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improve evaluation workflow and figure plot scripts; TODO: need to re…
…run the evaluation data, provisioning time seems too long
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,32 @@ | ||
| sparklines: | ||
| - title: CPU usage | ||
| rate-ms: 500 | ||
| scale: 0 | ||
| sample: ps -A -o %cpu | awk '{s+=$1} END {print s}' | ||
| - title: Free memory pages | ||
| rate-ms: 500 | ||
| scale: 0 | ||
| sample: free -w | grep 'Mem' | awk '{print $3}' | ||
|
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| runcharts: | ||
| - title: TEE provider monitor | ||
| rate-ms: 1000 | ||
| scale: 2 | ||
| items: | ||
| - label: TEE provider | ||
| sample: top -bn1 | grep sgx | grep -v grep | awk '{print $9}' | ||
| - title: blockchain node monitor | ||
| rate-ms: 1000 # sampling rate, default = 1000 | ||
| scale: 2 # number of digits after sample decimal point, default = 1 | ||
| legend: | ||
| enabled: true # enables item labels, default = true | ||
| details: true # enables item statistics: cur/min/max/dlt values, default = true | ||
| items: | ||
| - label: blockchain node0 | ||
| sample: top -bn1 -p $(ps -ef | grep node0 | grep -v grep | awk '{print $2}') | grep fisco | grep -v grep | awk '{print $9}' | ||
| - label: blockchain node1 | ||
| sample: top -bn1 -p $(ps -ef | grep node1 | grep -v grep | awk '{print $2}') | grep fisco | grep -v grep | awk '{print $9}' | ||
| - label: blockchain node2 | ||
| sample: top -bn1 -p $(ps -ef | grep node2 | grep -v grep | awk '{print $2}') | grep fisco | grep -v grep | awk '{print $9}' | ||
| - label: blockchain node3 | ||
| sample: top -bn1 -p $(ps -ef | grep node3 | grep -v grep | awk '{print $2}') | grep fisco | grep -v grep | awk '{print $9}' |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,112 @@ | ||
| import matplotlib.pyplot as plt | ||
| import matplotlib | ||
| import numpy as np | ||
| import linecache | ||
|
|
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| matplotlib.rcParams['font.size'] = 14 | ||
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| labels = ['alex', 'dense161', 'res18', 'squeeze1.0', 'vgg16'] | ||
| send_contract_call = [] | ||
| off_chain_pre_check = [] | ||
| model_inference = [] | ||
| generate_quote = [] | ||
| execution_return = [] | ||
| verify_quote = [] | ||
|
|
||
| private_send_contract_call = [] | ||
| private_off_chain_pre_check = [] | ||
| private_key_provision = [] | ||
| private_model_inference = [] | ||
| private_generate_quote = [] | ||
| private_execution_return = [] | ||
| private_verify_quote = [] | ||
|
|
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| files = ['alexnet', 'densenet161', 'resnet18', 'squeezenet1_0', 'vgg16'] | ||
| for file in files: | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 26).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 28).split('[')[3][:13]) | ||
| send_contract_call.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 28).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 29).split('[')[3][:13]) | ||
| off_chain_pre_check.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 29).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 30).split('[')[3][:13]) | ||
| model_inference.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 30).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 31).split('[')[3][:13]) | ||
| generate_quote.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 31).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 33).split('[')[3][:13]) | ||
| execution_return.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 46).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 47).split('[')[3][:13]) | ||
| verify_quote.append((ed-st)/1000) | ||
|
|
||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 35).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 37).split('[')[3][:13]) | ||
| private_send_contract_call.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 37).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 38).split('[')[3][:13]) | ||
| private_off_chain_pre_check.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 38).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 39).split('[')[3][:13]) | ||
| private_key_provision.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 40).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 41).split('[')[3][:13]) | ||
| private_model_inference.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 41).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 42).split('[')[3][:13]) | ||
