minerva-ml / Open Solution Data Science Bowl 2018
Licence: mit
Open solution to the Data Science Bowl 2018
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Data Science Bowl 2018: open solution
This is an open solution to the Data Science Bowl 2018 based on the topcoders winning solution from ods.ai.
More competitions 🎇
Check collection of public projects 🎁, where you can find multiple Kaggle competitions with code, experiments and outputs.
Disclaimer
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 😉.
Installation
Check Installation page on our Wiki, for detailed instructions.
$ 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
- collect submit from
/output/dsb/experiments/submission.csvdirectory.
User support
There are several ways to seek help:
- Kaggle discussion is our primary way of communication.
- Read project's Wiki, where we publish descriptions about the code, pipelines and neptune.
- You can submit an issue directly in this repo.
Contributing
Check CONTRIBUTING for more information.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].