How to quickly install tensorflow environment in Docker

Quickly install the tensorflow environment in Docker and use TensorFlow.

1. Download the TensorFlow image

docker pull tensorflow/tensorflow

2. Create a TensorFlow container

docker run --name corwien-tensortflow -it -p 8888:8888 -v /Users/kaiyiwang/Code/ai/notebooks:/notebooks/data tensorflow/tensorflow

Command Description

1. docker run runs the image.
2. --name creates an alias for the container.
3. -it keeps the command line running,
4. -p 8888:8888 maps the local port 8888 to http://localhost:8888/.
5. -v /Users/kaiyiwang/Code/ai/notebooks:/notebooks/data mounts the local /Users/kaiyiwang/Code/ai/notebooks folder to the /notebooks/data of the newly created container (the files created in this way can be saved to the local /Users/kaiyiwang/Code/ai/notebooks )
6. tensorflow/tensorflow is the specified image, and the default tag is latest (i.e. tensorflow/tensorflow:latest)

Execute the above command:

We can see that the TensorFlow container is created and a default login page for JupiterNotebook is given.

We can use the following command to view the executing container and the corresponding mapping port of the container in a new command window:

docker ps 

3. Open the TensorFlow container

1. You can right-click to open the connection directly from the command line, or enter http://127.0.0.1:8888 in the browser and paste token from the command line.

4. Start TensorFlow Programming

1. Click to log in and you can see the interface, and you can create a new project

2. Interpretation of tensorflow example source code

from __future__ import print_function
#Import tensorflow
import tensorflow as tf
# Input two arrays, input1 and input2, then add them together and output the result with tf.Session():
  input1 = tf.constant([1.0, 1.0, 1.0, 1.0])
  input2 = tf.constant([2.0, 2.0, 2.0, 2.0])
  output = tf.add(input1, input2)
  result = output.eval()
  print("result: ", result)

3. Run the program, and the output result is (run successfully)

result: [ 3. 3. 3. 3.]

5. Related commands

1. Close or open the TensorFlow environment

#Shut down the tensorflow container docker stop corwien-tensortflow

#Start the TensorFlow container docker start corwien-tensortflow
#Enter http://localhost:8888/ in the browser

2. Modify the read and write permissions of the file

# Check read and write permissions ls -l
#Change tensorflow to belong to corwien (system default) user sudo chown -R corwien tensorflow/
#Change tensorflow to belong to the corwien (system default) user group sudo chgrp -R corwien tensorflow/

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