How to run TensorBoard in Docker container without root privileges? - docker

Question

I am running tensorflow-gpu in a Docker container.
At the moment I am only able to run and access TensorBoard when I access the running Docker container using root privileges. I would like to accomplish this without using root privileges. How can this be accomplished?
Here some information on what I am doing and what worked out:
I am running a tensorflow-gpu using the provided docker containers from TensorFlow using the following command.
$ docker run \
-u $(id -u username):$(id -g username) \
-it --rm --runtime=nvidia \
-v $(realpath ~/data/workspace/notebooks):/tf/notebooks \
-v $(realpath ~/data/workspace/):/tf/workspace \
-v $(realpath ~/data/images/):/tf/images \
-p 8888:8888 -p 6007-6015:6007-6015 tensorflow/tensorflow:2.0.0a0-gpu-py3-jupyter
In the command line for starting container I added additional ports for TensorBoard.
I accomplished to run TensorBoard when doing the following.
The container is running (using the commands above for startup)
→ each attempt to run and access the TensorBoard out of the running Jupyter notebook fails
From the docker host PC I run the following commands:
$ docker psto get the container name
$ sudo docker exec -it <container name> bash
→ I tried this with and without sudo, without the command below will not work
tf-docker /tf > tensorboard --logdir <log directory> --port 6007
Now I am able to access the TensorBoard on localhost:6007
I am new to Docker, TensorFlow, and I am a newcomer to Linux (Ubuntu).
I would like to accomplish what I described above without the usage of root privileges.
Is there a way to do it without?
What would be the best/correct way?
What is your best practice advice?
Edit 2019-06-24:
I do not know why it did not workout in the first place, perhaps I used the wrong port. This is what I accomplished until now.
I start the container using the following command line where I changed the port for TensorBoard to 6006
$ docker run \
-u $(id -u username):$(id -g username) \
-it --rm --runtime=nvidia \
-v $(realpath ~/data/workspace/notebooks):/tf/notebooks \
-v $(realpath ~/data/workspace/):/tf/workspace \
-v $(realpath ~/data/images/):/tf/images \
-p 8888:8888 -p 6006:6006 tensorflow/tensorflow:2.0.0a0-gpu-py3-
jupyter
Then from the command line I start a bash shell inside the docker container without using root privileges: $ docker exec -it <container name> bash
After that, I start TensorBoard and use the link in the out put in a webbrowser: tf-docker /tf > tensorboard --logdir <log directory> --port 6007
Instead of the previous command I could also start Tensorboard from Jupyter notebook.
%reload_ext tensorboard.notebook
%tensorboard --logdir=<log directory> --port=6006
Edit 2019-10-09:
Since using the TensorFlow 2.0.0 release with TensorBoard 2.0.0 I have to start TensorBoard the following:
$ tensorboard --logdir=<log directory> --host 0.0.0.0 --port 6006
Without explicitly adding the host option it does not work.

Answer 1

The steps I followed and I could visualise the results with tensorboard:
when creating a the container, open/map an external port for tensorboard:
> nvidia-docker run -d --name tkra_tensorb --ipc=host -it -p 8513:8090
> -p 3014:6006 -v /data:/data tkra_tb
inside the container, run tensorboard:
> tensorboard --logdir /data/tkra/MyDatasets/resnet101/checkpoints/
> --host 0.0.0.0 --port 6006
Open tensorboard in my browser: <server_address>:3014