TrackingEnergy 0.1.2
EnergyTracker is a library for monitoring system resource usage and energy consumption. CPU, RAM, Disk,GPU and Co2
License: MIT
Requires Python: >=3.6

# EnergyTracker User Manual

## Table of Contents
1. [Introduction](#introduction)
2. [Installation](#installation)
3. [Core Concepts](#core-concepts)
4. [Basic Usage](#basic-usage)
5. [Advanced Features](#advanced-features)
   - [Process Tracking](#process-tracking)
   - [Model Layer Tracking](#model-layer-tracking)
   - [Continuous Monitoring](#continuous-monitoring)
   - [Markers](#markers)
6. [Data Export & Analysis](#data-export--analysis)
7. [Visualization](#visualization)
8. [Practical Examples](#practical-examples)
9. [API Reference](#api-reference)
10. [Troubleshooting](#troubleshooting)

## Introduction

EnergyTracker is a Python toolkit designed to monitor and analyze energy consumption, power usage, and CO2 emissions of software systems. It enables developers, researchers, and data scientists to:

- Measure the environmental impact of machine learning models
- Compare energy efficiency of different algorithms
- Identify power-intensive code sections
- Estimate carbon footprint of applications

The toolkit provides both real-time monitoring capabilities and tools for post-processing and visualizing energy usage data.

## Installation

### Requirements
- Python 3.6+
- psutil (core dependency)

### Optional Dependencies
- pandas (for data analysis)
- matplotlib (for visualization)
- tabulate (for pretty tables)
- pynvml (for NVIDIA GPU monitoring)

### Installation Commands

```bash
# Basic installation
pip install energy-tracker

# Full installation with all dependencies
pip install energy-tracker[all]

# Or install with specific feature sets
pip install energy-tracker[viz]  # Core + visualization tools
pip install energy-tracker[gpu]  # Core + GPU monitoring
```

## Core Concepts

### Layers

EnergyTracker monitors resources at different "layers":

- **System Layer**: Overall system metrics (CPU, RAM, disk, etc.)
- **Process Layer**: Metrics for specific processes
- **Model Layer**: Custom-defined layers for ML model components

### Metrics

The toolkit tracks several metrics:

- CPU usage (percentage)
- Memory usage (MB and percentage)
- GPU utilization and memory (if available)
- Estimated power consumption (Watts)
- CO2 emissions (grams)
- Battery status (for portable devices)

### Snapshots

Resource measurements are stored as "snapshots" - point-in-time records of all metrics. These can be:
- Manually triggered
- Automatically captured at intervals
- Associated with markers for reference

## Basic Usage

### Quick Monitoring

For simple tracking, use the `quick_track` utility function:

```python
from energy_tracker import quick_track

# Monitor system for 10 seconds
tracker = quick_track(duration=10)

# Print summary
print(tracker.get_summary())
```

### Tracking a Function

To measure energy used by a specific function:

```python
from energy_tracker import track_function

def my_algorithm():
    # Your code here
    pass

# Run function with energy tracking
result, tracker = track_function(my_algorithm)

# Print summary
print(tracker.get_summary())
```

### Manual Tracking Control

For more control over the tracking process:

```python
from energy_tracker import EnergyTracker

# Create a tracker
tracker = EnergyTracker(name="my_tracking_session")

# Start tracking
tracker.start()

# Do work here
heavy_computation()

# Stop tracking
tracker.stop()

# Get results
print(tracker.get_summary())
```

## Advanced Features

### Process Tracking

To monitor specific processes:

```python
from energy_tracker import EnergyTracker

# Create tracker with process layer enabled
tracker = EnergyTracker(
    name="process_tracking",
    track_layers=["system", "process"]
)

# Track processes
tracker.track_process(pid=1234)  # By process ID
tracker.track_process(process_name="chrome")  # By name (substring match)

# Start tracking
tracker.start()

# Later...
tracker.stop()
print(tracker.get_summary())
```

