Ready-to-run Docker images containing Jupyter applications
The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
An API Client package to access the APIs for NBA.com
Master the fundamentals of machine learning, deep learning, and mathematical optimization by building key concepts and models from scratch using Python.
Collection of google colaboratory notebooks for fast and easy experiments
A reactive Python kernel for Jupyter notebooks.
Productionise & schedule your Jupyter Notebooks as easily as you wrote them.
A cli tool to convert and manage jupyter notebook blogs. Proudly host your notebooks even as a static site.
Easy to use test framework for Jupyter Notebooks
A web frontend for scheduling Jupyter notebook reports
Project page for "The Debugging Book"
Inline data annotator for Jupyter notebooks
Turn a Git repo into a collection of interactive notebooks. This is Binder's user documentation repository.
massively parallel experimentation with Jupyter and AWS Lambda 🐑🌩📒
A Python and R autograding solution
Automated Quantum Mechanical Environments (AQME) offers transparent and reproducible workflows available for Jupyter Notebooks and command lines, including: 1) RDKit- and CREST-based conformer generation, 2) QM input file creation, 3) post-processing of QM output files, 4) generation of xTB, DFT and RDKit descriptors. https://aqme.readthedocs.io
Cell-by-cell testing for production Jupyter notebooks in JupyterLab
Convert monolithic Jupyter notebooks 📙 into maintainable Ploomber pipelines. 📊
This repo covers Kubeflow Environment with LABs: Kubeflow GUI, Jupyter Notebooks on pods, Kubeflow Pipelines, Experiments, KALE, KATIB (AutoML: Hyperparameter Tuning), KFServe (Model Serving), Training Operators (Distributed Training), Projects, etc.
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