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SPECIFICTION
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ML Workspace

mltooling.org

Free plan WebWindowsmacOSLinuxSelf-hosted
Visit mltooling.org
Spec score 8.3 #7 of 32 in Data Science Platforms

What it is

ML Workspace is a self-deployed, web-based development environment for machine learning and data science. It brings browser-accessible Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI together, with libraries such as TensorFlow, PyTorch, Keras, and scikit-learn in its main image. Git-related tools include Ungit, Jupytext, nbdime, and a Jupyter extension for pushing notebooks. TensorBoard supports training monitoring, while Netdata and Glances cover hardware monitoring. The workspace can provide a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, typically through passwordless SSH, and can be accessed through a browser, SSH, or VNC. Image options include minimal, R, Spark, and GPU variants; the GPU flavor requires compatible Nvidia drivers and supports CUDA 11.2. Docker is required, with images deployable on Mac, Linux, and Windows. The project describes token or basic authentication and configurable SSL/HTTPS. It is designed for one user, and at least 2 CPUs and 500 MB of memory are recommended for stable use. The free plan costs nothing.

Spec sheet

Purpose
ML Workspace is an all-in-one web-based IDE specialized for machine learning and data science.Read 5 Oct 2026 · github.com
Product
ML Workspace is a self-deployed, web-based IDE for machine learning and data science.Read 5 Oct 2026 · github.com
Deployment
The project provides Docker images and says they can be deployed on Mac, Linux, and Windows; Docker is required.Read 5 Oct 2026 · github.com
Integrations
Listed tools and integrations include Git, TensorBoard, Netdata, Jupyter, JupyterLab, and Visual Studio Code.Read 5 Oct 2026 · github.com
Security
The documentation describes token or basic authentication options and configurable SSL/HTTPS support.Read 5 Oct 2026 · github.com
Support
The maintainers say they cannot provide individual support by email and direct users to public support channels; the page lists [email protected] for other requests.Read 5 Oct 2026 · github.com
Authentication
The project recommends enabling Jupyter token authentication or Nginx basic authentication for access to preinstalled tools through the main workspace port.Read 5 Oct 2026 · github.com
Development tools
It includes browser-based Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.Read 5 Oct 2026 · github.com
Encryption
SSL/HTTPS can be enabled with supplied certificates or generated self-signed certificates.Read 5 Oct 2026 · github.com
Flavors
Available image flavors include minimal, R, Spark, and GPU variants.Read 5 Oct 2026 · github.com
Git
It includes Git tools such as a Jupyter extension for pushing notebooks, the Ungit web client, Jupytext, and nbdime.Read 5 Oct 2026 · github.com
GPU requirements
The GPU flavor requires compatible Nvidia drivers and supports CUDA 11.2 according to the project documentation.Read 5 Oct 2026 · github.com
Included IDEs
It provides browser-accessible Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.Read 5 Oct 2026 · github.com
Libraries
The main image comes preloaded with data science libraries including TensorFlow, PyTorch, Keras, and scikit-learn.Read 5 Oct 2026 · github.com
ML libraries
The main image comes preinstalled with data science libraries including TensorFlow, PyTorch, Keras, and scikit-learn.Read 5 Oct 2026 · github.com
Monitoring
It provides TensorBoard for training monitoring and Netdata and Glances for hardware monitoring.Read 5 Oct 2026 · github.com
Remote access
The workspace can be accessed through a browser, SSH, or VNC, and supports remote Jupyter kernels and VS Code development over SSH.Read 5 Oct 2026 · github.com
Remote development
It can serve as a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, typically through passwordless SSH.Read 5 Oct 2026 · github.com
Resource needs
The documentation says the workspace requires at least 2 CPUs and 500MB to run stably and be usable.Read 5 Oct 2026 · github.com
Resource requirements
The documentation says the workspace needs at least 2 CPUs and 500 MB of memory to run stably and be usable.Read 5 Oct 2026 · github.com
Security checks
The maintainers say each minor release receives vulnerability and virus checks using Safety, ClamAV, Trivy, and Snyk via Docker Scan.Read 5 Oct 2026 · github.com
Security limitation
The documentation says using a non-root user is not currently supported and notes the general container escape risk associated with root privileges.Read 5 Oct 2026 · github.com
User model
The workspace is designed as a single-user development environment; the maintainers recommend ML Hub for multi-user deployments.Read 5 Oct 2026 · github.com

Plans and prices

ML Workspace Free Single-user development environment · requires Docker · at least 2 CPUs and 500MB recommended Read 5 Oct 2026 · github.com

Every ML Workspace plan and price →

Strong points and limits

Strong points

  • Includes Jupyter, JupyterLab, and Visual Studio Code
  • Main image includes TensorFlow and PyTorch
  • Offers remote runtimes over SSH
  • Free to use

Limits

  • Docker is required
  • Designed as a single-user environment
  • At least 2 CPUs and 500 MB are recommended
  • Non-root use is not supported

Who it suits

ML Workspace suits an individual building machine-learning or data-science projects who can deploy and manage Docker. Its single-user design is not aimed at multi-user deployments.

Verdict

ML Workspace combines familiar notebook and code tools with monitoring and remote runtimes in a self-deployed setup. Consider its single-user scope, resource needs, and root-privilege caveat before deployment.

Where Specifiction places it

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