I created this repo with the intention to refine the notebooks that show how to work Data Science in GPUs using the RAPIDS library, DASK and Skorch. It is a work in progress to make them more readable but I already made them work.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
│
├── environment.yml <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `conda env export --no_builds | head -n -1 > environment.yml`
│
├── src <- Source code for use in this project.
│ │
│ ├── models <- Scripts to train models and then use trained models to make predictions
├── data <- Scripts to pull or transform data used to train models
For support, email [email protected] , [email protected] or join Ibex slack channel
KAUST Vizualization Core Lab :
Project based on the cookiecutter data science project template. #cookiecutterdatascience