{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/4"}],"enrichment":{"capability":"Measures and tracks carbon emissions from local computing hardware (CPU, GPU, RAM) and provides estimates of their environmental impact based on regional electricity carbon intensity.","skillfed_tags":["sustainability","environmental-impact","hardware-monitoring"],"use_cases":["Measure carbon emissions from training machine learning models to compare efficiency across different architectures or hyperparameters","Track total CO\u2082 impact of long-running data processing or scientific computing jobs to report environmental metrics","Monitor GPU utilization and emissions during development to identify and optimize energy-intensive code paths","Establish baseline carbon footprint for a research project and track improvements over time","Generate emissions reports for sustainability reporting or academic papers on green computing"],"what_it_does":"CodeCarbon is a Python library that estimates the carbon emissions produced by your local computing hardware. It measures or estimates power consumption from CPU, GPU, and RAM, then applies the regional carbon intensity of your electricity grid to calculate CO\u2082 emissions in kilograms. The library offers both a programmatic API (via EmissionsTracker) and a command-line interface for tracking emissions without modifying code.\n\nIt's designed for developers and researchers who want to understand and reduce the environmental footprint of their computational work\u2014particularly useful for machine learning experiments, data processing, and other CPU- or GPU-intensive tasks. The package integrates with a web dashboard for visualization and provides configuration through files, environment variables, or Python arguments.","worth_installing":"Yes, if you need to measure and track carbon emissions from local computing. The library is actively maintained, has no known vulnerabilities, installs easily, and uses a permissive MIT license. It fills a specific niche\u2014local hardware emissions tracking\u2014and is complementary to tools that track remote API calls. Install it if environmental impact of your code matters to your workflow or reporting requirements."},"id":"codecarbon","links":{"html":"https://skillfed.io/packages/codecarbon","md":"https://skillfed.io/packages/codecarbon.md","pypi":"https://pypi.org/project/codecarbon/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":"MIT","license_treatment":"permissive","name":"codecarbon","python_support":"supports_current","summary":null},"popularity":{"monthly_downloads":204797,"position":9600,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.3.0"}