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ML Workspace
What it is
ML Workspace is a free, self-deployed web IDE for machine learning and data science. Its browser-accessible environment combines Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI. The main image includes TensorFlow, PyTorch, Keras, and scikit-learn. Git support includes Ungit, Jupytext, nbdime, and a Jupyter extension for pushing notebooks. TensorBoard handles training monitoring, while Netdata and Glances monitor hardware. The workspace can serve as a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, generally through passwordless SSH, with browser, SSH, or VNC access. Image flavors include minimal, R, Spark, and GPU variants; the GPU option requires compatible Nvidia drivers and supports CUDA 11.2. Docker is required, with images deployable on Mac, Linux, and Windows. Documentation describes token or basic authentication and configurable SSL/HTTPS. It is designed for one user, needs at least 2 CPUs and 500 MB of memory, and currently does not support non-root use.
For and against
For
- Includes several browser-based development environments
- Preloads major machine-learning libraries
- Provides Git and notebook tooling
- Offers training and hardware monitoring
- Supports remote runtimes over SSH
Against
- Docker is required for deployment
- Designed as a single-user environment
- At least 2 CPUs and 500 MB are recommended
- Non-root use is not currently supported
The verdict
ML Workspace gathers notebooks, coding tools, libraries, monitoring, and remote access in a self-hosted package. Its single-user design and Docker requirement matter when planning deployment.
Who it suits
It suits individual machine-learning and data-science developers who can self-host Docker environments. Teams needing multiple users are directed toward ML Hub instead.
Plans and prices
ML Workspace
Single-user development environment · requires Docker · at least 2 CPUs and 500MB recommended
Read 5 Oct 2026 · github.com
On the record
- Authentication
- The project recommends enabling Jupyter token authentication or Nginx basic authentication for access to preinstalled tools through the main workspace port.github.com · read 5 Oct 2026
- Deployment
- The project provides Docker images and says they can be deployed on Mac, Linux, and Windows; Docker is required.github.com · read 5 Oct 2026
- Development tools
- It includes browser-based Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.github.com · read 5 Oct 2026
- Encryption
- SSL/HTTPS can be enabled with supplied certificates or generated self-signed certificates.github.com · read 5 Oct 2026
- Flavors
- Available image flavors include minimal, R, Spark, and GPU variants.github.com · read 5 Oct 2026
- Git
- It includes Git tools such as a Jupyter extension for pushing notebooks, the Ungit web client, Jupytext, and nbdime.github.com · read 5 Oct 2026
- GPU requirements
- The GPU flavor requires compatible Nvidia drivers and supports CUDA 11.2 according to the project documentation.github.com · read 5 Oct 2026
- Included IDEs
- It provides browser-accessible Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.github.com · read 5 Oct 2026
- Integrations
- Listed tools and integrations include Git, TensorBoard, Netdata, Jupyter, JupyterLab, and Visual Studio Code.github.com · read 5 Oct 2026
- Libraries
- The main image comes preloaded with data science libraries including TensorFlow, PyTorch, Keras, and scikit-learn.github.com · read 5 Oct 2026
- ML libraries
- The main image comes preinstalled with data science libraries including TensorFlow, PyTorch, Keras, and scikit-learn.github.com · read 5 Oct 2026
- Monitoring
- It provides TensorBoard for training monitoring and Netdata and Glances for hardware monitoring.github.com · read 5 Oct 2026
- Product
- ML Workspace is a self-deployed, web-based IDE for machine learning and data science.github.com · read 5 Oct 2026
- Purpose
- ML Workspace is an all-in-one web-based IDE specialized for machine learning and data science.github.com · read 5 Oct 2026
- Remote access
- The workspace can be accessed through a browser, SSH, or VNC, and supports remote Jupyter kernels and VS Code development over SSH.github.com · read 5 Oct 2026
- Remote development
- It can serve as a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, typically through passwordless SSH.github.com · read 5 Oct 2026
- Resource needs
- The documentation says the workspace requires at least 2 CPUs and 500MB to run stably and be usable.github.com · read 5 Oct 2026
- Resource requirements
- The documentation says the workspace needs at least 2 CPUs and 500 MB of memory to run stably and be usable.github.com · read 5 Oct 2026
