ML Workspace
Last updated
Last updated
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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.
π« Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.
π Pre-installed with many popular data science libraries & tools.
π₯ Full Linux desktop GUI accessible via web browser.
π Seamless Git integration optimized for notebooks.
π Integrated hardware & training monitoring via Tensorboard & Netdata.
πͺ Access from anywhere via Web, SSH, or VNC under a single port.
π Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH.
π³ Easy to deploy on Mac, Linux, and Windows via Docker.
The workspace is equipped with a selection of best-in-class open-source development tools to help with the machine learning workflow. Many of these tools can be started from the Open Tool menu from Jupyter (the main application of the workspace):
Within your workspace you have full root & sudo privileges to install any library or tool you need via terminal (e.g., pip, apt-get, conda, or npm). You can find more ways to extend the workspace within the Extensibility section.
Install Dependencies in Notebooks: Itβs a good idea to include cells which install and load any custom libraries or files (which are not pre-installed in the workspace) that your notebook needs.
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