** 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 π.
Improving πͺ 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 π€π when the need arises (trust me at some point your company will need an effective forecast).
Building your open source profile (who doesnβt like a green commit history and real PRs they can share with future employees).
Earning cool flow-forecast swag πππ©
How can I participate?
The sprint officials begins on 11/10 1pm EST. In the meantime you can do the following:
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
Issue Board
Issues | Category | Required Skills | Complexity | Assignee |
|---|
Add Temporal Fusion Transformer | 
| PyTorch, refactoring research code, Pandas | 10-20 hours depending on developer skill. | |
Add DSA-Net Model | | PyTorch, refactoring research code | 10-20 hours depending on developer skill. | |
Add Deep-AR Model |
| PyTorch | 5-10 hours depending on developer skill | |
Add GRU-Bayes Model |
| PyTorch, Pandas, refactoring skills | 15-25 hours | |
Integrate Neural ODE models/library with flow |
| PyTorch, Pandas, ODE knowledge, | 30+ | |
Add MASE loss function | 
| PyTorch, Statistics | 5-10 hours | |
Increase GPU Utilization | 
| PyTorch (data-loaders), GPU settings, Wandb | 10 hours? | |
Create Flow Training Docker Image |
| Docker, Python | | |
Add more tutorials | 
| PyTorch | | |
Jira Issue Status Board
