Search Results for "jupyterlab"
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Showing 9 open source projects for "jupyterlab"
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Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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jupyterlab-git
A Git extension for JupyterLab
A JupyterLab extension for version control using Git. To see the extension in action, open the example notebook included in the Binder demo. Open the Git extension from the Git tab on the left panel. This extension tries to handle credentials for HTTP(S) connections (if you don't have set up a credential manager). But not for other SSH connections. -
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JupyterLab LSP
Coding assistance for JupyterLab (code navigation + hover suggestions
Hover over any piece of code; if an underline appears, you can press Ctrl to get a tooltip with function/class signature, module documentation or any other piece of information that the language server provides. Critical errors have red underline, warnings are orange, etc. Hover over the underlined code to see a more detailed message. Use the context menu entry, or Alt + 🖱️ to jump to definitions/references (you can change it to Ctrl/⌘ in settings); use Alt + o to jump back. Place your... -
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Gator
Conda environment and package management extension from within Jupyter
The Mamba Navigator, a Web UI for managing conda environments. Provides Conda/Mamba environment and package management as a standalone application or as an extension for JupyterLab. -
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JupyterLite
Wasm powered Jupyter running in the browser
JupyterLite is a browser-based distribution of the Jupyter ecosystem that enables interactive computing entirely within a web browser without requiring a backend server. Built using JupyterLab components and powered by WebAssembly technologies, it allows users to run Python and other language kernels directly in the browser through tools like Pyodide or Xeus. This architecture eliminates the need for installation or server infrastructure, making it highly accessible for education, demonstrations, and lightweight data science workflows. ... -
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Jupyter Docker Stacks
Ready-to-run Docker images containing Jupyter applications
Jupyter Docker Stacks provides a curated set of ready-to-run Docker container images that bundle Jupyter applications with popular data science and computing tools, enabling users to quickly start working in a reproducible environment. These stacks support a range of use cases, from lightweight base notebook images to full featured environments that include scientific computing libraries, machine learning tools, and IDE-like notebook interfaces, all within Docker containers that run... -
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RetroLab
JupyterLab distribution with a retro look and feel
RetroLab (also known as JupyterLab Retro, previously called JupyterLab Classic) is a JupyterLab distribution with a retro look and feel, more similar to the classic Jupyter notebook. -
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Jupyter Dash
Dash v2.11+ has Jupyter support built in
Dash 2.11 and later supports running Dash apps in classic Jupyter Notebooks and in JupyterLab without the need to update the code or use the additional JupyterDash library. If you are using an earlier version of Dash, you can run Dash apps in a notebook using JupyterDash. This page documents additional options available when running Dash apps in notebooks as well as troubleshooting information. -
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ML workspace
All-in-one web-based IDE specialized for machine learning
...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. Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. Easy to deploy on Mac, Linux, and Windows via Docker. Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.By default, the workspace container has no resource constraints and can use as much of a given resource as the host’s kernel scheduler allows. -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
...This projects highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker. If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies easy on-boarding for their team members, freeing them up from complex systems admin and security processes. Administrators control data access and resource provisioning for their users. ... -
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