dymaxionlabs / labfunctions

Run parametrized Jupyter notebooks on demand locally or in the cloud

LabFunctions

Description

LabFunctions is a library and a service that allows you to run parametrized notebooks on demand.

It was thought to empower different data roles to put notebooks into production whatever they do, this notebooks could be models, ETL process, crawlers, etc. This way of working should allow going backward and foreward in the process of building data products.

Although this tool allow different workflows in a data project, we propose this one as an example:

Status

⚠️ Although the project is considered stable please keep in mind that LabFunctions is still under active development and therefore full backward compatibility is not guaranteed before reaching v1.0.0., APIS could change.

Features

Some features can be used standalone, and others depend on each other.

Feature	Status	Note
Notebook execution	Stable	-
Workflow scheduling	Beta	This allow to schedule: every hour, every day, etc
Build Runtimes	Beta	Build OCI compliance continers (Docker) and store it.
Runtimes templates	Stable	Genereate Dockerfile based on templates
Create and destroy servers	Alpha	Create and delete Machines in different cloud providers
GPU Support	Beta	Allows to run workloads that requires GPU
Execution History	Alpha	Track notebooks & workflows executions
Google Cloud support	Beta	Support google store and google cloud as provider
Secrets managment	Alpha	Encrypt and manager private data in a project
Project Managment	Alpha	Match each git repostiroy to a project

Cluster options

It is possible to run different cluster configurations with custom auto scalling policies

Instances inside a cluster could be created manually or automatically

See a simple demo of a gpu cluster creation

https://www.youtube.com/watch?v=-R7lJ4dGI9s

🌎 Roadmap

See Roadmap draft

🏣 Architecture

📑 References & inspirations

• Notebook Innovation - Netflix
• Tensorflow metastore
• Maintainable and collaborative pipelines
• The magic of Merlin
• Scale aware approach
• [

Contributing

Bug reports and pull requests are welcome on GitHub at the issues page. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

Please refer to this document for more details about our current governance model and formal committers group.

History

Labfunctions was initially developed by Xavier Petit in the context of the needs of algorinfo.com and inspired by the following posts: Netflix and Maintainable and collaborative pipelines, during the second half of 2021.

The common cycle of work before the idea of labfunctions was to start exploring and prototyping models and processes in Jupyter Notebooks and then migrate those notebooks to packages and modules in python, finally the code was deployed as containers into production.

At that time the problem to solve was to reduce the step required from notebooks to production, then labfunctions emerge first as a module in the context of dataproc using Sanic, RQ and Papermill as main libraries to orchestrate and execute notebooks as workflows.

In 2022 Xavier Petit started working as a freelancer in DymaxionLabs. They have a similar problem to be solved, but with two extra requirements: notebooks should be reproducible, and workloads usually require GPU hardware that should be provisioned on demand. With those two needs in mind, labfunctions was born adding: the idea of a “project” which match to a Git Repository, the builds of docker containers (called runtimes in labfunctions) and the option to create servers on demand, each step with GPU support.

License

This project is licensed under Apache 2.0. Refer to LICENSE.txt.