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generate_train_execution.py
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import argparse
def generate_train_commands(dict):
execution_command = ''
wait_until_training_ends = ''
git_command = ''
if "margin" in dict:
margin = str(dict["margin"]).replace('.', '')
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}_k_{dict["kernel_size"]}_m_{margin}.json' \
f' &> output/training/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-m-{margin}-out.txt &'
wait_until_training_ends = f'!while ! grep "<Figure" ' \
f'output/training/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-m-{margin}-out.txt;do ' \
f'tail -n 30 output/training/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-m-{margin}-out.txt; sleep 10;done'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push {dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-m-{margin} results to github"\n' \
'!git push origin master\n'
elif 'kernel_size' in dict:
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}_k_{dict["kernel_size"]}.json' \
f' &> output/training/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-out.txt &'
wait_until_training_ends = f'!while ! grep "<Figure" ' \
f'output/training/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-out.txt;do ' \
f'tail -n 30 output/training/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-out.txt; sleep 10;done'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push {dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]} results to github"\n' \
'!git push origin master\n'
else:
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}.json' \
f' &> output/training/{dict["model"]}-{dict["filters"]}-out.txt &'
wait_until_training_ends = f'!while ! grep "<Figure" ' \
f'output/training/{dict["model"]}-{dict["filters"]}-out.txt;do ' \
f'tail -n 30 output/training/{dict["model"]}-{dict["filters"]}-out.txt; sleep 10;done'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push {dict["model"]}-{dict["filters"]} results to github"\n' \
'!git push origin master\n'
if len(execution_command) > 0:
print(execution_command)
print()
print(wait_until_training_ends)
print()
print(git_command)
def generate_evaluate_commands(dict):
execution_command = ''
git_command = ''
if "margin" in dict:
margin = str(dict["margin"]).replace('.', '')
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} --mode evaluate ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}_k_{dict["kernel_size"]}_m_{margin}.json' \
f' &> output/evaluation/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-m-{margin}-out.txt'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push evaluation {dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-m-{margin} results to github"\n' \
'!git push origin master\n'
elif "kernel_size" in dict:
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} --mode evaluate ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}_k_{dict["kernel_size"]}.json' \
f' &> output/evaluation/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-out.txt'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push evaluation {dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]} results to github"\n' \
'!git push origin master\n'
else:
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} --mode evaluate ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}.json' \
f' &> output/evaluation/{dict["model"]}-{dict["filters"]}-out.txt'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push evaluation {dict["model"]}-{dict["filters"]} results to github"\n' \
'!git push origin master\n'
if len(execution_command) > 0:
print(execution_command)
print()
print(git_command)
def generate_evaluate_best_val_loss_commands(dict):
execution_command = ''
git_command = ''
if "margin" in dict:
margin = str(dict["margin"]).replace('.', '')
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} --mode evaluate-best ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}_k_{dict["kernel_size"]}_m_{margin}.json' \
f' &> output/evaluation/best/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-m-{margin}-out.txt'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push evaluation best val loss {dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-m-{margin} results to github"\n' \
'!git push origin master\n'
elif "kernel_size" in dict:
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} --mode evaluate-best ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}_k_{dict["kernel_size"]}.json' \
f' &> output/evaluation/best/{dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]}-out.txt'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push evaluation best val loss {dict["model"]}-{dict["filters"]}-k-{dict["kernel_size"]} results to github"\n' \
'!git push origin master\n'
else:
execution_command = f'!python3 stack_over_flow_qa_eval.py --model {dict["model"]} --mode evaluate-best ' \
f'--conf_file conf/stack_over_flow_conf_f_{dict["filters"]}.json' \
f' &> output/evaluation/best/{dict["model"]}-{dict["filters"]}-out.txt'
git_command = '!git status\n' \
'!git pull origin master\n' \
'!git add -A\n' \
f'!git commit -am "push evaluation best val loss {dict["model"]}-{dict["filters"]} results to github"\n' \
'!git push origin master\n'
if len(execution_command) > 0:
print(execution_command)
print()
print(git_command)
embeddings = [{'model': 'embedding', 'filters': 4000, 'kernel_size': 2},
{'model': 'embedding', 'filters': 4000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'embedding', 'filters': 4000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'embedding', 'filters': 4000, 'kernel_size': 2, 'margin': 0.2}]
attentions = [{'model': 'attention', 'filters': 4000, 'kernel_size': 2},
{'model': 'attention', 'filters': 4000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'attention', 'filters': 4000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'attention', 'filters': 4000, 'kernel_size': 2, 'margin': 0.2}]
attentions_with_bn = [{'model': 'attention-with-bn', 'filters': 4000, 'kernel_size': 2},
{'model': 'attention-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'attention-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'attention-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.2}]
cnns = [{'model': 'cnn', 'filters': 50},
{'model': 'cnn', 'filters': 50, 'kernel_size': 2},
{'model': 'cnn', 'filters': 100},
{'model': 'cnn', 'filters': 100, 'kernel_size': 2},
{'model': 'cnn', 'filters': 200},
