GitHub - ml-tooling/ml-workspace: 🛠 All-in-one web-based IDE specialized for machine learning and data science.

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🛠 All-in-one web-based IDE specialized for machine learning and data science. - GitHub - ml-tooling/ml-workspace: 🛠 All-in-one web-based IDE specialized for machine learning and data science.
All-in-one web-based development environment for machine learning

Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issues • Contribution

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.

Highlights

💫 Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.

Jupyter, JupyterLab, and Visual Studio Code web-based IDEs. 🗃 Pre-installed with many popular data science libraries & tools.

Pre-installed with many popular data science libraries & tools. 🖥 Full Linux desktop GUI accessible via web browser.

Full Linux desktop GUI accessible via web browser. 🔀 Seamless Git integration optimized for notebooks.

Seamless Git integration optimized for notebooks. 📈 Integrated hardware & training monitoring via Tensorboard & Netdata.

Integrated hardware & training monitoring via Tensorboard & Netdata. 🚪 Access from anywhere via Web, SSH, or VNC under a single port.

Access from anywhere via Web, SSH, or VNC under a single port. 🎛 Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH.

Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. 🐳 Easy to deploy on Mac, Linux, and Windows via Docker.

Getting Started

Prerequisites

The workspace requires Docker to be installed on your machine ( 📖 Installation Guide).

Start single instance

Deploying a single workspace instance is as simple as:

docker run -p 8080:8080 mltooling/ml-workspace:0.13.2

Voilà, that was easy! Now, Docker will pull the latest workspace image to your machine. This may take a few minutes, depending on your internet…
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