labfunctions 0.9.0

Schedule parameterized notebooks programmatically using cli or a REST API

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:

Philosophy

LabFunctions isn't a complete MLOps solution.

We try hard to expose the right APIs to the user for the part of scheduling notebooks with reproducibility in mind.

Whenever possible we try to use well established open source tools, projects and libraries to resolve common problems. Moreover we force some good practices like code versioning, and the use of containers to run wokrloads

The idea comes from a Netflix post which suggest using notebooks like an interface or a some kind of DSL to orchestrate different workloads like Spark and so on. But it also could be used to run entire process as we said before.

The benefits of this approach is that notebooks runned could be stored and inspected for good or for bad executions. If something fails, is easy to run in a classical way: cell by cell in a local pc or in a remote server.

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

:earth_americas: Roadmap

See Roadmap draft

:post_office: Architecture

:bookmark_tabs: 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.

License

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