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CodeCarbon and the alternatives

Several tools measure or estimate the energy and carbon footprint of computing. They overlap, but they were built for different jobs, and for some of the questions below another tool is the better answer. This page tries to say so plainly.

For the CodeCarbon versus EcoLogits question specifically — local hardware versus remote GenAI APIs — see When to use CodeCarbon. The two are complementary rather than competing.

The tools compared here are ML CO2 Impact, carbontracker, eco2AI, experiment-impact-tracker, Zeus, Scaphandre and the cloud providers' own consoles.

Which one should you use?

"I want to know the cost before I run it"

Use ML CO2 Impact or carbontracker. This is not what CodeCarbon does.

ML CO2 Impact, created by the same team before CodeCarbon, is a web form: you pick a GPU, a region and a number of hours, and it gives you an estimate plus a LaTeX snippet for your paper. Nothing to install, nothing to instrument.

carbontracker measures the first epochs of a training run and extrapolates to the full schedule, which lets you decide whether to continue before you have spent the compute. If "should I run this at all" is your question, that is the right shape of tool.

CodeCarbon reports what a run actually consumed, during and after the fact.

"I want to attribute emissions to a specific function or training phase"

Use CodeCarbon. tracker.start_task("name") and tracker.stop_task() split a single process into separately reported segments, so you can compare preprocessing against training against inference inside one script. See examples/task_inference.py and examples/task_loop_same_task.py.

"I want to reduce energy use, not just report it"

Look at Zeus first. It comes from a systems-research angle and is built around finding a good point on the energy/time trade-off curve — batch size, power limit, GPU frequency — rather than around producing a footprint report. CodeCarbon will tell you what a configuration costs; it will not search for a better one.

"I want to monitor a fleet, not a script"

Scaphandre is likely the better fit. It is an agent, not a library: it meters whole hosts and containers without any change to the applications running on them, and it exports to Prometheus natively. If your goal is a datacenter-or-cluster dashboard and you are not instrumenting individual jobs, start there.

CodeCarbon can do fleet work, but it approaches it from the other direction — it starts inside the process and scales outward. If you want per-job attribution and fleet aggregation, it has:

"I'm on SLURM or an HPC cluster"

CodeCarbon has a dedicated SLURM guide covering multi-node runs, plus ROCm and PyTorch examples under examples/slurm_rocm/. Most of the alternatives leave the multi-node aggregation problem to you.

"I need the total for my AWS or GCP account"

Use the provider's own tool: the AWS Customer Carbon Footprint Tool, Google Cloud Carbon Footprint, or the Azure Emissions Impact Dashboard. They have access to data no external library can see — actual facility PUE, the provider's own energy contracts — and they cover your whole account, including services no Python library can instrument.

They will not answer "what did this training run cost", because they report at billing-account granularity with monthly latency. The two are complementary: the provider console for reporting obligations, CodeCarbon for attributing a number to a specific experiment.

"I need something I can cite"

carbontracker, eco2AI and experiment-impact-tracker each have an accompanying academic paper, and so does the methodology behind CodeCarbon. CodeCarbon also has a Zenodo DOI: use the Cite this repository button in the GitHub sidebar, or the BibTeX entry in the repository README. See also the Methodology page. If a reviewer asks how accurate the numbers are, Accuracy and validation is the page to point at — including its list of known gaps.

Note that experiment-impact-tracker's repository is archived, with no commits since January 2024. It remains worth knowing about, because many people find the paper first and then look for the tool. If that is you, CodeCarbon, carbontracker and eco2AI all cover the same ground and are still maintained.

"I'm not on Linux"

CodeCarbon reads powermetrics on macOS (Intel and Apple Silicon) and the Energy Meter Interface on Windows 11, falling back to the TDP estimate elsewhere. Most of the alternatives are Linux-first and rely on RAPL. If you are on a Mac or a Windows machine, check platform support carefully whichever tool you pick — and read the accuracy page, because a fallback estimate on any of these tools is an estimate.