

Flagperf
Overview :
FlagPerf, developed collaboratively by the Zhiyuan Institute and AI hardware manufacturers, is an integrated AI hardware evaluation engine designed to establish an industry practice-oriented metric system for assessing the actual capabilities of AI hardware across software stack combinations (models + frameworks + compilers). The platform supports a multidimensional evaluation metric system covering scenarios of large model training and inference, and accommodates multiple training frameworks and inference engines, connecting AI hardware and software ecosystems.
Target Users :
FlagPerf is targeted at AI hardware manufacturers, researchers, and developers who need a fair and comprehensive platform to evaluate and compare the performance of different AI hardware. The platform's multidimensional assessments and open-source features make it an essential tool for technology evaluation in the AI field.
Use Cases
NVIDIA tests the performance of its A100 chip using FlagPerf.
Baidu's PaddlePaddle team utilizes FlagPerf to evaluate the performance of the integrated Llama model.
Huawei's MindSpore team tests framework performance through FlagPerf.
Features
Construct a multidimensional evaluation metric system including performance metrics, resource usage indicators, and ecosystem adaptability metrics.
Support diverse scenarios and tasks, covering over 30 classic models in fields such as computer vision and natural language processing.
Support multiple training frameworks and inference engines like PyTorch and TensorFlow, with collaborations with domestic frameworks such as PaddlePaddle and MindSpore.
Support various testing environments to comprehensively assess single-card, single-machine, and multi-machine performance.
Strict review of submitted codes to ensure a fair testing process and equitable results.
Open-source all testing codes to ensure reproducibility of the testing process and data.
How to Use
1. Install Docker and set up the Python environment.
2. Ensure that server configurations, such as hardware drivers, networking, and hardware virtualization, are fully set up.
3. Download the FlagPerf project code and deploy it on the server.
4. Modify the machine configuration files, including hardware settings and test environment parameters.
5. Start the tests, selecting from baseline specifications, operation assessments, training evaluations, or inference tests as needed.
6. Review test results and logs to analyze the performance of the AI hardware.
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