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