Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issues • Contribution
The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated.
Voilà, that was easy! Now, Docker will pull the latest workspace image to your machine. This may take a few minutes, depending on your internet speed. Once the workspace is started, you can access it via http://localhost:8080.
This command runs the container in background (-d), mounts your current working directory into the /workspace folder (-v), secures the workspace via a provided token (--env AUTHENTICATE_VIA_JUPYTER), provides 512MB of shared memory (--shm-size) to prevent unexpected crashes (see known issues section), and keeps the container running even on system restarts (--restart always). You can find additional options for docker run here and workspace configuration options in the section below.