docs: change mpirun with msrun and add other notices (merge into main) #805
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CaitinZhao
reviewed
Sep 5, 2024
README.md
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| ```shell | ||
| # distributed training | ||
| # assume you have 4 GPUs/NPUs | ||
| mpirun -n 4 python train.py --distribute \ | ||
| msrun --worker_num 4 python train.py --distribute \ |
CaitinZhao
approved these changes
Sep 5, 2024
Ash-Lee233
reviewed
Sep 5, 2024
README.md
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| --model=densenet121 --dataset=imagenet --data_dir=/path/to/imagenet | ||
| ``` | ||
| > Notes: If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. | ||
| Notice that if you are using mpirun startup with 2 devices, please add `--bind-to numa` to avoid known performance error. For example: |
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using mpirun xxxx --bind_core=True to improve performance.
README.md
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| Notice that if you are using mpirun startup with 2 devices, please add `--bind-to numa` to avoid known performance error. For example: | ||
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| ```shell | ||
| mpirun --allow-run-as-root --merge-stderr-to-stdout --output-filename ./output_bind --bind-to numa -n 2 \ |
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msrun --bind_core=True --worker_num=2--local_worker_num=2 --master_port=8118
--log_dir=msrun_log --join=True --cluster_time_out=300 \
README_CN.md
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| --model densenet121 --dataset imagenet --data_dir ./datasets/imagenet | ||
| ``` | ||
| 注意，如果在两卡环境下选用mpirun作为启动方式，请添加配置项 `--bind-to numa` 增加绑核操作以规避两卡场景下的性能问题，范例代码如下： |
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如果在两卡环境下选用msrun作为启动方式--bind_core=True增加绑核操作以优化两卡性能
README_CN.md
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| 注意，如果在两卡环境下选用mpirun作为启动方式，请添加配置项 `--bind-to numa` 增加绑核操作以规避两卡场景下的性能问题，范例代码如下： | ||
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| ```shell | ||
| mpirun --allow-run-as-root --merge-stderr-to-stdout --output-filename ./output_bind --bind-to numa -n 2 \ |
configs/README.md
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| ```shell | ||
| # standalone training on a gpu or ascend device | ||
| python train.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/dataset --distribute False | ||
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| # distributed training on gpu or ascend divices | ||
| mpirun -n 8 python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet | ||
| msrun --worker_num 8 python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet |
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msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./device
docs/zh/index.md
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| --model densenet121 --dataset imagenet --data_dir ./datasets/imagenet | ||
| ``` | ||
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| 注意，如果在两卡环境下选用mpirun作为启动方式，请添加配置项 `--bind-to numa` 增加绑核操作以规避两卡场景下的性能问题，范例代码如下： |
docs/zh/index.md
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| 注意，如果在两卡环境下选用mpirun作为启动方式，请添加配置项 `--bind-to numa` 增加绑核操作以规避两卡场景下的性能问题，范例代码如下： | ||
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| ```shell | ||
| mpirun --allow-run-as-root --merge-stderr-to-stdout --output-filename ./output_bind --bind-to numa -n 2 \ |
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Ash-Lee233
reviewed
Sep 12, 2024
README.md
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| --model=densenet121 --dataset=imagenet --data_dir=/path/to/imagenet | ||
| ``` | ||
| > Notes: If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. | ||
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| Notice that if you are using msrun startup with 2 devices, please add `--bind_core=True` to avoid known performance error. For example: |
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to improve performance.
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Ash-Lee233
approved these changes
Sep 12, 2024
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