metadata
license: agpl-3.0
tags:
- smoltrace
- smolagents
- evaluation
- benchmark
- llm
- agents
Tiny Agents. Total Visibility.
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
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 - 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
Installation
pip install smoltrace
Citation
If you use SMOLTRACE in your research, please cite:
@software{smoltrace,
title = {SMOLTRACE: Benchmarking Framework for Smolagents},
author = {Thakkar, Kshitij},
url = {https://github.com/Mandark-droid/SMOLTRACE},
year = {2025}
}
Generated by SMOLTRACE