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updated training resnet2060 command
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recognition/arcface_torch/README.md

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More details see [eval.md](docs/install.md) in docs.
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## Training
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### 1. Single node, 1 GPUs:
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```shell
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python -m torch.distributed.launch --nproc_per_node=1 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py
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```
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### 2. Single node, 8 GPUs:
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### 1. Single node, 8 GPUs:
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```shell
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python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py
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```
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### 3. Multiple nodes, each node 8 GPUs:
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### 2. Multiple nodes, each node 8 GPUs:
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Node 0:
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```shell
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python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py
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python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py
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```
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### 3.Training resnet2060 with 8 GPUs:
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```shell
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python -m torch.distributed.launch --nproc_per_node=1 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py --network r2060
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```
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## Speed Benchmark
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![Image text](https://github.com/nttstar/insightface-resources/blob/master/images/arcface_speed.png)
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