JupyterLab with Tensorflow

Note

This is a fork from guumaster/playground-docker-gpu-jupyterlab I just updated cuda and some tools version. CUDA is 10.1 Jupyterlab is 1.0.0 Tensorflow 2

I will not push this to guumaster because I have not did all tests properly, but so far so good. If you find any problems, you may do a pull request to me

You need EDIT ./configure.sh first Run ./build-image.sh to configure and build

To start you container locally run ./start.sh local

Below is README from guumaster

Thanks @guumaster

Pre-requisites

Install nvidia-dirvers for your graphic card and make sure that nvidia-smi outputs information about the installed driver.

Installation

nvidia-docker

Add nvidia-docker repo and install nvidia-docker2 plugin.

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey |   sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
echo $distribution
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list |   sudo tee /etc/apt/sources.list.d/nvidia-docker.list
apt-get update
apt-get install -y nvidia-docker2
service docker restart

Then add nvidia as default-runtime in the file /etc/docker/daemon.json. Edit like this:

{
  "default-runtime": "nvidia",
  "runtimes": ... 
}

Test GPU support

You can check if your local configuration is correct and your containers would have GPU support with this commmands:

docker run --rm nvidia/cuda:9.0-base nvidia-smi

# Output something like this:
# +-----------------------------------------------------------------------------+
# | NVIDIA-SMI 415.13       Driver Version: 415.13       CUDA Version: 10.0     |
# |-------------------------------+----------------------+----------------------+
# | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
# | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
# |===============================+======================+======================|
# |   0  GeForce GTX 105...  Off  | 00000000:01:00.0 Off |                  N/A |
# | N/A   40C    P8    N/A /  N/A |   3743MiB /  4042MiB |      0%      Default |
# +-------------------------------+----------------------+----------------------+
#                                                                                
# +-----------------------------------------------------------------------------+
# | Processes:                                                       GPU Memory |
# |  GPU       PID   Type   Process name                             Usage      |
# |=============================================================================|
# +-----------------------------------------------------------------------------+

Build image

Setup .env

First copy .env-example to .env and fill the info with the data obtained from id

$ id
#   uid=1000(mr_robot) gid=1002(mr_robot) groups=1002(mr_robot)

# edit .env like this:
NB_USER=mr_robot
NB_UID=1000
NB_GID=1000

This is necessary so the notebooks would be mounted with write permissions for your host user

build with docker

docker build -t jupyter-lab .

NOTE: This is the moment to grab that coffee. It'd take long to build.

Test JupyterLab GPU support

To test if everything is ok and there is GPU support run this command:

docker run --rm jupyter-lab nvidia-smi

# Output similar to the above example

Usage

Once you have your image built. Add a persistence volume to save your notebooks and start the service.

Run docker-compose

docker-compose up

Adding notebooks

You can add notebooks as volumes shared to the container. Edit docker-compose.yml. You can use $NB_USER in the path:

    volumes:
      - ./my_notebooks:/home/$NB_USER/my_notebooks
      - ./datasets:/home/$NB_USER/datasets

fsan / playground-docker-gpu-jupyterlab