greenai-tracker 0.1.1

Track carbon footprint of ML training jobs — 3 lines of code

greenai-tracker

Track carbon footprint of ML training jobs — 3 lines of code.

pip install greenai-tracker

Quick Start

import greenai

greenai.init(api_key="gai_live_xxx", project_id="proj_xxx")

with greenai.track("bert-finetuning"):
    model.fit(X_train, y_train)

That's it. GreenAI automatically:

• Measures real GPU power draw (via NVIDIA NVML if GPU present)
• Estimates CPU power from utilization × TDP
• Detects your cloud region (AWS/GCP/Azure)
• Computes CO₂ emissions using region-specific grid intensity
• Sends everything to your GreenAI dashboard

Install

# Basic install (CPU tracking)
pip install greenai-tracker

# With GPU support (reads real watts from NVIDIA GPUs)
pip install greenai-tracker[gpu]

# Full install (GPU + better CPU model detection)
pip install greenai-tracker[full]

Usage Patterns

Option 1: Init once, track many runs

import greenai

greenai.init(
    api_key="gai_live_xxx",
    project_id="proj_xxx",
    job_type="training",          # or "inference", "data_processing"
    region="aws:us-east-1",       # auto-detected if omitted
)

# Run 1
with greenai.track("training-epoch-1"):
    model.fit(X_train, y_train)

# Run 2
with greenai.track("training-epoch-2"):
    model.fit(X_train, y_train)

Option 2: Environment variables (no code change)

export GREENAI_API_KEY=gai_live_xxx
export GREENAI_PROJECT_ID=proj_xxx

import greenai

# Works without any arguments!
with greenai.track("my-training-run"):
    model.fit(X_train, y_train)

Option 3: Decorator

@greenai.track_function(api_key="gai_live_xxx", project_id="proj_xxx")
def train_model():
    model.fit(X_train, y_train)

train_model()  # Tracked automatically

Option 4: Validate your API key

import greenai
result = greenai.validate(api_key="gai_live_xxx")
print(result)
# {'valid': True, 'project': 'My ML Pipeline', 'plan': 'starter', 'job_runs_remaining': 9750}

Framework Integrations

PyTorch Lightning

from greenai_sdk.integrations.pytorch import GreenAICallback
import pytorch_lightning as pl

trainer = pl.Trainer(
    callbacks=[GreenAICallback(api_key="gai_live_xxx", project_id="proj_xxx")]
)
trainer.fit(model)

HuggingFace Transformers

from greenai_sdk.integrations.huggingface import GreenAICallback
from transformers import Trainer, TrainingArguments

trainer = Trainer(
    model=model,
    args=TrainingArguments(output_dir="./results"),
    callbacks=[GreenAICallback(api_key="gai_live_xxx", project_id="proj_xxx")]
)
trainer.train()

Scikit-learn

from greenai_sdk.integrations.sklearn import track_fit
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()
model = track_fit(model, api_key="gai_live_xxx", project_id="proj_xxx")
model.fit(X_train, y_train)  # Tracked automatically

How It Works

Measurement	Method
GPU power	nvidia-ml-py reads real watts from NVIDIA GPU
CPU power	psutil utilization × CPU TDP from model name
Region	Auto-detect from EC2/GCP/Azure metadata endpoints
CO₂	Watts × duration × regional grid intensity (kg CO₂e/kWh)

Supported regions: 80+ regions across AWS, GCP, and Azure including Mumbai (ap-south-1), Tokyo, Sydney, Frankfurt, and more.

Graceful fallback: Works without NVIDIA GPU (CPU-only estimate). Works offline (caches payloads and retries).

Requirements

• Python 3.8+
• requests, psutil (auto-installed)
• Optional: nvidia-ml-py for real GPU readings (pip install greenai-tracker[gpu])
• Optional: py-cpuinfo for better CPU model detection (pip install greenai-tracker[full])

Sign Up

Get your API key at greenai.dev — free tier includes 10,000 job runs/month.

Links

• Dashboard
• Documentation
• API Reference
• Issues

License

MIT