Neuraxle
Automatic machine learning, hyperparameter tuning, and pipeline abstractions are provided by the Neuraxle software library.
About Neuraxle
Neuraxle is an open source machine learning library for building pipelines from encapsulated steps that can fit and acquire state. Each pipeline step can define its own hyperparameter space, allowing pipelines to be optimized with AutoML methods such as TPE. It can use scikit-learn and TensorFlow models inside pipelines. Pipeline steps support train and test modes, manage how they are saved, and can transform data in parallel using multiprocessing queues. It is installed with pip install neuraxle.
Key features
- Build pipelines from encapsulated, composable steps
- Assign hyperparameter spaces to individual pipeline steps
- Tune pipelines with AutoML methods including TPE
- Use scikit-learn and TensorFlow models inside pipelines
- Support train and test modes with self-managed saving
- Process data in parallel steps via multiprocessing queues
Best for: Python developers building machine learning pipelines that require per-step hyperparameter tuning.
Good for
- Compatible with scikit-learn, TensorFlow, and other libraries
- Open source code available as a repository
- Includes examples for pipelines and model comparison
Neuraxle at a glance
- Website
- github.com/Neuraxio/Neuraxle
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Neuraxle FAQ
How do I install Neuraxle?
Run pip install neuraxle.
Which machine learning libraries can I use with Neuraxle?
Scikit-learn and TensorFlow models can be used inside pipelines, with compatibility stated for most other libraries, frameworks and MLOps environments.
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