# 为 TF 服务构建 Docker 镜像 我们继续使用 Docker 镜像进行如下操作： 1. 使用以下内容创建名为`dockerfile`的文件： ```py FROM ubuntu:16.04 MAINTAINER Armando Fandango <armando@geekysalsero.com> RUN apt-get update && apt-get install -y \ build-essential \ curl \ git \ libfreetype6-dev \ libpng12-dev \ libzmq3-dev \ mlocate \ pkg-config \ python-dev \ python-numpy \ python-pip \ software-properties-common \ swig \ zip \ zlib1g-dev \ libcurl3-dev \ openjdk-8-jdk\ openjdk-8-jre-headless \ wget \ && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* RUN echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" \ | tee /etc/apt/sources.list.d/tensorflow-serving.list RUN curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg \ | apt-key add - RUN apt-get update && apt-get install -y \ tensorflow-model-server RUN pip install --upgrade pip RUN pip install mock grpcio tensorflow tensorflow-serving-api CMD ["/bin/bash"] ``` 1. 运行以下命令从此`dockerfile`构建 Docker 镜像： ```py $ docker build --pull -t $USER/tensorflow_serving -f dockerfile . ``` 1. 创建图像需要一段时间。当您看到类似于以下内容的内容时，您就会知道图像已构建： ```py Removing intermediate container 1d8e757d96e0 Successfully built 0f95ddba4362 Successfully tagged armando/tensorflow_serving:latest ``` 1. 运行以下命令以启动容器： ```py $ docker run --name=mnist_container -it $USER/tensorflow_serving ``` 1. 当您看到以下提示时，您将登录到容器： ```py root@244ea14efb8f:/# ``` 1. 将`cd`命令转到主文件夹。 2. 在主文件夹中，提供以下命令以检查 TensorFlow 是否正在提供代码。我们将使用此代码中的示例来演示，但您可以查看自己的 Git 仓库来运行您自己的模型： ```py $ git clone --recurse-submodules https://github.com/tensorflow/serving ``` 克隆仓库后，我们就可以构建，训练和保存 MNIST 模型了。 1. 使用以下命令删除临时文件夹（如果尚未删除）： ```py $ rm -rf /tmp/mnist_model ``` 1. 运行以下命令以构建，训练和保存 MNIST 模型。 ```py $ python serving/tensorflow_serving/example/mnist_saved_model.py /tmp/mnist_model ``` 您将看到类似于以下内容的内容： ```py Training model... Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Extracting /tmp/train-images-idx3-ubyte.gz Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. Extracting /tmp/train-labels-idx1-ubyte.gz Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. Extracting /tmp/t10k-images-idx3-ubyte.gz Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. Extracting /tmp/t10k-labels-idx1-ubyte.gz 2017-11-22 01:09:38.165391: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA training accuracy 0.9092 Done training! Exporting trained model to /tmp/mnist_model/1 Done exporting! ``` 1. 按`Ctrl + P`和`Ctrl + Q`从 Docker 镜像中分离。 2. 提交对新映像的更改并使用以下命令停止容器： ```py $ docker commit mnist_container $USER/mnist_serving $ docker stop mnist_container ``` 1. 现在，您可以通过提供以下命令随时运行此容器： ```py $ docker run --name=mnist_container -it $USER/mnist_serving ``` 1. 删除我们为保存图像而构建的临时 MNIST 容器： ```py $ docker rm mnist_container ```