45 lines
2.4 KiB
Bash
45 lines
2.4 KiB
Bash
# -*- coding: utf-8 -*-
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# @Author: Weisen Pan
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# Load environment modules and required dependencies
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source /etc/profile.d/modules.sh
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module load gcc/11.2.0 # Load GCC version 11.2.0
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module load openmpi/4.1.3 # Load OpenMPI version 4.1.3
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module load cuda/11.5/11.5.2 # Load CUDA version 11.5.2
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module load cudnn/8.3/8.3.3 # Load cuDNN version 8.3.3
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module load nccl/2.11/2.11.4-1 # Load NCCL version 2.11.4-1
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module load python/3.10/3.10.4 # Load Python version 3.10.4
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# Activate the virtual Python environment
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source ~/venv/pytorch1.11+horovod/bin/activate # Activate a virtual environment for PyTorch and Horovod
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# Define the log directory, clean up old records if any, and recreate the directory
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LOG_PATH="/home/projadmin/Federated_Learning/project_EdgeFLite/records/${JOB_NAME}_${JOB_ID}"
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rm -rf ${LOG_PATH} # Remove any existing log directory
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mkdir -p ${LOG_PATH} # Create a new log directory
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# Set up the local data directory and copy the dataset into it
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DATA_STORAGE="${SGE_LOCALDIR}/${JOB_ID}/" # Define a local data directory for the job
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cp -r ../summit2024/simpleFL/performance_test/cifar100/data ${DATA_STORAGE} # Copy CIFAR-100 dataset to the local directory
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# Navigate to the working directory where training scripts are located
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cd EdgeFLite # Change directory to the EdgeFLite project
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# Execute the training script with federated learning parameters
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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 \ # Allow dynamic cluster formation
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--split_factor=1 \ # Data split factor
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--num_clusters=20 \ # Number of clusters
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--num_selected=20 \ # Number of selected clients per round
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--arch="wide_resnet16_8" \ # Network architecture: Wide ResNet 16-8
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--dataset="cifar10" \ # Use CIFAR-10 dataset
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--num_classes=10 \ # Number of classes in CIFAR-10
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--is_single_branch=0 \ # Multi-branch network
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--is_amp=0 \ # Disable Automatic Mixed Precision (AMP)
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--num_rounds=300 \ # Number of federated learning rounds
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--fed_epochs=1 \ # Number of local training epochs per round
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--cifar10_non_iid="quantity_skew" \ # Non-IID data distribution: quantity skew
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--spid="FGKT_W168_20c_skew" \ # Set a specific job identifier
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--data=${DATA_STORAGE} # Path to the dataset
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