| Name | nxbench JSON |
| Version |
0.1.15
JSON |
| download |
| home_page | None |
| Summary | A centralized benchmarking suite to facilitate comparative profiling of tools across graph analytic libraries and datasets |
| upload_time | 2024-12-16 21:40:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
networkx
graph
network
benchmarking
profiling
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://badge.fury.io/py/nxbench)
[](https://badge.fury.io/py/nxbench)
[](https://pre-commit.com/)
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# NxBench
<p align="center">
<img src="doc/_static/assets/nxbench_logo.png" alt="NxBench Logo" width="150"/>
</p>
**nxbench** is a comprehensive benchmarking suite designed to facilitate comparative profiling of graph analytic algorithms across NetworkX and compatible backends. Built on top of [Airspeed Velocity (ASV)](https://github.com/airspeed-velocity/asv), nxbench places an emphasis on extensible and granular performance analysis, enabling developers and researchers to optimize their graph analysis workflows efficiently and reproducibly.
## Key Features
- **Cross-Backend Benchmarking**: Leverage NetworkX's backend system to profile algorithms across multiple implementations (NetworkX, nx-parallel, GraphBLAS, and CuGraph)
- **Configurable Suite**: YAML-based configuration for algorithms, datasets, and benchmarking parameters
- **Real-World Datasets**: Automated downloading and caching of networks and their metadata from NetworkRepository
- **Synthetic Graph Generation**: Support for generating benchmark graphs using any of NetworkX's built-in generators
- **Validation Framework**: Comprehensive result validation for correctness across implementations
- **Performance Monitoring**: Track execution time and memory usage with detailed metrics
- **Interactive Visualization**: Dynamic dashboard for exploring benchmark results using Plotly Dash
- **Flexible Storage**: SQLite-based result storage with pandas integration for analysis
- **CI Integration**: Support for automated benchmarking through ASV (Airspeed Velocity)
## Installation
PyPi:
```bash
pip install nxbench
```
From a local clone:
```bash
make install
```
Docker:
```bash
# CPU-only
docker-compose -f docker/docker-compose.cpu.yaml build
# With GPU
docker-compose -f docker/docker-compose.gpu.yaml build
```
## Quick Start
1. Configure your benchmarks in a yaml file (see `configs/example.yaml`):
```yaml
algorithms:
- name: "pagerank"
func: "networkx.pagerank"
params:
alpha: 0.85
groups: ["centrality"]
datasets:
- name: "karate"
source: "networkrepository"
```
2. Run benchmarks based on the configuration:
```bash
nxbench --config 'nxbench/configs/example.yaml' benchmark run
```
3. Export results:
```bash
nxbench --config 'nxbench/configs/example.yaml' benchmark export 'results/results.csv' --output-format csv # convert benchmarked results into csv format.
```
4. View results:
```bash
nxbench viz serve # visualize results using parallel categories dashboard
```
<p align="center">
<img src="doc/_static/assets/animation.gif" alt="Parallel Categories Animation" width="1000"/>
</p>
## Advanced Command Line Interface
The CLI provides comprehensive management of benchmarks, datasets, and visualization:
```bash
# Data Management
nxbench data download karate # download specific dataset
nxbench data list --category social # list available datasets
# Benchmarking
nxbench --config 'nxbench/configs/example.yaml' -vvv benchmark run # debug benchmark runs
nxbench --config 'nxbench/configs/example.yaml' benchmark export 'results/benchmarks.sqlite' --output-format sql # export the results into a sql database
```
## Configuration
Benchmarks are configured through YAML files with the following structure:
```yaml
algorithms:
- name: "algorithm_name"
func: "fully.qualified.function.name"
params: {}
requires_directed: false
groups: ["category"]
validate_result: "validation.function"
datasets:
- name: "dataset_name"
source: "networkrepository"
params: {}
```
## Supported Backends
- NetworkX (default)
- nx-CuGraph (requires separate CuGraph installation and supported GPU hardware)
- GraphBLAS Algorithms (optional)
- nx-parallel (optional)
## Reproducible benchmarking through containerization
```bash
# Run benchmarks with GPU
NUM_GPU=1 docker-compose -f docker/docker-compose.gpu.yaml up nxbench
# Run benchmarks CPU-only
docker-compose -f docker/docker-compose.cpu.yaml up nxbench
# Start visualization dashboard
docker-compose -f docker/docker-compose.cpu.yaml up dashboard
# Run specific backend
docker-compose -f docker/docker-compose.cpu.yaml run --rm nxbench --config 'nxbench/configs/example.yaml' benchmark run --backend networkx
# View results
docker-compose -f docker/docker-compose.cpu.yaml run --rm nxbench --config 'nxbench/configs/example.yaml' benchmark export results.csv
```
## Contributing
Contributions are welcome! Please read our [Contributing Guide](CONTRIBUTING.md) for details on:
- Code style guidelines
- Development setup
- Testing requirements
- Pull request process
## License
This project is licensed under the MIT License - see [LICENSE](LICENSE) for details.
