comet-ml
Supercharging Machine Learning
Decision gist · record as of 2026-08-14
Yes, if you run machine learning experiments and want centralized tracking without heavy instrumentation. The low install friction, permissive MIT license, active maintenance, and no known vulnerabilities make it a safe choice. The main condition is willingness to sign up for a Comet.ml account and use their cloud service; if you need fully on-premise or offline experiment tracking, this is not the right tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a free Comet.ml account and API key from https://www.comet.com to send data to the cloud dashboard.
- Low install friction with a pure Python wheel and 16 well-established runtime dependencies.
- Actively maintained with a release 29 days ago.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-07-16 (29 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 768,668 downloads/mo, #5,110 on PyPI
Alternatives
Verify before relying
pip install comet-ml
from comet_ml import Experiment
experiment = Experiment(api_key="YOUR_API_KEY")
# Your training code here- Exact scope of automatic logging (which frameworks and libraries are 'supported' beyond generic stdout/stderr capture)
- Whether the package works offline or requires continuous cloud connectivity during training
- Data retention policies and storage limits for free accounts
What it is and what it does
Comet ML is a cloud-based experiment tracking service that integrates into Python scripts to automatically log training runs, hyperparameters, metrics, and code. It wraps your existing machine learning workflow with minimal code changes—typically just instantiating an Experiment object with an API key—and sends all captured data to a centralized dashboard where you can compare runs, visualize results, and manage experiment history.
The package depends on common utilities like requests, rich, jsonschema, and sentry-sdk to handle HTTP communication, formatted output, validation, and error reporting. It's designed to work with any Python script and claims to have built-in support for popular ML libraries, automatically capturing their hyperparameters and metrics without explicit instrumentation. The service solves the problem of experiment results being lost or forgotten by providing a persistent, queryable record of every training run.
Use it for
- Track and compare hyperparameter tuning experiments across multiple model training runs to identify the best configuration.
- Automatically log stdout, stderr, and code from long-running training jobs for reproducibility and debugging.
- Centralize metrics and model performance data from distributed training across different machines or cloud environments.
- Version control experiment metadata and results for regulatory compliance or research publication.
- Monitor training progress in real-time via a web dashboard while scripts run on local or remote hardware.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run machine learning experiments and want centralized tracking without heavy instrumentation.
The low install friction, permissive MIT license, active maintenance, and no known vulnerabilities make it a safe choice. The main condition is willingness to sign up for a Comet.ml account and use their cloud service; if you need fully on-premise or offline experiment tracking, this is not the right tool.
Install
comet-ml on PyPI
Before you install
Low install friction with a pure Python wheel and 16 well-established runtime dependencies. Actively maintained with a release 29 days ago. Supports Python 3.8 through 3.12.
Requires a free Comet.ml account and API key from https://www.comet.com to send data to the cloud dashboard.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install comet-ml
from comet_ml import Experiment
experiment = Experiment(api_key="YOUR_API_KEY")
# Your training code hereVerify before relying
- Exact scope of automatic logging (which frameworks and libraries are 'supported' beyond generic stdout/stderr capture)
- Whether the package works offline or requires continuous cloud connectivity during training
- Data retention policies and storage limits for free accounts
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagesdulwicheverettimportlib-metadatajsonschemapsutilpython-boxrequests-toolbeltrequestsrichsemantic-versionsentry-sdksetuptoolssimplejsonurllib3wraptwurlitzer |
| Maintenance | Actively maintained 29 days since the last release |
| First released | |
| Downloads | 768,668 / month, #5,110 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: comet_ml-3.58.4-py3-none-any.whl
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