gzupark/jupyterlab repository overview

⁠JupyterLab Dockerfiles

This is JupyterLab's Dockerfiles, create and deploy them to my Docker Hub⁠.

Check the full list of tags⁠ for the available images.

⁠Image Description

Base OS is Ubuntu and use the Miniconda3 environment.

• Ubuntu
  • 16.04
  • 18.04
• GPU
  • CUDA 10.1
• Miniconda3
  • Python 3.7.3
    • numpy
    • pandas
    • matplotlib
    • pillow
• Machine Learning (ml)
  • scipy
  • scikit-learn
  • nltk
  • opencv-python
  • tensorflow==1.14.0
  • pytorch==1.1.0
• C++
  • xeus-cling

⁠Tag Rules

• latest: same as xenial-py3
• latest-gpu: same as xenial-gpu-py3
• xenial: Ubuntu16.04
• bionic: Ubuntu18.04
• -py3: Python 3.7.3
• -gpu: enable to use CUDA
• -ml: pre-installed machine learning packages
• -cpp: enable to use C++ environment from xeus-cling⁠ which is the jupyter kernel for the C++

⁠Running Containers

$ docker run -it --rm -p 8888:8888 gzupark/jupyterlab

Run a JupyterLab server, navigate to localhost:8888 in your browser. Then, type default jupyter password which is "jupyterlab", you can see the guide if you want to change it through tutorial_change_passwd.ipynb in the workspace.

Start a machine learning packages and C++ environment:

$ docker run -it --rm -p 8888:8888 gzupark/jupyerlab:py3-ml-cpp

Want to mount with your local machine:

$ docker run -it --rm -v $(realpath ~/project):/workspace -p 8888:8888 gzupark/jupyterlab

Want to use GPU version:

$ docker run -it --rm --runtime=nvidia -p 8888:8888 gzupark/jupyterlab:latest-gpu