These notebooks are not maintained, should not be expected to run with the latest release, but are a master class in getting the most out of your GPU accelerated workflows with the RAPIDS partner ecosystem.
If you would like to update the notebooks, please do, and I'll link your update below.
| Folder | Notebook Title | Description | GPU | Dataset Used |
|---|---|---|---|---|
| kaggle-> landmark | cudf_stratifiedKfold_1000x_speedup | This notebook demonstrates the cuDF implementation of a stratified kfold operation that achieved a 1000x speed up for the Google Landmark Recognition competition | ||
| kaggle-> malware | malware_time_column_explore | This notebook studies the difference between train and test datasets in order to develop a robust validation scheme. | ||
| kaggle-> malware | rapids_solution_gpu_only | This notebook contains the GPU based RAPIDS solution to achieve 0.695 private LB in 12 minutes | ||
| kaggle-> malware | rapids_solution_gpu_vs_cpu | This notebook compares the CPU versus the GPU solution to achieve 0.695 private LB | ||
| kaggle-> santander | cudf_tf_demo | This financial industry facing notebook is the cudf-tensorflow approach from the RAPIDS.ai team for Santander Customer Transaction Prediction. Placed 17/8808. Blog | ||
| kaggle-> santander | E2E_santander_pandas | This This financial data modelling notebook is the Pandas based version the RAPIDS.ai team's best single model for Santander Customer Transaction Prediction competition. Placed 17/8808. Blog | ||
| kaggle-> santander | E2E_santander | This financial data modelling notebook is the cuDF based version of the RAPIDS.ai team's best single model for Santander Customer Transaction Prediction competition. It allows you to compare cuDF performance to the Pandas version. Placed 17/8808. Blog. | ||
| kaggle-> plasticc-> notebooks | rapids_lsst_full_demo | Archive Only. This notebook demos the full CPU and GPU implementation of the RAPIDS.ai team's model that placed 8/1094 in the PLAsTiCC Astronomical Classification competition. Blog. Updated notebooks found here | MG | Kaggle PLAsTiCC-2018 dataset |
| kaggle-> plasticc-> notebooks | rapids_lsst_gpu_only_demo | Archive Only. This GPU only based notebook shows the RAPIDS speedup of the the RAPIDS.ai team's model that placed 8/1094 in the PLAsTiCC Astronomical Classification competition. Blog. Updated notebooks found here | MG | Kaggle PLAsTiCC-2018 dataset |