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lamya1baidouri
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commit
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1 changed files with 96 additions and 36 deletions
132
.github/workflows/pipeline.yml
vendored
132
.github/workflows/pipeline.yml
vendored
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@ -46,15 +46,11 @@ jobs:
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# Installation des packages nécessaires
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sudo apt-get update
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sudo apt-get install -y powerstat linux-tools-common linux-tools-generic python3-pip
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sudo apt-get install -y linux-tools-common linux-tools-generic python3-pip python3-psutil
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# Installation de PowerAPI et pandas
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pip3 install powerapi pandas
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# Vérifier les installations
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python3 --version
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pip3 list | grep powerapi
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pip3 list | grep pandas
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# Installation de PowerAPI et dépendances
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pip3 install powerapi==0.9.0 pandas numpy
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sudo powerapi --formula rapl
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- name: Cache Maven packages
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uses: actions/cache@v3
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@ -63,6 +59,13 @@ jobs:
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key: ${{ runner.os }}-m2-${{ hashFiles('**/pom.xml') }}
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restore-keys: ${{ runner.os }}-m2
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- name: Start PowerAPI monitoring
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id: start-powerapi
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run: |
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# Démarrer le daemon PowerAPI
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sudo powerapi daemon start --formula rapl
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echo "POWERAPI_PID=$(pgrep -f powerapi)" >> $GITHUB_ENV
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- name: Start monitoring
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id: start-monitoring
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run: |
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@ -98,6 +101,10 @@ jobs:
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start_time=$(date +%s%N)
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# Démarrer la mesure PowerAPI
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sudo powerapi monitor record --formula rapl --pid $$ --output metrics/power/build_power.csv &
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POWER_MONITOR_PID=$!
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# Collecter les métriques avant build
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free -m > metrics/system/pre_build_memory.txt
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@ -110,6 +117,9 @@ jobs:
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build_status=$?
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end_time=$(date +%s%N)
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# Arrêter la mesure PowerAPI
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kill $POWER_MONITOR_PID
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# Collecter les métriques post-build
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free -m > metrics/system/post_build_memory.txt
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@ -128,6 +138,10 @@ jobs:
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start_time=$(date +%s%N)
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# Démarrer la mesure PowerAPI
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sudo powerapi monitor record --formula rapl --pid $$ --output metrics/power/test_power.csv &
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POWER_MONITOR_PID=$!
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# Collecter les métriques pré-tests
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free -m > metrics/system/pre_test_memory.txt
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@ -137,6 +151,9 @@ jobs:
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test_status=$?
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end_time=$(date +%s%N)
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# Arrêter la mesure PowerAPI
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kill $POWER_MONITOR_PID
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# Collecter les métriques post-tests
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free -m > metrics/system/post_test_memory.txt
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@ -155,6 +172,10 @@ jobs:
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start_time=$(date +%s%N)
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# Démarrer la mesure PowerAPI
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sudo powerapi monitor record --formula rapl --pid $$ --output metrics/power/docker_power.csv &
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POWER_MONITOR_PID=$!
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# Collecter les métriques pré-docker
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free -m > metrics/system/pre_docker_memory.txt
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df -h > metrics/system/pre_docker_disk.txt
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@ -165,6 +186,9 @@ jobs:
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build_status=$?
