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I'm starting to learn how to use TensorFlow to do machine learning. And find out docker is pretty convenient to deploy TensorFlow to my machine. However, the example that I could found did not work on my target setting. Which is

Under ubuntu16.04 os, using nvidia-docker to host jupyter and tensorboard service together(could be two container or one container with two service). And files create from jupyter should be visible to host OS.

  • Ubuntu 16.04
  • Dokcer
  • nvidia-docker
    • Jupyter
    • Tensorboard

Jupyter container

nvidia-docker run \ --name jupyter \ -d \ -v $(pwd)/notebooks:/root/notebooks \ -v $(pwd)/logs:/root/logs \ -e "PASSWORD=*****" \ -p 8888:8888 \ tensorflow/tensorflow:latest-gpu

Tensorboard container

nvidia-docker run \ --name tensorboard \ -d \ -v $(pwd)/logs:/root/logs \ -p 6006:6006 \ tensorflow/tensorflow:latest-gpu \ tensorboard --logdir /root/logs

I tried to mount logs folder to both container, and let Tensorboard access the result of jupyter. But the mount seems did work. When I create new file in jupyter container with notebooks folder, host folder $(pwd)/notebooks just appear nothing.

I also followed the instructions in Nvidia Docker, Jupyter Notebook and Tensorflow GPU

nvidia-docker run -d -e PASSWORD='winrar' -p 8888:8888 -p 6006:6006 gcr.io/tensorflow/tensorflow:latest-gpu-py3

Only Jupyter worked, tensorboard could not reach from port 6006.

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2 Answers 2

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I was facing the same problem today.

Short answer: I'm going to assume you are using the same container for both Jupyter Notebook and tensorboard. So, as you wrote, you can deploy the container with:

nvidia-docker run -d --name tensor -e PASSWORD='winrar'\ -p 8888:8888 -p 6006:6006 gcr.io/tensorflow/tensorflow:latest-gpu-py3

Now you can access both 8888 and 6006 ports but first you need to initialize tensorboard:

docker exec -it tensor bash tensorboard --logdir /root/logs

About the other option: running jupyter and tensorboard in different containers. If you have problems mounting same directories in different containers (in the past there was a bug about that), since Docker 1.9 you can create independent volumes unlinked to particular containers. This may be a solution.

  1. Create two volumes to store logs and notebooks.
  2. Deploy both images with these volumes.

docker volume create --name notebooks docker volume create --name logs 

nvidia-docker run \ --name jupyter \ -d \ -v notebooks:/root/notebooks \ -v logs:/root/logs \ -e "PASSWORD=*****" \ -p 8888:8888 \ tensorflow/tensorflow:latest-gpu 

 nvidia-docker run \ --name tensorboard \ -d \ -v logs:/root/logs \ -p 6006:6006 \ tensorflow/tensorflow:latest-gpu \ tensorboard --logdir /root/logs
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2 Comments

Thank you for providing your solution. First solution works for me!
Is there any way to avoid: docker exec -it tensor bash and rather use sth like docker exec tensorboard --logs=... <-- This command does not work.
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As an alternative, you can also use the ML Workspace Docker image. The ML Workspace is a web IDE that combines Jupyter, TensorBoard, VS Code, and many other tools & libraries into one convenient Docker image. Deploying a single workspace instance is as simple as:

docker run -p 8080:8080 mltooling/ml-workspace:latest

All tools are accessible from the same port. You can find information on how to access TensorBoard here.

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