57 lines
2.1 KiB
Bash
57 lines
2.1 KiB
Bash
# -*- coding: utf-8 -*-
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# @Author: Weisen Pan
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# Load necessary system modules
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source /etc/profile.d/modules.sh
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# Load the GCC module version 11.2.0
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module load gcc/11.2.0
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# Load the OpenMPI module version 4.1.3
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module load openmpi/4.1.3
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# Load the CUDA module version 11.5.2
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module load cuda/11.5/11.5.2
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# Load the cuDNN module version 8.3.3
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module load cudnn/8.3/8.3.3
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# Load the NCCL module version 2.11.4-1
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module load nccl/2.11/2.11.4-1
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# Load the Python module version 3.10.4
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module load python/3.10/3.10.4
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# Activate the virtual environment for PyTorch and Horovod
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source ~/venv/pytorch1.11+horovod/bin/activate
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# Set up the log directory and clean previous records if they exist
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LOG_OUTPUT="/home/projadmin/Federated_Learning/project_EdgeFLite/records/${JOB_NAME}_${JOB_ID}"
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rm -rf ${LOG_OUTPUT} # Remove previous log files
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mkdir -p ${LOG_OUTPUT} # Create a new directory for logs
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# Prepare local storage for the dataset by copying it to a local directory
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LOCAL_DATA_DIR="${SGE_LOCALDIR}/${JOB_ID}/"
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cp -r ../summit2024/simpleFL/performance_test/cifar100/data ${LOCAL_DATA_DIR}
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# Navigate to the EdgeFLite project directory
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cd EdgeFLite
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# Run the federated learning experiment with the specified parameters
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python run_gkt.py \
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--is_fed=1 \ # Enable federated learning
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--fixed_cluster=0 \ # Disable fixed cluster settings
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--split_factor=1 \ # Use split factor of 1
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--num_clusters=20 \ # Set the number of clusters to 20
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--num_selected=20 \ # Select 20 clients for each round
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--arch=resnet_model_110sl \ # Use ResNet110 single branch architecture
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--dataset=cifar100 \ # Use CIFAR-100 dataset
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--num_classes=100 \ # Set the number of classes to 100
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--is_single_branch=0 \ # Use multiple branches in the model
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--is_amp=0 \ # Disable automatic mixed precision
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--num_rounds=650 \ # Set the number of communication rounds to 650
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--fed_epochs=1 \ # Set the number of federated epochs to 1
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--cifar100_non_iid="quantity_skew" \ # Apply non-IID data partitioning (quantity skew)
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--spid="FGKT_R110_20c_skew" \ # Set the experiment ID
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--data=${LOCAL_DATA_DIR} # Set the path to the dataset in local storage
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