yinyang/base repository overview

⁠Contents

• CUDA 11.0 and CUDNN 8 (inherited from the official CUDA image⁠)
• Miniconda
• Jupyter notebook IDE with multiple programming languages:
  • Python
  • Javascript
  • C++
  • Go

⁠How to install

⁠Step 1: install Nvidia CUDA.

• Linux: run the commands from Nvidia⁠.

• Windows:

  • Go to Windows Update, sign up and switch to the fast track of the Windows Insider Program.
  • Install Windows Subsystem for Linux (WSL), and install Ubuntu from Microsoft App Store.
  • Install the Nvidia driver for WSL⁠.
  • Open WSL, run the commands from Nvidia⁠.

• Jetson:

  • Flash your device with the latest Jetpack.

• Restart computer after the installation.

⁠Step 2: install Nvidia-Docker.

• Follow the instructions from Nvidia⁠.

• On Windows, do not use Docker Desktop for Windows. Instead, install nvidia-docker using WSL command line as above.

⁠Step 3: pull this docker image:

sudo docker pull yinyang/base

• On Jetson, run: sudo docker pull yinyang/base:jetson

⁠How to run

⁠Step 1: run this docker image:

• Linux and Windows WSL: sudo docker run --rm -it --gpus all --net host yinyang/base

• Jetson: sudo docker run --rm -it --gpus all --net host yinyang/base:jetson

⁠Step 2: start Jupyter notebook inside docker container:

jupyter notebook password
jupyter notebook --ip 0.0.0.0 --port 8888 --no-browser --allow-root

⁠Step 3: open localhost:8888 in a web browser.

⁠How to extend this image

• Step 1: run this docker image with sudo docker run --rm -it --gpus all --name base_container yinyang/base.

• Step 2: install your own packages. For example:

  • conda install your_own_package
  • apt install another_package

• Step 3: Detach from the docker image with Ctrl-P, Ctrl-Q, and then run docker commit base_container your_new_image_name.

⁠License

Creative Commons⁠