Open solution to the Data Science Bowl 2018
Check collection of public projects :gift:, where you can find multiple Kaggle competitions with code, experiments and outputs.
In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script :wink:.
Check Installation page on our Wiki, for detailed instructions.
bash $ 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
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.