61 lines
2.4 KiB
Bash
61 lines
2.4 KiB
Bash
# -*- coding: utf-8 -*-
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# @Author: Weisen Pan
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# Load environment modules required for execution
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source /etc/profile.d/modules.sh
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# Load the GCC compiler version 11.2.0
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module load gcc/11.2.0
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# Load the OpenMPI version 4.1.3 for distributed computing
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module load openmpi/4.1.3
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# Load CUDA version 11.5 (subversion 11.5.2) for GPU acceleration
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module load cuda/11.5/11.5.2
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# Load cuDNN version 8.3 (subversion 8.3.3) for deep learning libraries
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module load cudnn/8.3/8.3.3
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# Load NCCL version 2.11 (subversion 2.11.4-1) for multi-GPU communication
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module load nccl/2.11/2.11.4-1
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# Load Python version 3.10 (subversion 3.10.4) as the programming language
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module load python/3.10/3.10.4
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# Activate the Python virtual environment for PyTorch and Horovod
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source ~/venv/pytorch1.11+horovod/bin/activate
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# Create and clean the log directory for this job
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LOG_PATH="/home/projadmin/Federated_Learning/project_EdgeFLite/records/${JOB_NAME}_${JOB_ID}"
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# Remove any existing log directory to avoid conflicts
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rm -rf ${LOG_PATH}
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# Create a fresh log directory for the current job
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mkdir -p ${LOG_PATH}
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# Prepare the local dataset storage
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DATA_PATH="${SGE_LOCALDIR}/${JOB_ID}/"
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# Copy the dataset for local processing to improve performance
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cp -r ../summit2024/simpleFL/performance_test/cifar100/data ${DATA_PATH}
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# Change to the working directory of the federated training scripts
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cd EdgeFLite
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# Execute the federated training process with the specified configuration
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python run_gkt.py \
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--is_fed=1 \ # Enable federated training mode
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--fixed_cluster=0 \ # Do not fix clusters
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--split_factor=1 \ # Set the split factor to 1
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--num_clusters=20 \ # Number of clusters to use in federated training
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--num_selected=20 \ # Number of selected clusters per round
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--arch="wide_resnetsl50_2" \ # Use the wide ResNet-50_2 architecture
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--dataset="pill_base" \ # Specify the dataset to use (Pill Base)
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--num_classes=98 \ # Number of classes in the dataset
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--is_single_branch=0 \ # Enable multi-branch training
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--is_amp=0 \ # Disable automatic mixed precision training
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--num_rounds=350 \ # Number of federated training rounds
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--fed_epochs=1 \ # Number of epochs per federated round
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--batch_size=32 \ # Batch size for training
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--crop_size=224 \ # Image crop size
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--spid="FGKT_W502_20c_350r" \ # Specify the unique session ID for logging
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--data=${DATA_PATH} # Path to the dataset
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