| private_generate_quote.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 42).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 44).split('[')[3][:13]) | ||
| private_execution_return.append((ed-st)/1000) | ||
| st = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 47).split('[')[3][:13]) | ||
| ed = int(linecache.getline("../../eval_logs/%s.log.sorted" % file, 48).split('[')[3][:13]) | ||
| private_verify_quote.append((ed-st)/1000) | ||
|
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||
| width = 0.35 | ||
| send_contract_call = np.array(send_contract_call) | ||
| off_chain_pre_check = np.array(off_chain_pre_check) | ||
| model_inference = np.array(model_inference) | ||
| generate_quote = np.array(generate_quote) | ||
| execution_return = np.array(execution_return) | ||
| verify_quote = np.array(verify_quote) | ||
|
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||
| fig, ax = plt.subplots() | ||
| _pos = np.arange(len(labels)) | ||
| private_send_contract_call = np.array(send_contract_call) | ||
| private_off_chain_pre_check = np.array(off_chain_pre_check) | ||
| private_key_provision = np.array(private_key_provision) | ||
| private_model_inference = np.array(model_inference) | ||
| private_generate_quote = np.array(generate_quote) | ||
| private_execution_return = np.array(execution_return) | ||
| private_verify_quote = np.array(verify_quote) | ||
|
|
||
| ax.bar(_pos+width/2, private_send_contract_call, width, label='on-chain contract call (transfer to off-chain)', color='lightyellow', hatch='///', edgecolor='black') | ||
| ax.bar(_pos+width/2, private_off_chain_pre_check, width, bottom=private_send_contract_call, label='enclave initialization', color='lightgray', hatch='.', edgecolor='black') | ||
| ax.bar(_pos+width/2, private_key_provision, width, bottom=private_send_contract_call+private_off_chain_pre_check, label='secret key provisioning (private model only)', color='orange', hatch='\\', edgecolor='black') | ||
| ax.bar(_pos+width/2, private_model_inference, width, bottom=private_send_contract_call+private_off_chain_pre_check+private_key_provision, label='model inference', color='lightblue', hatch='x', edgecolor='black') | ||
| ax.bar(_pos+width/2, private_generate_quote, width, bottom=private_send_contract_call+private_off_chain_pre_check+private_key_provision+private_model_inference, label='quote generation', color='lightpink', hatch='o') | ||
| ax.bar(_pos+width/2, private_verify_quote, width, bottom=private_send_contract_call+private_off_chain_pre_check+private_key_provision+private_model_inference+private_generate_quote, label='quote verification', color='lightseagreen', hatch='+') | ||
| ax.bar(_pos+width/2, private_execution_return, width, bottom=private_send_contract_call+private_off_chain_pre_check+private_key_provision+private_model_inference+private_generate_quote+private_verify_quote, label='contract context recovery (back to on-chain)', color='red', hatch='*') | ||
|
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| ax.bar(_pos-width/2, send_contract_call, width, color='lightyellow', hatch='///', edgecolor='black') | ||
| ax.bar(_pos-width/2, off_chain_pre_check, width, bottom=send_contract_call, color='lightgray', hatch='.', edgecolor='black') | ||
| ax.bar(_pos-width/2, model_inference, width, bottom=off_chain_pre_check+send_contract_call, color='lightblue', hatch='x', edgecolor='black') | ||
| ax.bar(_pos-width/2, generate_quote, width, bottom=off_chain_pre_check+send_contract_call+model_inference, color='lightpink', hatch='o') | ||
| ax.bar(_pos-width/2, verify_quote, width, bottom=off_chain_pre_check+send_contract_call+model_inference+generate_quote, color='lightseagreen', hatch='+') | ||
| ax.bar(_pos-width/2, execution_return, width, bottom=off_chain_pre_check+send_contract_call+model_inference+generate_quote+verify_quote, color='red', hatch='*') | ||
|
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| # Add some text for labels, title and custom x-axis tick labels, etc. | ||
| ax.set_ylabel('time cost (seconds)') | ||
| x = np.arange(len(labels)) | ||
| ax.set_xticks(x,labels) | ||
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|
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| # ax.yaxis.grid(True) | ||
|
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| fig.tight_layout() | ||
| ax.legend(loc='best') | ||
|
|
||
| plt.show() |
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