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job_faulty_clients_dirichlet.sh
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parallel_processes=0
num_rounds=15
num_clients=10
clients_per_round=10
key_start="faulty_dirichlet"
dist_type="non_iid_dirichlet"
label2flip={1:0,2:0,3:0,4:0,5:0,6:0,7:0,8:0,9:0,10:0,11:0,12:0,13:0}
max_server_data_size=2048
faulty_clients_ids=["0"]
dirichlet_alpha=0.3,0.7,1
check_prov_cache=True
echo " ****************** Medical Image Faulty Clients Experiments ******************"
model_names="densenet121"
dataset_names="pathmnist,organamnist,cifar10,mnist"
python -m tracefl.main --multirun data_dist.dist_type=$dist_type dirichlet_alpha=$dirichlet_alpha label2flip=$label2flip noise_rate=$label2flip faulty_clients_ids=$faulty_clients_ids do_training=True exp_key=$key_start model.name=$model_names dataset.name=$dataset_names num_clients=$num_clients num_rounds=$num_rounds clients_per_round=$clients_per_round dry_run=False | tee -a logs/train_pathmnist.log
# provenance
python -m tracefl.main --multirun data_dist.dist_type=$dist_type dirichlet_alpha=$dirichlet_alpha label2flip=$label2flip noise_rate=$label2flip faulty_clients_ids=$faulty_clients_ids do_training=False exp_key=$key_start model.name=$model_names dataset.name=$dataset_names num_clients=$num_clients num_rounds=$num_rounds clients_per_round=$clients_per_round dry_run=False check_prov_cache=$check_prov_cache parallel_processes=$parallel_processes do_provenance=True data_dist.max_server_data_size=$max_server_data_size hydra/launcher=joblib | tee logs/prov_pathological_pathmnist.log
echo " ****************** Text Faulty Clients Experiments ******************"
model_names="openai-community/openai-gpt"
dataset_names="dbpedia_14,yahoo_answers_topics"
python -m tracefl.main --multirun data_dist.dist_type=$dist_type dirichlet_alpha=$dirichlet_alpha label2flip=$label2flip noise_rate=$label2flip faulty_clients_ids=$faulty_clients_ids do_training=True exp_key=$key_start model.name=$model_names dataset.name=$dataset_names num_clients=$num_clients num_rounds=$num_rounds clients_per_round=$clients_per_round dry_run=False | tee -a logs/train_pathmnist.log
# provenance
python -m tracefl.main --multirun data_dist.dist_type=$dist_type dirichlet_alpha=$dirichlet_alpha label2flip=$label2flip noise_rate=$label2flip faulty_clients_ids=$faulty_clients_ids do_training=False exp_key=$key_start model.name=$model_names dataset.name=$dataset_names num_clients=$num_clients num_rounds=$num_rounds clients_per_round=$clients_per_round dry_run=False check_prov_cache=$check_prov_cache parallel_processes=$parallel_processes do_provenance=True data_dist.max_server_data_size=$max_server_data_size hydra/launcher=joblib | tee logs/prov_pathological_pathmnist.log