### Model Layer Tracking

For machine learning workflows, track different model components:

```python
from energy_tracker import EnergyTracker

# Create tracker with model layer enabled
tracker = EnergyTracker(
    name="ml_tracking", 
    track_layers=["system", "model"]
)

tracker.start()

# Track data preparation
tracker.start_model_layer("my_model", "data_preparation")
prepare_data()
tracker.stop_model_layer()

# Track training
tracker.start_model_layer("my_model", "training")
train_model()
tracker.stop_model_layer()

# Track inference
tracker.start_model_layer("my_model", "inference")
run_inference()
tracker.stop_model_layer()

tracker.stop()

# Get breakdown by model component
print(tracker.get_summary())
```

### Continuous Monitoring

For long-running processes, use background monitoring:

```python
from energy_tracker import EnergyTracker

# Configure sampling interval
tracker = EnergyTracker(
    name="background_tracking",
    sample_interval=0.5  # 0.5 seconds between samples
)

# Start tracking in background
tracker.start()
tracker.start_continuous()

# Continue with your code
# ... (tracking happens in background)

# Stop when done
tracker.stop_continuous()
tracker.stop()
```

### Markers

Use markers to identify important points:

```python
from energy_tracker import EnergyTracker

tracker = EnergyTracker(name="marked_tracking")
tracker.start()

# Add markers at significant points
tracker.mark("algorithm_start")
run_part1()
tracker.mark("part1_complete")
run_part2()
tracker.mark("part2_complete")

tracker.stop()

# Summary will include marker data
print(tracker.get_summary())
```

## Data Export & Analysis

### Exporting Data

Save tracking data for later analysis:

```python
# Save raw data as CSV
tracker.save_to_csv("energy_data.csv")

# Save summary as JSON
tracker.save_summary_json("energy_summary.json")
```

### Getting Data in Different Formats

```python
# As text report
text_summary = tracker.get_summary(output_format="text")
print(text_summary)

# As dictionary
dict_summary = tracker.get_summary(output_format="dict")
print(dict_summary["avg_power_watts"])

# As JSON string
json_summary = tracker.get_summary(output_format="json")
```

### Tabular Data

```python
# Get data table (list of dictionaries)
data_table = tracker.get_data_table()

# Get formatted table
summary_table = tracker.get_summary_table(format="markdown")
print(summary_table)
```

## Visualization

EnergyTracker includes a visualization module for analyzing results.

### Command Line Usage

```bash
# Visualize from exported files
python -m energy_tracker.visualize --csv energy_data.csv --json energy_summary.json

# Generate specific plot types
python -m energy_tracker.visualize --json energy_summary.json --plot-type pie
python -m energy_tracker.visualize --csv energy_data.csv --plot-type time --metrics "CPU (%)" "Power (W)"

# Generate all plot types
python -m energy_tracker.visualize --csv energy_data.csv --json energy_summary.json --plot-type all
```

### Programmatic Visualization

```python
from energy_tracker.visualize import (
    load_data, load_summary, 
    plot_time_series, plot_model_layers, plot_energy_summary
)

# Load data
df = load_data("energy_data.csv")
summary = load_summary("energy_summary.json")

# Create time series plot
plot_time_series(df, metrics=["CPU (%)", "Power (W)"], output_file="resources.png")

# Create model layer comparison
plot_model_layers(summary, output_file="models.png")

# Create comprehensive summary
plot_energy_summary(summary, output_file="energy_report.png")
```

## Practical Examples

### Compare Sorting Algorithms

```python
from energy_tracker import EnergyTracker

def bubble_sort(arr):
    # Implementation...
    pass

def quick_sort(arr):
    # Implementation...
    pass

def merge_sort(arr):
    # Implementation...
    pass

# Create test data
data = list(range(10000))
random.shuffle(data)

# Compare algorithms
algorithms = {
    "bubble_sort": bubble_sort,
    "quick_sort": quick_sort,
    "merge_sort": merge_sort
}

results = {}
for name, algorithm in algorithms.items():
    print(f"Testing {name}...")
    tracker = EnergyTracker(name=f"sort_{name}")
    tracker.start()
    
    # Run the algorithm
    algorithm(data.copy())
    
    tracker.stop()
    results[name] = tracker.get_summary_dict()
    print(f"  Power: {results[name]['avg_power_watts']:.2f}W")
    print(f"  CO2: {results[name]['total_co2_g']:.6f}g")
```

