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https://github.com/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.
https://github.com/IntelPython/scikit-learn_bench
benchmarks daal4py hacktoberfest machine-learning machine-learning-benchmarks scikit-learn-benchmarks
Last synced: about 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/IntelPython/scikit-learn_bench
- Owner: IntelPython
- License: apache-2.0
- Created: 2018-10-02T03:30:53.000Z (almost 6 years ago)
- Default Branch: main
- Last Pushed: 2024-07-03T14:22:10.000Z (3 months ago)
- Last Synced: 2024-07-04T02:16:13.615Z (3 months ago)
- Topics: benchmarks, daal4py, hacktoberfest, machine-learning, machine-learning-benchmarks, scikit-learn-benchmarks
- Language: Python
- Homepage:
- Size: 693 KB
- Stars: 107
- Watchers: 12
- Forks: 65
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# Machine Learning Benchmarks
[](https://dev.azure.com/daal/scikit-learn_bench/_build/latest?definitionId=8&branchName=main)
**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
### 📜 Table of Contents
- [Machine Learning Benchmarks](#machine-learning-benchmarks)
- [🔧 Create a Python Environment](#-create-a-python-environment)
- [🚀 How To Use Scikit-learn\_bench](#-how-to-use-scikit-learn_bench)
- [Benchmarks Runner](#benchmarks-runner)
- [Report Generator](#report-generator)
- [Scikit-learn\_bench High-Level Workflow](#scikit-learn_bench-high-level-workflow)
- [📚 Benchmark Types](#-benchmark-types)
- [📑 Documentation](#-documentation)
## 🔧 Create a Python Environment
How to create a usable Python environment with the following required frameworks:
- **sklearn, sklearnex, and gradient boosting frameworks**:
```bash
# with pip
pip install -r envs/requirements-sklearn.txt
# or with conda
conda env create -n sklearn -f envs/conda-env-sklearn.yml
```
- **RAPIDS**:
```bash
conda env create -n rapids --solver=libmamba -f envs/conda-env-rapids.yml
```
## 🚀 How To Use Scikit-learn_bench
### Benchmarks Runner
How to run benchmarks using the `sklbench` module and a specific configuration:
```bash
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:
```bash
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:
```bash
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](sklbench/runner/README.md#arguments).
### Report Generator
To combine raw result files gathered from different environments, call the report generator:
```bash
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](sklbench/report/README.md#arguments).
### Scikit-learn_bench High-Level Workflow
```mermaid
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
```
## 📚 Benchmark Types
**Scikit-learn_bench** supports the following types of benchmarks:
- **Scikit-learn estimator** - Measures performance and quality metrics of the [sklearn-like estimator](https://scikit-learn.org/stable/glossary.html#term-estimator).
- **Function** - Measures performance metrics of specified function.
## 📑 Documentation
[Scikit-learn_bench](README.md):
- [Configs](configs/README.md)
- [Benchmarks Runner](sklbench/runner/README.md)
- [Report Generator](sklbench/report/README.md)
- [Benchmarks](sklbench/benchmarks/README.md)
- [Data Processing](sklbench/datasets/README.md)
- [Emulators](sklbench/emulators/README.md)
- [Developer Guide](docs/README.md)