62 lines
2.3 KiB
Bash
62 lines
2.3 KiB
Bash
# -*- coding: utf-8 -*-
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# @Author: Weisen Pan
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# Load necessary modules and dependencies
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source /etc/profile.d/modules.sh
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# Load GCC version 11.2.0
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module load gcc/11.2.0
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# Load 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 operations
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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)
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module load python/3.10/3.10.4
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# Activate the Python virtual environment for PyTorch 1.11 + Horovod
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source ~/venv/pytorch1.11+horovod/bin/activate
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# Configure the output log directory and clean up any existing records
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OUTPUT_LOG_DIR="/home/projadmin/Federated_Learning/project_EdgeFLite/records/${JOB_NAME}_${JOB_ID}"
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# Remove any previous log files from the directory
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rm -rf ${OUTPUT_LOG_DIR}
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# Create a fresh directory for storing logs
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mkdir -p ${OUTPUT_LOG_DIR}
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# Copy the dataset to a local directory for processing during training
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LOCAL_DATA_PATH="${SGE_LOCALDIR}/${JOB_ID}/"
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# Copy the dataset files from the performance test directory to the local directory
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cp -r ../summit2024/simpleFL/performance_test/cifar100/data ${LOCAL_DATA_PATH}
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# Switch to the working directory containing the EdgeFLite training scripts
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cd EdgeFLite
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# Run the federated learning training script with the specified settings
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python run_gkt.py \
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--is_fed=1 \ # Enable federated learning
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--fixed_cluster=0 \ # Disable fixed clusters
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--split_factor=1 \ # Set data split factor
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--num_clusters=20 \ # Specify number of clusters
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--num_selected=20 \ # Specify number of selected clients
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--arch="wide_resnet16_8" \ # Use Wide ResNet 16-8 architecture
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--dataset="cifar10" \ # Set dataset to CIFAR-10
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--num_classes=10 \ # Set number of classes
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--is_single_branch=0 \ # Use multi-branch training
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--is_amp=0 \ # Disable automatic mixed precision (AMP)
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--num_rounds=300 \ # Set number of training rounds
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--fed_epochs=1 \ # Set number of federated learning epochs per round
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--spid="fedgkt_wrn168_split1_cifar10_20clients_20choose_300rounds" \ # Set session ID
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--data=${LOCAL_DATA_PATH} # Set path to the local dataset
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