容器後，進入容器內手動拉起 Xinference。"

#: ../../source/getting_started/using_docker_image.rst:63
msgid ""
"For multiple GPUs, make sure to set the shared memory size, for example: "
"`docker run --shm-size=128g ...`"
msgstr "對於多張 GPU，確保設置共享記憶體大小，例如：`docker run --shm-size=128g ...`"

#: ../../source/getting_started/using_docker_image.rst:67
msgid "Mount your volume for loading and saving models"
msgstr "掛載模型目錄"

#: ../../source/getting_started/using_docker_image.rst:68
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 ""
"預設情況下，映像檔中不包含任何模型檔案，使用過程中會在容器內下載模型。如果需要使用已經下載好的模型，需要將宿主機的目錄掛載到容器內。在這種情況下，需要在執行容器時指定本地磁碟區，並為"
" Xinference 配置環境變數。"

#: ../../source/getting_started/using_docker_image.rst:77
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 ""
"上述命令的原理是將主機上指定的目錄掛載到容器中，並設定 ``XINFERENCE_HOME`` "
"環境變數指向容器內的該目錄。如此一來，所有下載的模型檔案將儲存在您在主機上指定的目錄中。您無需擔心在 Docker "
"容器停止時遺失這些檔案，下次執行容器時，您可以直接使用現有的模型，無需重複下載。"

#: ../../source/getting_started/using_docker_image.rst:81
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 ""
"如果你在宿主機使用預設路徑下載的模型，由於 xinference cache "
"目錄是使用軟鏈結的方式儲存模型，需要將原始檔案所在的目錄也掛載到容器內。例如你使用 Hugging Face 和 ModelScope "
"作為模型倉庫，那麼需要將這兩個對應的目錄掛載到容器內，一般對應的 cache 目錄分別在 <home_path>/.cache/huggingface "
"和 <home_path>/.cache/modelscope，使用的指令如下："

#~ msgid ""
#~ "For CUDA 12.8, add ``-cu128`` suffix, e.g. ``nightly-main-cu128``. "
#~ "(Xinference version should be between v1.8.1 and v1.15.0)"
#~ msgstr ""
#~ "針對 CUDA 12.8 版本，增加 ``-cu128`` 後綴，例如 ``nightly-main-cu128``。（Xinference 版本需介於"
#~ " v1.8.1 與 v1.15.0）"

#~ msgid ""
#~ "Starting from **Xinference v2.0**, only ``-cu129`` and ``-cpu`` images are "
#~ "officially provided."
#~ msgstr ""

#~ msgid ""
#~ "Starting from **Xinference v2.0**, only two image variants are provided: the"
#~ " default (no suffix, **CUDA 12.9**) and the ``-cpu`` image."
#~ msgstr ""
#~ "從 **Xinference v2.0** 開始，僅提供兩種映像變體：預設映像（無後綴， **CUDA 12.9** ）和 ``-cpu`` 映像。"

#~ msgid ""
#~ "For CUDA 12.9, no suffix, e.g. ``nightly-main``. (Xinference version should "
#~ "be v2.0 at least)"
#~ msgstr "對於 CUDA 12.9，不帶後綴，例如 ``nightly-main`` 。（Xinference 版本應至少為 v2.0）"