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end_time=$(date +%s%N)
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# Arrêter la mesure PowerAPI
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kill $POWER_MONITOR_PID
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# Collecter les métriques post-docker
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free -m > metrics/system/post_docker_memory.txt
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df -h > metrics/system/post_docker_disk.txt
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@ -185,53 +209,84 @@ jobs:
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# Collecter les métriques système finales
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echo "=== Final System Resources ===" > metrics/system/final_metrics.txt
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top -b -n 1 >> metrics/system/final_metrics.txt || echo "Failed to collect top metrics"
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top -b -n 1 >> metrics/system/final_metrics.txt
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echo "=== Final Memory Usage ===" > metrics/system/final_memory.txt
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free -m >> metrics/system/final_memory.txt || echo "Failed to collect memory metrics"
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free -m >> metrics/system/final_memory.txt
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echo "=== Final Disk Usage ===" > metrics/system/final_disk.txt
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df -h >> metrics/system/final_disk.txt || echo "Failed to collect disk metrics"
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df -h >> metrics/system/final_disk.txt
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# Marquer la fin du pipeline
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date +%s%N > metrics/pipeline_end_time.txt
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# Analyser les temps d'exécution
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python3 << EOF
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import pandas as pd
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import glob
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import os
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def analyze_power_metrics():
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power_files = glob.glob('metrics/power/*.csv')
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if not power_files:
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print("No power metrics found")
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return
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def read_time_file(filename):
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try:
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with open(filename, 'r') as f:
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return float(f.read().strip())
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except:
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return 0
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power_data = []
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for file in power_files:
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stage = os.path.basename(file).replace('_power.csv', '')
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try:
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df = pd.read_csv(file)
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stats = {
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'stage': stage,
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'avg_power': df['power'].mean(),
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'max_power': df['power'].max(),
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'total_energy': df['power'].sum() * df['power'].count() * 0.1,
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'duration': len(df) * 0.1
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}
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power_data.append(stats)
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except Exception as e:
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print(f"Error processing {file}: {e}")
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# Collecter les temps
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times = {
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'build': read_time_file('metrics/performance/build_time.txt'),
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'test': read_time_file('metrics/performance/test_time.txt'),
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'docker': read_time_file('metrics/performance/docker_time.txt')
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}
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if power_data:
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power_df = pd.DataFrame(power_data)
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power_df.to_csv('metrics/power/power_analysis.csv', index=False)
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# Créer le rapport de performance
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with open('metrics/performance/summary.txt', 'w') as f:
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f.write("Pipeline Performance Summary\n")
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f.write("==========================\n\n")
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with open('metrics/power/power_summary.txt', 'w') as f:
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f.write("Energy Consumption Summary\n")
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f.write("=========================\n\n")
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for _, row in power_df.iterrows():
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f.write(f"Stage: {row['stage']}\n")
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f.write(f"Average Power: {row['avg_power']:.2f} W\n")
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f.write(f"Maximum Power: {row['max_power']:.2f} W\n")
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f.write(f"Total Energy: {row['total_energy']:.2f} J\n")
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f.write(f"Duration: {row['duration']:.2f} s\n\n")
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def analyze_times():
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times = {
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'build': float(open('metrics/performance/build_time.txt').read().strip()),
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'test': float(open('metrics/performance/test_time.txt').read().strip()),
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'docker': float(open('metrics/performance/docker_time.txt').read().strip())
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}
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total_time = sum(times.values())
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for stage, duration in times.items():
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percentage = (duration / total_time * 100) if total_time > 0 else 0
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f.write(f"{stage.capitalize()} Stage:\n")
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f.write(f"Duration: {duration/1000:.2f} seconds\n")
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f.write(f"Percentage of total time: {percentage:.1f}%\n\n")
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with open('metrics/performance/summary.txt', 'w') as f:
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f.write("Pipeline Performance Summary\n")
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f.write("==========================\n\n")
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f.write(f"Total Pipeline Duration: {total_time/1000:.2f} seconds\n")
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for stage, duration in times.items():
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percentage = (duration / total_time * 100)
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f.write(f"{stage.capitalize()} Stage:\n")
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f.write(f"Duration: {duration/1000:.2f} seconds\n")
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f.write(f"Percentage of total time: {percentage:.1f}%\n\n")
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# Créer un CSV avec les métriques
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pd.DataFrame([times]).to_csv('metrics/performance/times.csv', index=False)
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f.write(f"Total Pipeline Duration: {total_time/1000:.2f} seconds\n")
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pd.DataFrame([times]).to_csv('metrics/performance/times.csv', index=False)
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analyze_power_metrics()
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analyze_times()
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EOF
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- name: Export metrics to Prometheus
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@ -274,5 +329,10 @@ jobs:
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- name: Cleanup
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if: always()
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run: |
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# Arrêter PowerAPI
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if [ -n "$POWERAPI_PID" ]; then
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sudo powerapi daemon stop
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fi
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docker system prune -af
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rm -rf node_exporter*
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