# ml-tooling/ml-workspace

🛠 All-in-one web-based IDE specialized for machine learning and data science.

Repository: https://github.com/ml-tooling/ml-workspace
Canonical: https://ross.abutalabs.com/products/ml-workspace
Homepage: https://mltooling.org/ml-workspace
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: machine-learning, deep-learning, data-science, docker, jupyter, jupyter-lab, python, anaconda, tensorflow, pytorch, neural-networks, data-analysis, scikit-learn, r, gpu, jupyter-notebook, kubernetes, data-visualization, vscode, nlp
Last push: 2024-07-26T07:39:39+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2655, "days_push": 768, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3544, forks 458 (observed 2026-08-28T04:08:09.336089+00:00)

## What it is
ML Workspace is an all-in-one web-based IDE Docker image specialized for machine learning and data science. It bundles Jupyter, JupyterLab, VS Code, a Linux desktop GUI, and popular data science libraries (TensorFlow, PyTorch, scikit-learn) into a single deployable container.

## Use cases
- spin up a preconfigured jupyter environment for machine learning
- run a remote jupyter kernel or vscode server over ssh
- access a full linux desktop with data science tools from a browser
- train pytorch or tensorflow models with gpu support in docker
- set up a reproducible data science workspace on kubernetes
- monitor training runs with tensorboard and netdata

## When to choose
- you want a batteries-included ML development environment deployable in minutes via Docker
- you need browser-based access to Jupyter, VS Code, and a Linux desktop from one port
- you want preinstalled data science libraries without local setup hassle

## When to avoid
- you need a lightweight minimal image or custom toolchain
- you require actively developed features - the project's latest release is mid-2024 and appears in maintenance
- you prefer native local IDEs over containerized web-based environments

## Facets
- artifact type: application
- maturity: maintenance
- function: developer-tools, data-science, machine-learning, deep-learning, data-visualization, nlp
- domain: machine-learning, data-science, developer-tools, deep-learning
- platform: windows, self-hosted
- tags: jupyter, vscode, web-ide, tensorflow, pytorch, gpu, remote-kernel, ssh, vnc, anaconda, docker, linux, macos, web-server, kubernetes

## Member repositories
- ml-tooling/ml-workspace (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.336089+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:34:26.372040+00:00, confidence not recorded.
  - readme: https://github.com/ml-tooling/ml-workspace (fetched 2026-08-28T04:08:09.336089+00:00, sha e55d7b6faeb0)
  - homepage: https://mltooling.org/ml-workspace (fetched 2026-08-29T09:28:33.426258+00:00, sha 6972880bd0d2)
- Data as of 2026-08-30T08:39:29.467469+00:00.