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train.sh
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train.sh
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# for single card training
# CUDA_VISIBLE_DEVICES=0
# plsc-train -c ./configs/IResNet50_MS1MV3_ArcFace_pfc10_1n8c_dp_mp_fp16o1.yaml
# for multi-node and multi-cards training
# export PADDLE_NNODES=2
# export PADDLE_MASTER="192.168.210.1:12538"
# export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
# for single-node and multi-cards training
export PADDLE_NNODES=1
export PADDLE_MASTER="127.0.0.1:12538"
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m paddle.distributed.launch \
--nnodes=$PADDLE_NNODES \
--master=$PADDLE_MASTER \
--devices=$CUDA_VISIBLE_DEVICES \
plsc-train \
-c ./configs/IResNet50_MS1MV3_ArcFace_pfc10_1n8c_dp_mp_fp16o1.yaml