Flow-Forecast PyData Global Sprint 🏃 2020 (11-10 to 11-18)

** Attention PyData participants: Even though our official sprint is listed as being on Friday. We will be running our sprint all week long 11/10-11-18. We encourage you to participate on whatever days work best for you.

Why participate?

Flow forecast is an open-source deep learning for time series framework licensed under GPL-3.0 that aims to make it really easy to leverage deep learning for time series forecasting and classification. By participating in the sprint you will be

Contributing to an open source project that is currently being used to address many AI4Good initiatives such as COVID-19, river/stream flooding, clean energy forecasting, and much more. Additionally, many researchers and small business rely on flow-forecast to give them accurate temporal forecasts 📈.
Improving 💪 your general machine learning, programming, cloud infrastructure and time series analysis skills.
Learning the internal mechanics of flow-forecast so you can utilize at your company 🤓🎉 when the need arises (trust me at some point your company will need an effective forecast).
Building your open source profile (who doesn’t like a green commit history and real PRs they can share with future employees).
Earning cool flow-forecast swag 👕👒🎩

How can I participate?

The sprint officials begins on 11/10 1pm EST. In the meantime you can do the following:

Fill out the sign-up form online (DO THIS FIRST)

Join our Slack channel

Read the guide to contributing and watch the relevant videos

Familiarize yourself with some of our open issues items

Sign up on our JIRA site

Send us any questions that you might have.

Read our overall roadmap and focus areas

We plan on having plenty of issues suitable for all skill levels; anything from fixing documentation to porting the latest state of the art models. Or if you are interested in seeing how flow-forecast models perform on your own (public) dataset then we would be happy to help you get started. The main goal of this sprint is to add more deep learning time series models, benchmark existing models and improve our cloud provider integration. However, we will also welcome PRs that solve documentation issues or bugs.

Primary Contacts

@Isaac Godfried

@kriti mahajan

Sprint Timeline

Issue Board

Issues	Category	Required Skills	Complexity	Assignee

Issues	Category	Required Skills	Complexity
Add Temporal Fusion Transformer	New models	PyTorch, refactoring research code, Pandas	10-20 hours depending on developer skill.
Add DSA-Net Model	New models	PyTorch, refactoring research code	10-20 hours depending on developer skill.
Add Deep-AR Model	New models	PyTorch	5-10 hours depending on developer skill
Add GRU-Bayes Model	New models	PyTorch, Pandas, refactoring skills	15-25 hours
Integrate Neural ODE models/library with flow	New models	PyTorch, Pandas, ODE knowledge,	30+
Add MASE loss function	LOSS FUNCTIONS	PyTorch, Statistics	5-10 hours
Increase GPU Utilization	INfrastructure	PyTorch (data-loaders), GPU settings, Wandb	10 hours?
Create Flow Training Docker Image	INfrastructure	Docker, Python
Add more tutorials	Tutorials	PyTorch

Jira Issue Status Board