Perceptrain is a Python package that provides a simple interface to execute distributed machine learning training. It supports customization, gradient-based, gradient-free optimizations and various experiment tracking methods.
**For more detailed information, check out the documentation.
**For any questions or comments, feel free to start a discussion. **
Feature highlights
-
Training models made simple with Trainer and Train Configurations.
-
Support for gradient based and gradient free optimization.
-
Accelerator supported distributed training made simple across multi node/multi gpu setups.
-
Extensive callbacks, along with support for mlflow and tensorboard tracking.
Installation guide
perceptrain is available on PyPI and can be installed using pip as follows:
pip install perceptrain
The default, pre-installed backend for perceptrain is PyQTorch, a differentiable state vector simulator. It is possible to install additional following extras:
mlflow: For experiment tracking.
To install individual extras, use the following syntax (IMPORTANT Make sure to use quotes):
pip install "perceptrain[mlflow]"
To install all available extras, simply do:
pip install "perceptrain[all]"
Contributing
Before making a contribution, please review our code of conduct.
- Submitting Issues: To submit bug reports or feature requests, please use our issue tracker.
- Developing in perceptrain: To learn more about how to develop within
perceptrain, please refer to contributing guidelines.
Setting up perceptrain in development mode
We recommend to use the hatch environment manager to install perceptrain from source:
python -m pip install hatch
# get into a shell with all the dependencies
python -m hatch shell
# run a command within the virtual environment with all the dependencies
python -m hatch run python my_script.py
WARNING
hatch will not combine nicely with other environment managers such as Conda. If you still want to use Conda,
install it from source using pip:
# within the Conda environment
python -m pip install -e .
Users also report problems running Hatch on Windows, we suggest using WSL2.
Citation
If you use perceptrain for a publication, we kindly ask you to cite our work using the following BibTex entry:
@article{perceptrain2024pasqal,
title = {perceptrain},
author={Manu Lahariya},
year = {2025}
}
License
Perceptrain is a free and open source software package, released under the Apache License, Version 2.0.
Metadata
Release files for perceptrain 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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|---|---|---|---|
| perceptrain-0.1.0.tar.gz | 66.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| perceptrain-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 139.7 kB
Release files / perceptrain-0.1.0.tar.gz
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| Tags | Source |
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Transparency logRelease files / perceptrain-0.1.0-py3-none-any.whl
| Download URL | perceptrain-0.1.0-py3-none-any.whl |
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| Size | 73.4 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 25, 2025.
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