Daal4Py Optimization

Dataset obtained from :
from sklearn.datasets import load_boston
from sklearn.datasets import make_classification

Steps to Run on Intel Instance:

conda create -n test -y python=3.7
conda activate test
conda install -c conda-forge -y daal4py
conda install -c conda-forge -y scikit-learn
pip install xgboost
conda install -c conda-forge -y pandas
git clone https://github.com/basilsony/Daal4Py-Optimizations
cd Daal4Py-Optimizations/src/
python3 run.py -m="Model Name" -o=100000

Steps to Run on Graviton Instance:

sudo snap install cmake --classic
sudo apt update && sudo apt install -y python3-pip python3-pandas python3-sklearn
pip3 install xgboost
git clone https://github.com/basilsony/Daal4Py-Optimizations
cd Daal4Py-Optimizations/src/
python3 run.py -m="Model Name" -o=100000

Values within Model Name (-m) parameter:

Linear Regression - lm, lm_training, lm_patch, lm_patch_training, daal_lm, daal_lm_training
logistic Regression - logit, logit_training, logit_patch, logit_patch_training, daal_logit, daal_logit_training
Random Forest - rf, rf_training, rf_patch, rf_patch_training, daal_rf, daal_rf_training
K-Means - kmeans, kmeans_training, kmeans_patch, kmeans_patch_training, daal_kmeans_training
DBSCAN - dbs, dbs_patch