Docker下运行TensorFlow过程

阿桔的笔记 2021-07-06

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1. 官网下载Docker Desktop：

https://hub.docker.com/editions/community/docker-ce-desktop-windows

注意说明：Requires Microsoft Windows 10 Professional or Enterprise 64-bit, or Windows 10 Home 64-bit with WSL 2.

2. 下载TensorFlow镜像：

Windows PowerShell输入：docker pull tensorflow/tensorflow

PS C:\Users\86159> docker pull tensorflow/tensorflow
Using default tag: latest
latest: Pulling from tensorflow/tensorflow
01bf7da0a88c: Pull complete
f3b4a5f15c7a: Pull complete
57ffbe87baa1: Pull complete
82b03aaebc12: Pull complete
ded4361a2673: Pull complete
bea159c77425: Pull complete
cec93399b3cf: Pull complete
8c7aca956461: Pull complete
0297cbd0f431: Pull complete
c1f5822d5655: Pull complete
Digest: sha256:788c345613ff6cfe617b911dda22b1a900558c28c75afe6c05f8fa0d02bd9811
Status: Downloaded newer image for tensorflow/tensorflow:latest
docker.io/tensorflow/tensorflow:latest

3. 观察Docker Desktop生成对应image：

4. 运行TensorFlow：

PS C:\Users\86159> docker run -it --rm -v D:\00_Work\GitHub\Tensorflow\:/mnt -w mnt tensorflow/tensorflow


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_       __/    (__  )/ /_/ /  /   _  __/   _  / / /_/ /_ |/ |/ /
/_/    \___//_/ /_//____/ \____//_/    /_/      /_/  \____/____/|__/




WARNING: You are running this container as root, which can cause new files in
mounted volumes to be created as the root user on your host machine.


To avoid this, run the container by specifying your user's userid:


$ docker run -u $(id -u):$(id -g) args...
root@8076016fcb20:/mnt#
root@8076016fcb20:/mnt# ls
__pycache__  hello.py

Windows PowerShell运行TensorFlow，并挂载Windows代码目录到/mnt目录，设置工作目录为/mnt：

5. 观察Docker Desktop的Containers/Apps中显示的运行实例：

6. 在Windows代码目录中编辑python脚本hello.py：

import tensorflow as tf
mnist = tf.keras.datasets.mnist


(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0


model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])


model.compile(optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])


model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)

此脚本使用TensorFlow中的高级API - Keras开发，训练一个简单的单层神经网络来识别手写数字，训练数据库为MNIST。

7. 运行hello.py：

root@8076016fcb20:/mnt# python hello.py
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 3s 0us/step
2021-07-05 07:01:35.191422: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-07-05 07:01:35.411998: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2021-07-05 07:01:35.412332: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2591990000 Hz
Epoch 1/5
1875/1875 [==============================] - 5s 2ms/step - loss: 0.2218 - accuracy: 0.9349
Epoch 2/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0975 - accuracy: 0.9706
Epoch 3/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0701 - accuracy: 0.9781
Epoch 4/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0524 - accuracy: 0.9829
Epoch 5/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0439 - accuracy: 0.9858
313/313 [==============================] - 1s 1ms/step - loss: 0.0591 - accuracy: 0.9831

可以看到，模型的预测精确度达到98%。

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Docker下运行TensorFlow过程

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