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1.9 KiB
1.9 KiB
Prepare the environment
Please follow the instructions shown in here to install the Deepspeech first.
File list
└── benchmark # 模型名
├── README.md # 运行文档
├── analysis.py # log解析脚本,每个框架尽量统一,可参考paddle的analysis.py
├── recoder_mp_bs16_fp32_ngpu1.txt # 单卡数据
├── recoder_mp_bs16_fp32_ngpu8.txt # 8卡数据
├── prepare.sh # 竞品PyTorch运行环境搭建
├── run_benchmark.sh # 运行脚本（包含性能、收敛性）
├── run_analysis_mp.sh # 分析8卡的脚本
├── run_analysis_sp.sh # 分析单卡的脚本
├── log
│ ├── log_sp.out # 单卡的结果
│ └── log_mp.out # 8卡的结果
└── run.sh # 全量运行脚本
The physical environment
- 单机（单卡、8卡）
- 系统：Ubuntu 16.04.6 LTS
- GPU：Tesla V100-SXM2-16GB * 8
- CPU：Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz * 96
- Driver Version: 440.64.00
- 内存：440 GB
- CUDA、cudnn Version: cuda10.2-cudnn7
- 多机（32卡） TODO
Docker 镜像,如:
- 镜像版本:
registry.baidubce.com/paddlepaddle/paddle:2.1.0-gpu-cuda10.2-cudnn7 - CUDA 版本:
10.2 - cuDnn 版本:
7
Prepare the benchmark environment
bash prepare.sh
Start benchmarking
bash run.sh
The log
{"log_file": "recoder_sp_bs16_fp32_ngpu1.txt",
"model_name": "Conformer",
"mission_name": "one gpu",
"direction_id": 1,
"run_mode": "sp",
"index": 1,
"gpu_num": 1,
"FINAL_RESULT": 23.228,
"JOB_FAIL_FLAG": 0,
"log_with_profiler": null,
"profiler_path": null,
"UNIT": "sent./sec"
}