This project provides an all-in-one, web-based IDE tailored for machine learning and data science workflows. It aims to simplify deployment and accelerate productivity by offering a pre-configured environment with popular ML libraries and development tools, accessible from any web browser.

How It Works

The ML Workspace is delivered as a Docker container, providing a consistent and isolated environment. It integrates multiple web-based IDEs like Jupyter, JupyterLab, and VS Code (via code-server). The core approach is to bundle essential ML libraries (Tensorflow, PyTorch, Scikit-learn) and tools (Tensorboard, Netdata) within a single image, accessible through a unified web interface. This eliminates the need for complex local setup and dependency management, allowing users to start coding immediately.

Quick Start & Requirements

• Install/Run: docker run -p 8080:8080 mltooling/ml-workspace:0.13.2
• Prerequisites: Docker installed. For GPU support, Nvidia drivers and Nvidia Container Toolkit are required.
• Setup Time: Minutes, depending on Docker image download speed.
• Links: Getting Started, Features, FAQ

Highlighted Details

• Offers web-based access to Jupyter, JupyterLab, and VS Code.
• Includes integrated hardware and training monitoring (Tensorboard, Netdata).
• Supports seamless Git integration optimized for notebooks.
• Provides full Linux desktop GUI access via noVNC.
• Enables remote development capabilities via SSH.

Maintenance & Community

• Maintained by Benjamin Räthlein, Lukas Masuch, and Jan Kalkan.
• Active development and issue tracking on GitHub.

Licensing & Compatibility

• Licensed under Apache 2.0.
• Permissive license suitable for commercial use and integration with closed-source projects.

Limitations & Caveats

• The GPU flavor currently only supports CUDA 11.2.
• The workspace runs as a root user within the container, with plans to support non-root users in the future.
• Older CPUs lacking SSE4.2 support may cause Nginx to crash.