{"version":"1.0","type":"rich","title":"Docker for Data Science","description":"This example documents a practical workflow for containerizing data science environments with Docker, covering local macOS setups and remote Amazon EC2 instances. It shows shell commands for pulling Kaggle’s Python and R images, running Jupyter or RStudio servers with memory and CPU limits, and installing tools like tmux, htop, and pyspark inside containers. For EC2, it details mounting EBS volumes, configuring security groups, and enabling GPU access via nvidia-docker, including driver installation and clock optimization for K80 cards. The source is a markdown note with command snippets, not an interactive visualization.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-15fb85030d1d714a0e184ddb7878b52f?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}