44 lines
2.5 KiB
Bash
44 lines
2.5 KiB
Bash
# -*- coding: utf-8 -*-
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# @Author: Weisen Pan
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# Load necessary system modules for the job
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source /etc/profile.d/modules.sh
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module load gcc/11.2.0 # Load GCC compiler
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module load openmpi/4.1.3 # Load OpenMPI for distributed computing
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module load cuda/11.5/11.5.2 # Load CUDA for GPU acceleration
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module load cudnn/8.3/8.3.3 # Load cuDNN for deep learning frameworks
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module load nccl/2.11/2.11.4-1 # Load NCCL for multi-GPU communication
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module load python/3.10/3.10.4 # Load Python 3.10 environment
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# Activate the required Python virtual environment
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source ~/venv/pytorch1.11+horovod/bin/activate # Activate PyTorch 1.11 + Horovod environment
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# Define log directory and clean up any existing records before starting
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LOG_PATH="/home/projadmin/Federated_Learning/project_EdgeFLite/records/${JOB_NAME}_${JOB_ID}" # Set log path
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rm -rf ${LOG_PATH} # Remove any existing log directory
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mkdir -p ${LOG_PATH} # Create new log directory
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# Copy the dataset to the local temporary directory
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DATA_DIR="${SGE_LOCALDIR}/${JOB_ID}/" # Set the local directory for dataset
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cp -r ../summit2024/simpleFL/performance_test/cifar100/data ${DATA_DIR} # Copy CIFAR-100 dataset to the local directory
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# Move to the directory containing the training scripts
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cd EdgeFLite # Change to EdgeFLite project directory
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# Start the federated learning training process with the specified parameters
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python run_gkt.py \
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--is_fed=1 \ # Enable federated learning
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--fixed_cluster=0 \ # Use dynamic clustering
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--split_factor=1 \ # Set data split factor
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--num_clusters=20 \ # Set the number of clusters
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--num_selected=20 \ # Number of selected clients per round
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--arch="resnet_model_110sl" \ # Model architecture (ResNet 110 with single-layer output)
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--dataset="cifar100" \ # Dataset used (CIFAR-100)
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--num_classes=100 \ # Number of classes in the dataset
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--is_single_branch=0 \ # Enable multi-branch model
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--is_amp=0 \ # Disable automatic mixed precision
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--num_rounds=650 \ # Number of federated learning rounds
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--fed_epochs=1 \ # Number of local epochs per federated round
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--spid="FGKT_R110_20c_650r" \ # Experiment ID for logging and tracking
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--data=${DATA_DIR} # Specify the path to the dataset
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