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Showing content from https://github.com/IntelPython/scikit-learn_bench below:

IntelPython/scikit-learn_bench: scikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms.

Scikit-learn_bench is a benchmark tool for libraries and frameworks implementing Scikit-learn-like APIs and other workloads.

Benefits:

• Full control of benchmarks suite through CLI
• Flexible and powerful benchmark config structure
• Available with advanced profiling tools, such as Intel(R) VTune* Profiler
• Automated benchmarks report generation

• Machine Learning Benchmarks
  • 🔧 Create a Python Environment
  • 🚀 How To Use Scikit-learn_bench
    • Benchmarks Runner
    • Report Generator
    • Scikit-learn_bench High-Level Workflow
  • 📚 Benchmark Types
  • 📑 Documentation

How to create a usable Python environment with the following required frameworks:

• sklearn, sklearnex, and gradient boosting frameworks:

# with pip
pip install -r envs/requirements-sklearn.txt
# or with conda
conda env create -n sklearn -f envs/conda-env-sklearn.yml

• RAPIDS:

conda env create -n rapids --solver=libmamba -f envs/conda-env-rapids.yml

How to run benchmarks using the sklbench module and a specific configuration:

python -m sklbench --config configs/sklearn_example.json

The default output is a file with JSON-formatted results of benchmarking cases. To generate a better human-readable report, use the following command:

python -m sklbench --config configs/sklearn_example.json --report

By default, output and report file paths are result.json and report.xlsx. To specify custom file paths, run:

python -m sklbench --config configs/sklearn_example.json --report --result-file result_example.json --report-file report_example.xlsx

For a description of all benchmarks runner arguments, refer to documentation.

To combine raw result files gathered from different environments, call the report generator:

python -m sklbench.report --result-files result_1.json result_2.json --report-file report_example.xlsx

For a description of all report generator arguments, refer to documentation.

flowchart TB
    A[User] -- High-level arguments --> B[Benchmarks runner]
    B -- Generated benchmarking cases --> C["Benchmarks collection"]
    C -- Raw JSON-formatted results --> D[Report generator]
    D -- Human-readable report --> A

    classDef userStyle fill:#44b,color:white,stroke-width:2px,stroke:white;
    class A userStyle

Scikit-learn_bench supports the following types of benchmarks:

• Scikit-learn estimator - Measures performance and quality metrics of the sklearn-like estimator.
• Function - Measures performance metrics of specified function.

Scikit-learn_bench:

• Configs
• Benchmarks Runner
• Report Generator
• Benchmarks
• Data Processing and Storage
• Emulators
• Developer Guide

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