---
license: agpl-3.0
tags:
- smoltrace
- smolagents
- evaluation
- benchmark
- llm
- agents
---
---
# SMOLTRACE GPU & Environmental Metrics
This dataset contains time-series GPU metrics and environmental impact data from a SMOLTRACE benchmark run.
## Dataset Information
| Field | Value |
|-------|-------|
| **Model** | `ministral-3:3b` |
| **Run ID** | `48252774-d862-4c4e-8a90-54dc5fd3df2c` |
| **Total Samples** | 313 |
| **Generated** | 2025-12-10 13:55:15 UTC |
| **GPU Metrics** | Available |
## Schema
| Column | Type | Description |
|--------|------|-------------|
| `run_id` | string | Unique run identifier |
| `timestamp` | string | ISO timestamp of measurement |
| `timestamp_unix_nano` | string | Unix nanosecond timestamp |
| `service_name` | string | Service identifier |
| `gpu_id` | string | GPU device ID |
| `gpu_name` | string | GPU model name |
| `gpu_utilization_percent` | float | GPU compute utilization (0-100%) |
| `gpu_memory_used_mib` | float | GPU memory used (MiB) |
| `gpu_memory_total_mib` | float | Total GPU memory (MiB) |
| `gpu_temperature_celsius` | float | GPU temperature (°C) |
| `gpu_power_watts` | float | GPU power consumption (W) |
| `co2_emissions_gco2e` | float | Cumulative CO2 emissions (gCO2e) |
| `power_cost_usd` | float | Cumulative power cost (USD) |
## Environmental Impact
SMOLTRACE tracks environmental metrics to help you understand the carbon footprint of your AI workloads:
- **CO2 Emissions**: Calculated based on GPU power consumption and regional carbon intensity
- **Power Cost**: Estimated electricity cost based on configurable rates
## Usage
```python
from datasets import load_dataset
import pandas as pd
# Load metrics
ds = load_dataset("YOUR_USERNAME/smoltrace-metrics-TIMESTAMP")
# Convert to DataFrame for analysis
df = pd.DataFrame(ds['train'])
# Plot GPU utilization over time
import matplotlib.pyplot as plt
plt.plot(df['timestamp'], df['gpu_utilization_percent'])
plt.xlabel('Time')
plt.ylabel('GPU Utilization (%)')
plt.title('GPU Utilization During Evaluation')
plt.show()
# Get total environmental impact
total_co2 = df['co2_emissions_gco2e'].max()
total_cost = df['power_cost_usd'].max()
print(f"Total CO2: {total_co2:.4f} gCO2e")
print(f"Total Cost: ${total_cost:.6f}")
```
## Related Datasets
This evaluation run also generated:
- **Results Dataset**: Pass/fail outcomes for each test case
- **Traces Dataset**: Detailed OpenTelemetry execution traces
- **Leaderboard**: Aggregated metrics for model comparison
---
## About SMOLTRACE
**SMOLTRACE** is a comprehensive benchmarking and evaluation framework for [Smolagents](https://huggingface.co/docs/smolagents) - HuggingFace's lightweight agent library.
### Key Features
- Automated agent evaluation with customizable test cases
- OpenTelemetry-based tracing for detailed execution insights
- GPU metrics collection (utilization, memory, temperature, power)
- CO2 emissions and power cost tracking
- Leaderboard aggregation and comparison
### Quick Links
- [GitHub Repository](https://github.com/Mandark-droid/SMOLTRACE)
- [PyPI Package](https://pypi.org/project/smoltrace/)
- [Documentation](https://github.com/Mandark-droid/SMOLTRACE#readme)
- [Report Issues](https://github.com/Mandark-droid/SMOLTRACE/issues)
### Installation
```bash
pip install smoltrace
```
### Citation
If you use SMOLTRACE in your research, please cite:
```bibtex
@software{smoltrace,
title = {SMOLTRACE: Benchmarking Framework for Smolagents},
author = {Thakkar, Kshitij},
url = {https://github.com/Mandark-droid/SMOLTRACE},
year = {2025}
}
```
---
