ecmhongz 0.1.2

ecmhong is a Python package for Energy Consumption Monitoring.

Project Name

Energy Consumption Monitoring Tool

Project Overview

Background: In the current AI research field (especially in scenarios like large model training, hyperparameter search, etc.), there is a prevalent "performance-first" mindset. Researchers often treat hardware resources as virtually unlimited computing units, lacking systematic attention to the energy consumption characteristics of GPUs/CPUs (e.g., power fluctuations, thermal dissipation efficiency). As AI tasks experience exponential growth in computational demands, the energy efficiency of hardware resources has become a critical factor influencing research costs and environmental sustainability. Therefore, effectively and lightweightly monitoring energy consumption-related metrics has become extremely important. Currently, energy consumption data collection mainly relies on researchers manually executing command-line tools like nvidia-smi and perf, which results in fragmented data recording and task execution, adding to the debugging and optimization workload of researchers.

Objective: To develop an automated, lightweight, cross-platform monitoring tool that implements the following core functionalities:

• Real-time collection: Capture CPU/GPU energy consumption and performance metrics at configurable time intervals
• Multi-device support: Compatible with multi-GPU server environments
• Data persistence: Provide CSV and MySQL storage options to accommodate different scales of data management needs
• Visualization and analysis: Generate interactive charts to visually display the spatiotemporal relationship between hardware resource utilization and energy consumption

Results: A highly available energy monitoring system with the following technical specifications:

• Supports 0.1-second data collection precision (The level of granularity depends on the number of GPUs and their performance)
• Compatible with the entire range of NVIDIA GPUs (based on nvidia-smi standardized output parsing)
• Optimized MySQL batch writing (transaction commit frequency adjustable, with a daily average record processing capacity of tens of millions per table)
• Generates interactive HTML visualization reports (based on Plotly dynamic charts, supporting multi-dimensional data comparison)

Operations: The user can submit tasks and monitor CPU and GPU data in real time by running the script, and the data will be saved. The user can also utilize the saved data to generate charts for visualization and analysis.

Overall Implementation

Flowchart

1. Monitoring Data Collection Module

GPU Metrics Collection: Use subprocess to call the nvidia-smi command-line tool and parse the following key parameters:

# Core monitoring metrics
GPU_QUERY_FIELDS = [
    "task_name", "cpu_usage", "cpu_power_draw", "dram_usage", "dram_power_draw", "gpu_name",
    "gpu_index", "gpu_power_draw", "utilization_gpu", "utilization_memory", "pcie_link_gen_current",
    "pcie_link_width_current", "temperature_gpu", "temperature_memory", "sm", "clocks_gr", "clocks_mem"]

CPU & DRAM Metrics Collection: Use the psutil library and RAPL interface to implement multi-core utilization statistics:

def get_cpu_info():
    return psutil.cpu_percent(interval=0.05, percpu=False)  # Global average utilization

def get_cpu_power_info(sample_interval=0.05):
    try:
        powercap_path = "/sys/class/powercap" # RAPL interface
        ......

2. Data Storage Module

CSV Storage Solution: Append-write mode is used, and file naming convention: {task_name}_{timestamp}.csv

# Data fields and MySQL table structure strictly aligned
CSV_COLUMNS = [
    'timestamp', 'task_name', 'cpu_usage', 'gpu_name', 'gpu_index',
    'power_draw', 'utilization_gpu', 'utilization_memory', ...
]

MySQL Storage Solution: Dynamic table creation mechanism (automatically create tables based on task name + timestamp), supporting transaction processing with the InnoDB engine:

CREATE TABLE IF NOT EXISTS {table_name} (
    id INT AUTO_INCREMENT PRIMARY KEY COMMENT 'Auto-incremented ID',
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT 'Capture time',
    task_name VARCHAR(50) COMMENT 'Task name',
    gpu_index INT COMMENT 'GPU device index',
    sm FLOAT COMMENT 'SM utilization rate (%)',
    ...
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

3. Visualization Module

Dynamic chart generation based on the Plotly library, creating interactive line charts with multiple dimensions:

fig.add_trace(go.Scatter(
    x=df['timestamp'], 
    y=df['power_draw'],
    mode='lines',
    name='GPU Power (W)',
    hovertemplate="<b>%{x}</b><br>Power: %{y}W"
))

Project Usage Instructions

For detailed project usage instructions, please refer to [UserManual.md].