ecobench 1.0.1

A Python library for tracking LLM usage, costs, and environmental impact

Ecobench

A comprehensive Python library for tracking LLM usage, costs, and environmental impact. Monitor your AI model consumption with detailed analytics and environmental metrics.

Features

• Easy Integration: Simple API for tracking LLM usage in your applications
• Cost Tracking: Monitor API costs with support for input, output, and cached tokens
• Environmental Impact: Track energy consumption and water usage
• Multiple Models: Built-in support for popular models (GPT-4o, GPT-4o-mini, o3-mini, Mixtral-8x22B)
• Custom Models: Support for custom model configurations
• Chain of Thought: Support for CoT reasoning with token multipliers

Installation

From PyPI (Recommended)

pip install ecobench

From Source

git clone https://github.com/ecobench/ecobench.git
cd ecobench
pip install -e .

Development Installation

git clone https://github.com/ecobench/ecobench.git
cd ecobench
pip install -e ".[dev]"

Quick Start

from ecobench import Tracker

# Initialize tracker
tracker = Tracker("GPT-4o")

# Track usage
tracker.update_state(input_tokens=100, output_tokens=50, cached_tokens=20)

# Get summary
tracker.print_summary()

Usage Examples

Basic Usage

from ecobench import Tracker

# Initialize tracker with GPT-4o
tracker = Tracker("GPT-4o")

# Track an API call
result = tracker.update_state(
    input_tokens=100,
    output_tokens=50,
    cached_tokens=20
)

print(f"Cost: ${result['cost']:.4f}")
print(f"Energy: {result['energy_wh']:.4f} Wh")

Running Examples

The library includes several example scripts:

# Simple model comparison
python examples/simple_comparison.py

# Comprehensive visualization
python examples/visualize_model_comparison.py

# Notebook integration
python examples/notebook_integration.py

Integration with Chat Applications

class ChatApp:
    def __init__(self):
        self.tracker = Tracker("GPT-4o")
    
    def make_api_call(self, prompt, response, cached_tokens=0):
        # Estimate tokens (in practice, get from API response)
        input_tokens = len(prompt.split())
        output_tokens = len(response.split())
        
        # Track usage
        result = self.tracker.update_state(
            input_tokens=input_tokens,
            output_tokens=output_tokens,
            cached_tokens=cached_tokens
        )
        
        return response, result

Model Comparison

# Compare different models
tracker_gpt4o = Tracker("GPT-4o")
tracker_mini = Tracker("GPT-4o-mini")

result_gpt4o = tracker_gpt4o.update_state(input_tokens=1000, output_tokens=500)
result_mini = tracker_mini.update_state(input_tokens=1000, output_tokens=500)

print(f"GPT-4o cost: ${result_gpt4o['cost']:.4f}")
print(f"GPT-4o-mini cost: ${result_mini['cost']:.4f}")

Custom Models

from ecobench import Model, Tracker

# Create custom model
custom_model = Model(
    name="Custom Model",
    d_model=4096,
    d_ff=11008,
    layers=32,
    num_query_heads=32,
    cost_per_input_token=0.001/1000,
    cost_per_output_token=0.002/1000,
    cost_per_cache_token=0.0005/1000
)

# Use with tracker
tracker = Tracker(custom_model=custom_model)

Available Models

• GPT-4o: OpenAI's flagship model
• GPT-4o-mini: Smaller, faster version of GPT-4o
• o3-mini: OpenAI's reasoning model with CoT support
• Mixtral-8x22B: Open-source MoE model

API Reference

Tracker

__init__(model_name: str = "GPT-4o", custom_model: Model = None)

Initialize tracker with a model.

update_state(input_tokens: int, output_tokens: int, cached_tokens: int = 0, use_cot_reasoning: bool = False, cache_percent: float = None) -> dict

Update tracker with new usage data.

get_summary() -> dict

Get summary of all tracked usage.

print_summary()

Print formatted summary to console.

reset()

Reset all tracking data.

Environmental Impact

The library tracks:

• Energy Consumption: In Watt-hours (Wh)
• Water Usage: In liters
• Carbon Footprint: Through energy calculations

Development Setup

1. Fork the repository
2. Clone your fork: git clone https://github.com/yourusername/ecobench.git
3. Install in development mode: pip install -e ".[dev]"
4. Run tests: pytest
5. Run linting: flake8 src/ tests/
6. Format code: black src/ tests/

License

This project is licensed under the MIT License - see the LICENSE file for details.

## Support

- 📖 [Documentation](https://github.com/KnutOplandMoen/ecobench#readme)
- 🐛 [Bug Reports](https://github.com/KnutOplandMoen/ecobench/issues)
- 💬 [Discussions](https://github.com/KnutOplandMoen/ecobench/discussions)
- 📧 Email: [e-mail]