SVM Benchmarks

Dependencies

conda install -c intel scikit-learn pandas

Setting up the environment

Directory for saving and unloading datasets while the benchmark is running
```bash
export DATASETSROOT=<enter absolute path>

Download datasets

python workloads/load_datasets.py

Running benchmarks

For runs all workloads:

python benchmarks/svm_workload_run.py

For runs of the selected workload:

python benchmarks/svm_workload_run.py --workload a9a

You can choose library: sklearn, cuml, thundersvm, idp_sklearn For runs of the selected library. By default using sklearn with oneDAL optimizations (idp_sklearn). Example for thundersvm library:

python benchmarks/svm_workload_run.py --library thundersvm 

NOTE: for thundersvm/cuml runs need thundersvm/cuml library. Can you download with help pip or conda