A user friendly package for training quality prediction models
Project description
⚠️ Note: This package is currently in active development. It is not yet feature-complete and lacks comprehensive documentation. Users should expect changes and potential instability.
PQagent
PQagent is a Python package designed to streamline the development and training of predictive quality models using neural networks. Built on the powerful PyTorch framework, PQagent offers a comprehensive suite of tools for data preprocessing, model training, and evaluation, enabling users to construct robust predictive models tailored to their specific quality assurance needs.
For more details, please study the Documentation.
🧩 Features
to be documented
🚀 Quickstart Example
to be documented
Installation
pip install pqagent
©️ License
This project is licensed under the MIT License.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pqagent-0.0.12.tar.gz.
File metadata
- Download URL: pqagent-0.0.12.tar.gz
- Upload date:
- Size: 16.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
db6a352e831129926371d1d56ca0fada32d87f7cef9a8afe73c5d51c9fb98cbf
|
|
| MD5 |
e17a1434f13a068a00f0375dafcc3432
|
|
| BLAKE2b-256 |
a211b5a869574e4cf3ad5edb9e70edc1e6e3a0c0d0ce2fc62541d49fc39a7222
|
File details
Details for the file pqagent-0.0.12-py3-none-any.whl.
File metadata
- Download URL: pqagent-0.0.12-py3-none-any.whl
- Upload date:
- Size: 18.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f2b7dadfdfa5f8449469e14b4a0414bc6105f58483469cb22d3b03b911020769
|
|
| MD5 |
d86d96e5cd48c930970a87d684390659
|
|
| BLAKE2b-256 |
c7e1473d053e23afdc253c2db0601527c9f57093ac9d575c9c220473a834cb77
|
