A simple script to get you started with ML development within a docker container. It assumes you don't know much about Docker and takes you right into your development environment without breaking a sweat. This particular image comes pre configured with pytorch, tensorflow, keras, scikit-learn and a few other ML tool-chain so you can start coding in your favorite framework right away in jupyter-notebook.

Quickstart

Open the Makefile and change the values of the following variables

CONTAINER: Use azmfaridee/dl in the image name field if you want to use the pre-built image otherwise, assign your own name for the image and build with make docker-build command
CONTAINER: Assign an easily identifiable name for your docker container e.g. faridee_project1
AVAILABLE_GPUS: Index starts at 0 and ends at 3. Use comma separated values if you need more than one gpu, e.g. AVAILABLE_GPUS=2,3
Modify all the port numbers so that there's no conflict with other users
- LOCAL_JUPYTER_PORT
- LOCAL_TENSORBOARD_PORT
- VSCODE_PORT
Finally, change PASSWORD to your desired value, you'll use this password to access your jupyter notebook and vscode development environment.

To run the docker container for the first time, use the following command:

make docker-run

You should see a prompt as following:

NV_GPU='0' docker run -it -p 18443:8443 -p 18888:8888 -p \
        16006:6006 -p 18787:8787 -v /home/azmfaridee/Documents/projects/mpsc/docker-scripts:/notebooks --name faridee_project1 azmfaridee/dl
[I 18:33:05.793 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[I 18:33:05.997 NotebookApp] [jupyter_nbextensions_configurator] enabled 0.4.1
[I 18:33:05.998 NotebookApp] Serving notebooks from local directory: /notebooks
[I 18:33:05.998 NotebookApp] The Jupyter Notebook is running at:
[I 18:33:05.998 NotebookApp] http://34a6a9a3fc1f:8888/?token=...
[I 18:33:05.998 NotebookApp]  or http://127.0.0.1:8888/?token=...
[I 18:33:05.998 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 18:33:06.002 NotebookApp] No web browser found: could not locate runnable browser.

Point your browser to the provided url from the prompt with the following changes

If you are running the container in a remote server and you know the remote IP, replace 127.0.0.1 with the remote IP e.g. 172.217.15.110
Change the port 8888 again to your LOCAL_JUPYTER_PORT.

So url

http://127.0.0.1:8888/

should become

http://172.217.15.110:18888/

in this example. Now use the PASSWORD you defined earlier to access your jupyter notebook.

You can stop the container in a number of ways

By presssing Control-C in the same prompt where you first started the container
If you want to stop the container from a different shell, use the command make docker-stop while residing in the same workinng folder.

Advanced

Resuming the Container

If you close the prompt and want to re-run the your container again, you'll need to use the following command

make docker-resume

Shell Prompt

To get access to a shell (useful for running scripts directly on the container instead jupyter notebook), use the following command

make docker-shell

Your current working directly is mounted inside the docker container in /notebooks folder so do a cd /notebooks to get to your scripts.

Using Visual Studio Code

Simply run the following command in a seperate prompt (make sure your container is running in the background)

make docker-vscode

Use the same password you you get a prompt.

Using Tensorboard

Please run the following command in a seperate prompt (make sure your container is running in the background)

make docker-tensorboard

Installing your Own Set of Packages

If you ever need to install a custom package not provided by the image you can do so by dropping into the shell and installing the package with pip

pip install the_package_you_need

Recreating the container

If you feel that you've broken your packages by trying to install something buggy, you can try to recreate the container

make docker-clean
make docker-run

Making your Changes Persistent

Recreating the container will remove any custom packages that you might have installed. If you end up installing a lot of new tools and want make them persistent across the containers, it's better to make a new image. Trace the following steps

Set a custom name to your image IMAGE=your_custom_image_name in the Makefile.
Add the install commands into the the Dockerfile followed by RUN directive. e.g.
```
RUN pip install the_package_you_need
```
Build the image with make docker-build
Run the container us usual with either make docker-run or make docker-resume command

Accessing The Docker Container Behind Firewall

If you're running the container on a server behind firewall, use ssh tunnel to access the jupyter notebook

ssh -L local_port:remote_ip:remote_port user@remote_ip -fNT

So if your jupyter notebook is hosted at 11888 port on a remote server jupyter.remoteserver.com, and you want to access it on your pc at 8888 port, the command should look like

ssh -L 8888:jupyter.remoteserver.com:11888 -fNT

mpsc-lab-umbc/docker-scripts