Warning
After several impactful years, we have made the decision to archive the Kipoi repositories and end active maintenance of the project.
This is a bittersweet moment. While it’s always a little sad to sunset a project, the field of machine learning in genomics has evolved rapidly, with new technologies and platforms emerging that better meet current needs. Kipoi played an important role in its time, helping researchers share, reuse, and benchmark trained models in regulatory genomics. We’re proud of what it accomplished and grateful for the strong community support that made it possible.
Kipoi’s impact continues, however:
- The Kipoi webinar series will carry on, supporting discussions around model reuse and interpretability.
- Kipoiseq, our standard set of data-loaders for sequence-based modeling, also remains active and relevant.
Thanks to everyone who contributed, used, or supported Kipoi. It’s been a fantastic journey, and we're glad the project helped shape how models are shared in the field.
- The Kipoi Team
This repository hosts predictive models for genomics and serves as a model source for Kipoi. Each folder containing model.yaml is considered to be a single model.
- Install kipoi:
pip install kipoi-
Run
kipoi ls. This will checkout thekipoi/modelsrepo to~/.kipoi/models) -
Follow the instructions on contributing/Getting started.
To explore available models, visit http://kipoi.org/models. See kipoi/README.md and docs/using getting started for more information on how to programatically access the models from this repository using CLI, python or R.
This model source (https://github.com/kipoi/models) is included in the Kipoi config file (~/.kipoi/config.yaml) by default:
# ~/.kipoi/config.yaml
model_sources:
kipoi:
type: git-lfs
remote_url: git@github.com:kipoi/models.git
local_path: ~/.kipoi/models/
auto_update: TrueIf you wish to keep the models stored elsewhere, edit the local_path accordingly.