This is an open solution to the Data Science Bowl 2018.

1. Deliver open, ready-to-use and extendable solution to this competition. This solution should - by itself - establish solid benchmark, as well as provide good base for your custom ideas and experiments.
2. Encourage more Kagglers to start working on the Data Science Bowl, test their ideas and learn advanced data science.

Check Installation page on our Wiki, for instructions.

1. get repository, install PyTorch then remaining requirements
2. register to Neptune
3. run experiment:

$ neptune login
$ neptune send main.py --worker gcp-gpu-large --environment pytorch-0.2.0-gpu-py3 -- train_evaluate_predict_pipeline --pipeline_name unet_multitask

4. collect submit from /output/dsb/experiments/submission.csv directory.

There are several ways to seek help:

1. Kaggle discussion is our primary way of communication.
2. Read project's Wiki, where we publish descriptions about the code, pipelines and neptune.
3. You can submit an issue directly in this repo.

Check CONTRIBUTING for more information.