The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
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Updated
Jun 13, 2024 - Python
The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
A web frontend for scheduling Jupyter notebook reports
Example project with a CNN to train a Pokémon type classifier.
Cell-by-cell testing for production Jupyter notebooks in JupyterLab
📝 Pytest plugin for testing notebooks
Jupyter Notebook Remote Scheduler for Argo on Kubernetes
Example MLOps/ML-training pipeline running with no cloud infrastructure except a public personal (free) Github acount.
pre-commit hooks for papermill (https://github.com/nteract/papermill)
Python library to run ML/data pipelines on stateless compute infrastructure (that may be ephemeral or serverless). Please see the documentation site with more details and demo:
Run and publish parameterised Jupyter notebooks using Faculty platform
Microservice to generate Jupyter reports combining papermill and nbconvert.
A papermill engine and CLI tool that posts success/failure of notebook execution to Slack.
Add a description, image, and links to the papermill topic page so that developers can more easily learn about it.
To associate your repository with the papermill topic, visit your repo's landing page and select "manage topics."