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Dev add bert automated test #203
base: test_oneflow_release
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util.py Add GPU memory for printing. analysis.py Analyze log files to get loss and GPU memory. result_analysis.py Compare the two results to generate a png image. stitching_pic.py Stitching pictures. train_perbert.sh Training script. train_perbert_list.sh Training list.
This reverts commit 647dbd6.
util.py Add GPU memory for printing. analysis.py Analyze log files to get loss and GPU memory. result_analysis.py Compare the two results to generate a png image. stitching_pic.py Stitching pictures. train_perbert.sh Training script. train_perbert_list.sh Training list.
能不能把那些bash脚本改成python的,复杂的bash脚本很难维护 |
这个测试能做到无人值守吗,就是脚本自己能判断精度和性能是否达标 |
可以做到自己判断精度,而且还可以出一个分析报告。 |
之前试着改过python的方式,但是尝试失败了。主要问题是我需要非阻塞的在其他机上执行启动脚本的命令。 |
用 subprocess 模块的poll 或者 async await 都可以做到在python里面的非阻塞 |
好的,我后面用async await 修改 |
if __name__ == "__main__": | ||
main() | ||
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空行处理一下
PRINT_ITER=1 | ||
ITER_NUM=130 | ||
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NODE_IPS='10.10.0.2','10.10.0.3','10.10.0.4','10.10.0.5' |
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这个ip地址改成10.11.0.2
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NODE_IPS='10.10.0.2','10.10.0.3','10.10.0.4','10.10.0.5' | ||
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# INIT_MODEL=/opt/initial_model |
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删掉
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# ############################################################################### | ||
# # f32 adam | ||
# ############################################################################### |
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这个显得有点臃肿,能通过传参数不?
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import json | |||
import matplotlib.pyplot as plt |
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建议加requirements.txt
PYTHON="python3.8" | ||
DOCKER_USER=root | ||
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multi_machine() |
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用subprocess 或async处理下吧
# param 5 | ||
IS_F32=$5 | ||
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declare -a host_list=("10.10.0.2" "10.10.0.3" "10.10.0.4" "10.10.0.5") |
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10.11.0.2
######################################################################################## | ||
ALL_NODES=4 | ||
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declare -a host_list=("10.11.0.2" "10.11.0.3" "10.11.0.4" "10.11.0.5") |
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上面不是定义了?
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####################################################################################### | ||
# 2 run single | ||
######################################################################################## |
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这里应该也能通过传参解决
LanguageModeling/BERT/run_squad.sh
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BENCH_ROOT_DIR=/home/oyy/workspace/OneFlow-Benchmark/LanguageModeling/BERT |
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/path/to/
Add docker image multi-machine startup script and bert multi-machine execution script.