{'model': 'cnn', 'filters': 200, 'kernel_size': 2},
{'model': 'cnn', 'filters': 500},
{'model': 'cnn', 'filters': 500, 'kernel_size': 2},
{'model': 'cnn', 'filters': 1000},
{'model': 'cnn', 'filters': 1000, 'kernel_size': 2},
{'model': 'cnn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'cnn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'cnn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'cnn', 'filters': 1000, 'kernel_size': 3},
{'model': 'cnn', 'filters': 1000, 'kernel_size': 4},
{'model': 'cnn', 'filters': 2000},
{'model': 'cnn', 'filters': 2000, 'kernel_size': 2},
{'model': 'cnn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'cnn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'cnn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'cnn', 'filters': 2000, 'kernel_size': 3},
{'model': 'cnn', 'filters': 2000, 'kernel_size': 4},
{'model': 'cnn', 'filters': 4000},
{'model': 'cnn', 'filters': 4000, 'kernel_size': 2},
{'model': 'cnn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'cnn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'cnn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'cnn', 'filters': 4000, 'kernel_size': 3},
{'model': 'cnn', 'filters': 4000, 'kernel_size': 4}]
shared_cnns = [
{'model': 'shared-cnn', 'filters': 1000, 'kernel_size': 2},
{'model': 'shared-cnn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'shared-cnn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'shared-cnn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'shared-cnn', 'filters': 2000, 'kernel_size': 2},
{'model': 'shared-cnn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'shared-cnn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'shared-cnn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'shared-cnn', 'filters': 4000, 'kernel_size': 2},
{'model': 'shared-cnn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'shared-cnn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'shared-cnn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.2}
]
cnns_with_bn = [
{'model': 'cnn-with-bn', 'filters': 1000, 'kernel_size': 2},
{'model': 'cnn-with-bn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'cnn-with-bn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'cnn-with-bn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'cnn-with-bn', 'filters': 2000, 'kernel_size': 2},
{'model': 'cnn-with-bn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'cnn-with-bn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'cnn-with-bn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'cnn-with-bn', 'filters': 4000, 'kernel_size': 2},
{'model': 'cnn-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'cnn-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'cnn-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.2}
]
shared_cnns_with_bn = [
{'model': 'shared-cnn-with-bn', 'filters': 1000, 'kernel_size': 2},
{'model': 'shared-cnn-with-bn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'shared-cnn-with-bn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'shared-cnn-with-bn', 'filters': 1000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'shared-cnn-with-bn', 'filters': 2000, 'kernel_size': 2},
{'model': 'shared-cnn-with-bn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'shared-cnn-with-bn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'shared-cnn-with-bn', 'filters': 2000, 'kernel_size': 2, 'margin': 0.2},
{'model': 'shared-cnn-with-bn', 'filters': 4000, 'kernel_size': 2},
{'model': 'shared-cnn-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.05},
{'model': 'shared-cnn-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.1},
{'model': 'shared-cnn-with-bn', 'filters': 4000, 'kernel_size': 2, 'margin': 0.2}
]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='generate train execution')
parser.add_argument('--mode', metavar='mode', type=str, default='training',
help='--mode <training|evaluate|evaluate-best>')
args = parser.parse_args()
mode = args.mode
if mode == 'training':
print('--------- cnn --------------')
for cnn in cnns:
generate_train_commands(cnn)
print('--------- shared-cnn --------------')
for cnn in shared_cnns:
generate_train_commands(cnn)
print('--------- cnn with bn --------------')
for cnn in cnns_with_bn:
generate_train_commands(cnn)
print('--------- shared-cnn with bn --------------')
for cnn in shared_cnns_with_bn:
generate_train_commands(cnn)
print('--------- embedding --------------')
for embedding in embeddings:
generate_train_commands(embedding)
print('--------- attention --------------')
for attention in attentions:
generate_train_commands(attention)
print('--------- attentions-with-bn --------------')
for attention_with_bn in attentions_with_bn:
generate_train_commands(attention_with_bn)
elif mode == 'evaluate':
print('--------- cnn --------------')
for cnn in cnns:
generate_evaluate_commands(cnn)
print('--------- shared-cnn --------------')
for cnn in shared_cnns:
generate_evaluate_commands(cnn)
print('--------- cnn with bn --------------')
for cnn in cnns_with_bn:
generate_evaluate_commands(cnn)
print('--------- shared-cnn with bn --------------')
for cnn in shared_cnns_with_bn:
generate_evaluate_commands(cnn)
print('--------- embedding --------------')
for embedding in embeddings:
generate_evaluate_commands(embedding)
print('--------- attention --------------')
for attention in attentions:
generate_evaluate_commands(attention)
print('--------- attentions-with-bn --------------')
for attention_with_bn in attentions_with_bn:
generate_evaluate_commands(attention_with_bn)
elif mode == 'evaluate-best':
print('--------- cnn --------------')
for cnn in cnns:
generate_evaluate_best_val_loss_commands(cnn)
print('--------- shared-cnn --------------')
for cnn in shared_cnns:
generate_evaluate_best_val_loss_commands(cnn)
print('--------- cnn with bn --------------')
for cnn in cnns_with_bn:
generate_evaluate_best_val_loss_commands(cnn)
print('--------- shared-cnn with bn --------------')
for cnn in shared_cnns_with_bn:
generate_evaluate_best_val_loss_commands(cnn)
print('--------- embedding --------------')
for embedding in embeddings:
generate_evaluate_best_val_loss_commands(embedding)
print('--------- attention --------------')
for attention in attentions:
generate_evaluate_best_val_loss_commands(attention)
print('--------- attentions-with-bn --------------')
for attention_with_bn in attentions_with_bn:
generate_evaluate_best_val_loss_commands(attention_with_bn)
else:
parser.print_help()
sys.exit()