### Track ML Pipeline

```python
from energy_tracker import EnergyTracker

# Initialize tracker
tracker = EnergyTracker(
    name="ml_pipeline", 
    track_layers=["system", "model"]
)

tracker.start()

# Track data processing
tracker.start_model_layer("ml_model", "data_processing")
X, y = load_and_process_data()
tracker.stop_model_layer()

# Track model training
tracker.start_model_layer("ml_model", "training")
model = train_model(X, y)
tracker.stop_model_layer()

# Track multiple evaluation runs
for i, test_set in enumerate(test_sets):
    tracker.start_model_layer("ml_model", f"eval_{i}")
    evaluate_model(model, test_set)
    tracker.stop_model_layer()

tracker.stop()

# Generate HTML report
with open("ml_energy_report.html", "w") as f:
    f.write(f"<h1>ML Pipeline Energy Report</h1>")
    f.write(f"<pre>{tracker.get_summary()}</pre>")
    f.write(f"<table>{tracker.get_summary_table(format='html')}</table>")
```

## API Reference

### EnergyTracker Class

```python
EnergyTracker(
    name="default",                # Name for this tracking session
    track_layers=None,             # List of layers to track 
                                   # (options: 'system', 'process', 'model')
    track_battery=True,            # Whether to track battery on portable devices
    co2_per_kwh=475,               # CO2 emissions in grams per kWh
    sample_interval=1.0,           # Seconds between samples when continuous tracking
    gpu_enabled=True,              # Whether to enable GPU monitoring
    log_level=logging.INFO         # Logging level
)
```

#### Main Methods

| Method | Description |
|--------|-------------|
| `start(label=None)` | Start tracking with optional label |
| `stop()` | Stop tracking and take final snapshot |
| `mark(marker_name)` | Create a named marker at current point |
| `snapshot(label=None)` | Take a manual snapshot with optional label |
| `track_process(pid=None, process_name=None)` | Add process to track by PID or name |
| `start_model_layer(model_name, layer_name)` | Start tracking a model layer |
| `stop_model_layer(model_name=None, layer_name=None)` | Stop tracking a model layer |
| `start_continuous(label=None)` | Start continuous tracking in background |
| `stop_continuous()` | Stop continuous tracking |
| `get_summary(output_format="text")` | Get tracking summary |
| `get_summary_dict()` | Get summary as dictionary |
| `get_data_table()` | Get raw data table |
| `get_summary_table(format="markdown")` | Get formatted summary table |
| `save_to_csv(filename=None)` | Save data to CSV file |
| `save_summary_json(filename=None)` | Save summary to JSON file |
| `plot(metrics=None, output_file=None)` | Generate plots (requires matplotlib) |

### Utility Functions

```python
# Quick tracking for a fixed duration
quick_track(
    duration=10,           # Duration in seconds
    name="quick_track",    # Tracking session name
    interval=1.0,          # Sample interval
    track_battery=True     # Whether to track battery
)

# Track a specific function call
track_function(
    func,           # Function to track
    *args,          # Arguments to pass to function
    name=None,      # Optional name (defaults to function name)
    **kwargs        # Keyword arguments to pass to function
)
```

## Troubleshooting

### Common Issues

#### No GPU Data Available

- Ensure you have pynvml installed (`pip install pynvml`)
- Verify NVIDIA GPU is detected by your system
- Check that you have proper permissions

#### High Overhead for Continuous Tracking

- Increase `sample_interval` to reduce measurement frequency
- Minimize tracked layers to only those needed

#### Missing Dependencies

If you see errors about missing dependencies:

```bash
# For visualization support
pip install matplotlib pandas tabulate

# For GPU monitoring
pip install pynvml

# For all optional dependencies
pip install energy-tracker[all]
```

#### Inaccurate Power Measurements

Power measurements are estimations based on resource usage. For more accurate measurements:

- Calibrate against a hardware power meter if available
- Adjust the power model parameters for your specific hardware
- Consider external power monitoring tools for validation

### Debugging

Enable detailed logging for troubleshooting:

```python
import logging
tracker = EnergyTracker(log_level=logging.DEBUG)
```

### Support

If you encounter issues:

1. Check the troubleshooting section of the documentation
2. Look for similar issues in the GitHub repository
3. Submit a detailed bug report with environment information

## License

EnergyTracker is released under the MIT License.