Tracarbon is a Python library that tracks your device's energy consumption and calculates your carbon emissions.
It detects your location and your device automatically before starting to export measurements to an exporter. It could be used as a CLI with already defined metrics or programmatically with the API by defining the metrics that you want to have.
Read more in this article.
pip install tracarbon
pip install 'tracarbon[datadog,prometheus,kubernetes]'
| Devices | Description |
|---|---|
| Mac | ✅ Apple Silicon CPU, GPU, memory and Neural Engine energy via IOReport (no sudo). Excludes display and peripherals. Fallbacks: powermetrics (sudo), thenioreg . |
| Linux | ✅ Supports Intel and AMD processors via RAPL . Intel uses the powercap interface. AMD is supported on kernel 5.8+ (powercap) or via theamd_energy driver (HWMON). Works with containers onKubernetes using theMetric API if available. |
| Windows | ✅ NVIDIA GPU power via nvidia-smi . CPU, memory and host totals are unavailable. |
| Cloud Provider | Description |
|---|---|
| AWS | ✅ Use the hardware's usage with the EC2 instances carbon emissions datasets of cloud-carbon-coefficients . |
| GCP | ✅ Use the hardware's usage with the GCP instances carbon emissions datasets of cloud-carbon-coefficients . |
| Azure | ✅ Use the hardware's usage with the Azure instances carbon emissions datasets of cloud-carbon-coefficients . |
| GPU | Description |
|---|---|
| NVIDIA | ✅ Supported via nvidia-smi . Works on Linux, Windows, and Intel Macs. Supports multiple GPUs. |
| AMD | ✅ Supported via rocm-smi oramd-smi on Linux. Supports multiple GPUs. |
| Apple Silicon | ✅ Integrated GPU power via IOReport, without sudo. Falls back to powermetrics (requires sudo). |
| Intel | ❌ Not yet implemented. |
| Exporter | Description |
|---|---|
| Stdout | Print the metrics in Stdout. |
| JSON | Write the metrics in a JSON file. |
| Prometheus | Send the metrics to Prometheus. |
| Datadog | Send the metrics to Datadog. |
| Location | Description | Source |
|---|---|---|
| Worldwide | Get the latest co2g/kwh in near real-time using the CO2Signal or ElectricityMaps APIs. See here for available Electricity Maps query modes. | CO2Signal API orElectricityMaps |
| Europe | Static file created from the European Environment Agency Emission for the co2g/kwh in European countries. | EEA website |
| AWS | Static file of the AWS Grid emissions factors. | cloud-carbon-coefficients |
| GCP | Static file of the GCP Grid emissions factors (2024 yearly data). | GoogleCloudPlatform/region-carbon-info |
| Azure | Static file of the Azure Grid emissions factors. | cloud-carbon-coefficients |
The environment variables can be set from an environment file .env.
| Parameter | Description |
|---|---|
| TRACARBON_CO2SIGNAL_API_KEY | The api key received from CO2Signal orElectricityMaps . |
| TRACARBON_CO2SIGNAL_URL | The url of CO2Signal is the default endpoint to retrieve the last known state of the zone, but it could be changed toElectricityMaps . |
| TRACARBON_METRIC_PREFIX_NAME | The prefix to use in all the metrics name. |
| TRACARBON_INTERVAL_IN_SECONDS | The interval in seconds to wait between the metrics evaluation. |
| TRACARBON_LOG_LEVEL | The level to use for displaying the logs. |
| TRACARBON_IPINFO_TOKEN | An optional ipinfo.io API token used for country detection from the IP address, lifting the anonymous rate limit. |
| TRACARBON_KUBERNETES_NODE_NAME | The Kubernetes node name used to scope container metrics to the node being measured. Falls back to NODE_NAME when unset. |
Request your API key
- Go to CO2Signal and get your free API key for non-commercial use, or go toElectricityMaps for commercial use.
- This API is used to retrieve the last known carbon intensity (in gCO2eq/kWh) of electricity consumed in your location.
- Set your API key in the environment variables, in the
.envfile or directly in the configuration. - If you would like to start without an API key, it's possible, the carbon intensity will be loaded statistically from a file.
- Launch Tracarbon 🚀
Command Line
tracarbon run
Prometheus with Kubernetes containers
tracarbon run --exporter-name Prometheus --containers
With the default metric prefix, container metrics are exposed with these Prometheus names:
| Metric | Labels |
|---|---|
| tracarbon_energy_consumption_kubernetes_total | pod_name, pod_namespace, container_name, platform, containers, location, units |
| tracarbon_energy_consumption_kubernetes_cpu | pod_name, pod_namespace, container_name, platform, containers, location, units |
| tracarbon_energy_consumption_kubernetes_memory | pod_name, pod_namespace, container_name, platform, containers, location, units |
| tracarbon_carbon_emission_kubernetes_total | pod_name, pod_namespace, container_name, platform, containers, location, source, units |
| tracarbon_carbon_emission_kubernetes_cpu | pod_name, pod_namespace, container_name, platform, containers, location, source, units |
| tracarbon_carbon_emission_kubernetes_memory | pod_name, pod_namespace, container_name, platform, containers, location, source, units |
Zero values are exported. If Kubernetes returns no pod metrics, the CLI logs No Kubernetes container metrics were collected. Host metrics are still exported.
When running in Kubernetes, deploy Tracarbon per node and set NODE_NAME from spec.nodeName with the Downward API so container metrics are scoped to the measured node.
API
from tracarbon import TracarbonBuilder, TracarbonConfiguration
configuration = TracarbonConfiguration() # Your configuration
tracarbon = TracarbonBuilder(configuration=configuration).build()
tracarbon.start()
total_co2g = tracarbon.stop() # The CO2 grams emitted while it was running
with tracarbon:
report = tracarbon.report # Get the report
print(report.total_co2g)
total_co2g is None when no host carbon emission metric was collected. The total reflects collected samples.
Local: using uv
make init
make test-unit
The documentation is hosted here: https://fvaleye.github.io/tracarbon/documentation
- 2025-11 Carbon Emission Quantification of Machine Learning: A Review
- 2025-04-25 A Critical Analysis of Machine Learning Eco-feedback Tools through the Lens of Sustainable HCI
- 2025-04-25 "Should I choose a smaller model?": Understanding ML Model Selection and Its Impact on Sustainability
- 2024-07-08 Balancing computational chemistry's potential with its environmental impact
- 2023-06-26 GREENER principles for environmentally sustainable computational science
- 2023-01-19 eco2AI: Carbon Emissions Tracking of Machine Learning Models as the First Step Towards Sustainable AI
source & further reading
github.com — original article
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