## Acknowledgments
- NetworkX community for the core graph library and dispatching support
- NetworkRepository.com for harmonized dataset access
- ASV team for benchmark infrastructure
## Contact
For questions or suggestions:
- Open an issue on GitHub
- Email: <dpysalexander@gmail.com>
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"description": "[](https://badge.fury.io/py/nxbench)\n[](https://badge.fury.io/py/nxbench)\n[](https://pre-commit.com/)\n[](https://github.com/psf/black)\n\n# NxBench\n\n<p align=\"center\">\n <img src=\"doc/_static/assets/nxbench_logo.png\" alt=\"NxBench Logo\" width=\"150\"/>\n</p>\n\n**nxbench** is a comprehensive benchmarking suite designed to facilitate comparative profiling of graph analytic algorithms across NetworkX and compatible backends. Built on top of [Airspeed Velocity (ASV)](https://github.com/airspeed-velocity/asv), nxbench places an emphasis on extensible and granular performance analysis, enabling developers and researchers to optimize their graph analysis workflows efficiently and reproducibly.\n\n## Key Features\n\n- **Cross-Backend Benchmarking**: Leverage NetworkX's backend system to profile algorithms across multiple implementations (NetworkX, nx-parallel, GraphBLAS, and CuGraph)\n- **Configurable Suite**: YAML-based configuration for algorithms, datasets, and benchmarking parameters\n- **Real-World Datasets**: Automated downloading and caching of networks and their metadata from NetworkRepository\n- **Synthetic Graph Generation**: Support for generating benchmark graphs using any of NetworkX's built-in generators\n- **Validation Framework**: Comprehensive result validation for correctness across implementations\n- **Performance Monitoring**: Track execution time and memory usage with detailed metrics\n- **Interactive Visualization**: Dynamic dashboard for exploring benchmark results using Plotly Dash\n- **Flexible Storage**: SQLite-based result storage with pandas integration for analysis\n- **CI Integration**: Support for automated benchmarking through ASV (Airspeed Velocity)\n\n## Installation\n\nPyPi:\n\n```bash\npip install nxbench\n```\n\nFrom a local clone:\n\n```bash\nmake install\n```\n\nDocker:\n\n```bash\n# CPU-only\ndocker-compose -f docker/docker-compose.cpu.yaml build\n\n# With GPU\ndocker-compose -f docker/docker-compose.gpu.yaml build\n```\n\n## Quick Start\n\n1. Configure your benchmarks in a yaml file (see `configs/example.yaml`):\n\n```yaml\nalgorithms:\n - name: \"pagerank\"\n func: \"networkx.pagerank\"\n params:\n alpha: 0.85\n groups: [\"centrality\"]\n\ndatasets:\n - name: \"karate\"\n source: \"networkrepository\"\n```\n\n2. Run benchmarks based on the configuration:\n\n```bash\nnxbench --config 'nxbench/configs/example.yaml' benchmark run\n```\n\n3. Export results:\n\n```bash\nnxbench --config 'nxbench/configs/example.yaml' benchmark export 'results/results.csv' --output-format csv # convert benchmarked results into csv format.\n```\n\n4. 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Please read our [Contributing Guide](CONTRIBUTING.md) for details on:\n\n- Code style guidelines\n- Development setup\n- Testing requirements\n- Pull request process\n\n## License\n\nThis project is licensed under the MIT License - see [LICENSE](LICENSE) for details.\n\n## Acknowledgments\n\n- NetworkX community for the core graph library and dispatching support\n- NetworkRepository.com for harmonized dataset access\n- ASV team for benchmark infrastructure\n\n## Contact\n\nFor questions or suggestions:\n\n- Open an issue on GitHub\n- Email: <dpysalexander@gmail.com>\n",
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