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### Prepare the environment
here ](../../../docs/source/install.md ) to install the Deepspeech first.
### File list
├── 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](https://github.com/mmglove/benchmark/blob/jp_0907/scripts/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（ ）
- 系统：
- GPU：
- CPU：
- Driver Version: 440.64.00
- 内存：
- CUDA、cudnn Version: cuda10.2-cudnn7
- 多机（ ）
### 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
```
```
### Start benchmarking
```
```
### The log
```
"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"
}
```
