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Jupiter-x-Docker

Jupyter x Docker on Heroku

Jupyter is a tool for running interactive notebooks; basically add Python with Markdown and you've got Jupyter.

Deploy a Jupyter Notebook server on Heroku using Docker.

The big caveat

Jupyter has the ability to create new notebooks and they will 100% save on your deployed docker-based Jupyter server... but they will disappear as soon as you deploy a new version. That's because containers, by their very nature, are ephemeral by default.

This caveat doesn't mean we shouldn't do this... it just means it is a HUGE consideration when using this guide over something like http://colab.research.google.com.

So encounter this issue, We will package all your Jupyter contents, download it, and unpackage it again when we deploy.

Final Project Structure

|   .dockerignore
|   Dockerfile
|   Jupyter_x_Docker_to_Heroku.jpg
|   Pipfile
|   Pipfile.lock
|   README.md
|   
+---.github
|   \---workflows
|           publish.yml
|           
+---configuration
|       jupyter.py
|       
+---root
|       LoadUnload.ipynb
|       notebook.tar.gz
|       
\---scripts
        docker_build.ps1
        docker_build.sh
        docker_push.ps1
        docker_push.sh
        docker_run_dockerhub.ps1
        docker_run_dockerhub.sh
        docker_run_github_registry.ps1
        docker_run_github_registry.sh
        entrypoint.sh
        github_registry_push.ps1
        github_registry_push.sh
        heroku_push.ps1
        heroku_push.sh

How it's done.

1. Use pipenv and install jupyter

pip install pipenv
cd jupyter-x-docker
pipenv install jupyter --python 3.9

2. Create Jupyter Configuration

Generate Default Config

jupyter notebook --generate-config

This command creates the default jupyter_notebook_config.py file on your local machine. Mine was stored on ~/.jupyter/jupyter_notebook_config.py

Create configuration/jupyter.py

mkdir configuration
echo "" > configuration/jupyter.py

In configuration/jupyter.py add:

import os
c = get_config()

# Kernel config
c.IPKernelApp.pylab = 'inline'  # if you want plotting support always in your notebook

# Notebook config
c.NotebookApp.notebook_dir = 'root'
c.NotebookApp.allow_origin = u'jupyter-x-docker.herokuapp.com' # put your public IP Address here
c.NotebookApp.ip = '*'
c.NotebookApp.allow_remote_access = True
c.NotebookApp.open_browser = False

# ipython -c "from notebook.auth import passwd; passwd()"
c.NotebookApp.password = u"argon2:$argon2id$v=19$m=10240,t=10,p=8$98xA9epRaTToOra3j2dg/w$BCZmhp+/xaajsl2R8P57BigZvVT/KjkCqe9InvdyHwQ"
c.NotebookApp.port = int(os.environ.get("PORT", 8888))
c.NotebookApp.allow_root = True
c.NotebookApp.allow_password_change = True
c.ConfigurableHTTPProxy.command = ['configurable-http-proxy', '--redirect-port', '80']

A few noteable setup items here:

  • c.NotebookApp.notebook_dir I set as root which means you should create a directory as root for your default notebooks directory. In my case, jupyter will open right to this directory ignoring all others.
  • c.NotebookApp.password - this has to be a hashed password. To create a new one, just run ipython -c "from notebook.auth import passwd; passwd()" on your command line.
  • c.NotebookApp.port - Heroku sets this value in our environment variables thus int(os.environ.get("PORT", 8888)) as our default.

Test your new configuration locally with: jupyter notebook --config=./configuration/jupyter.py

3.Create a notebook under -> root/LoadUnload.ipynb

This will be how you can handle the ephemeral nature of Docker containers with Jupyter notebooks. Just create a new notebook called LoadUnload.ipynb, and add the following:

mode = "unload"

if mode == 'unload':
    # Zip all files in the current directory
    !tar chvfz notebook.tar.gz *
elif mode == 'load:
    # Unzip all files in the current directory
    !!tar -xv -f notebook.tar.gz

4. Add your Dockerfile

This is the absolute minimum setup here. You might want to add additional items as needed. Certain packages, especially the ones for data science, require additional installs for our docker-based linux server.

FROM python:3.9.0

ENV APP_HOME /app
WORKDIR ${APP_HOME}

COPY . ./

RUN pip install pip pipenv --upgrade
RUN pipenv install --skip-lock --system --dev

LABEL org.opencontainers.image.source https://github.com/JayantGoel001/Jupyter-x-Docker
LABEL org.opencontainers.image.description Jupyter Notebook Server built with Docker & deployed on Heroku.

RUN [ "chmod", "+x", "./scripts/entrypoint.sh" ]

CMD ["./scripts/entrypoint.sh"]

This is the one I used Jupyter-x-Docker

Since, You might need additional packages (like numpy or pandas or opencv) in your project.

FROM python:3.9.0

ENV APP_HOME /app
WORKDIR ${APP_HOME}

COPY . ./

# Install Ubuntu dependencies
# libopencv-dev = opencv dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
        tzdata \
        libopencv-dev \ 
        build-essential \
        libssl-dev \
        libpq-dev \
        libcurl4-gnutls-dev \
        libexpat1-dev \
        gettext \
        unzip \
        supervisor \
        python3-setuptools \
        python3-pip \
        python3-dev \
        python3-venv \
        python3-urllib3 \
        git \
        && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*


RUN pip install pip pipenv --upgrade

# sklearn opencv, numpy, and pandas
RUN pip install scikit-learn opencv-contrib-python numpy pandas

# tensorflow (including Keras)
RUN pip install tensorflow keras

# pytorch (cpu)
RUN apt-get update && apt-get -y install gcc mono-mcs && rm -rf /var/lib/apt/lists/*
RUN pip install torch==1.10.1+cpu torchvision==0.11.2+cpu torchaudio==0.10.1 -f https://download.pytorch.org/whl/torch_stable.html

# fastai
RUN pip install fastai

# Project installs
RUN pipenv install --skip-lock --system --dev

LABEL org.opencontainers.image.source https://github.com/JayantGoel001/Jupyter-x-Docker
LABEL org.opencontainers.image.description Jupyter Notebook Server built with Docker & deployed on Heroku.

RUN [ "chmod", "+x", "./scripts/entrypoint.sh" ]

CMD [ "./scripts/entrypoint.sh" ]

The most noteable part of this all is that (1) We are using pipenv locally and in docker and (2) We have both installed pipenv and run pipenv install --system to install all pipenv dependancies to the entire docker container (instead of in a virtual environment within the container as well).

5. Create scripts/entrypoint.sh

I perfer using a entrypoint.sh script for the CMD in Dockerfiles.

#!/bin/bash

/usr/local/bin/jupyter notebook --config=./configuration/jupyter.py

6. Build & Run Docker Locally

docker build -t jayantgoel001/jupyter-x-docker:latest .
docker run --env PORT=8888 -it -p 8888:8888 jayantgoel001/jupyter-x-docker

7. Heroku Setup

1. Create heroku app
heroku create jupyter-x-docker
  • Change jupyter-x-docker to your app name
2. Login to Heroku Container Registry
heroku container:login

7. Push & Release To Heroku

heroku container:push web -a jupyter-x-docker 
heroku container:release web -a jupyter-x-docker
  • web is the default for our Dockerfile.

8. That's it

heroku open

This should allow you to open up your project.

Additional Reference

Pipfile

[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"

[packages]
jupyter = "*"

[dev-packages]

[requires]
python_version = "3.9"