Machine learning operations and large language model operations are the focus of Starwhale’s platform.
Comet
This repository contains machine learning code examples that use Comet.ml.

About Comet
This GitHub repository contains machine learning code examples that show how to use Comet.ml, a platform for experiment tracking. According to the repository's homepage, Comet records datasets, code changes, experiment history, and production models. The examples cover many Python libraries, including fastai, PyTorch, scikit-learn, Keras, and TensorFlow, as well as plain Python and Jupyter notebooks. The repository also includes examples for Opik, Comet's separate open-source LLM observability and evaluation platform.
The repository holds examples, not the Comet platform itself. Comet's Python SDK is stated to be compatible with Python 3.5 to 3.13. Full documentation and additional training examples are hosted on separate Comet documentation pages. Pricing is not stated in the source.
Key features
- Code examples for Comet use with fastai, PyTorch, scikit-learn, Keras, and TensorFlow
- Opik examples for LLM tracing and offline evaluation in a dedicated folder
- Integration examples grouped by ML task, such as model training and evaluation
- Standalone how-to guides, custom panel examples, and general notebooks
- Starter template for contributing new integration examples
- Comet Python SDK installable with pip install comet_ml
- Examples are grouped by framework and ML task, which helps readers find a relevant starting point.
- A starter template and contribution guidelines are provided for adding new examples.
- The Comet Python SDK is stated to be compatible with Python 3.5 to 3.13 only.
- Full documentation and additional training examples are hosted outside this repository.
Comet at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Good for
- Developers
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
Comet alternatives
Tools and sites you could use instead, ranked by how closely they match.
Future AGI evaluates and observes AI and agent applications with tracing, simulations, datasets, and guardrails.
Talos provides tools for running hyperparameter experiments with TensorFlow and Keras.
The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.
The linfa project provides a machine learning framework written in Rust.
Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.
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Comet FAQ
Which Python versions does the Comet Python SDK support?
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