Metadata-Version: 2.1
Name: PyTorchLabFlow
Version: 0.2.5
Summary: PyTorchLabFlow is a lightweight framework that simplifies PyTorch experiment management, reducing setup time with reusable components for training, logging, and checkpointing. It streamlines workflows, making it ideal for fast and efficient model development.
Home-page: https://github.com/BBEK-Anand/PyTorchLabFlow
Author: BBEK-Anand
Author-email: 
License: Apache-2.0
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: tqdm

# PyTorchLabFlow

[![PyPI version](https://badge.fury.io/py/pytorchlabflow.svg)](https://badge.fury.io/py/pytorchlabflow)
[![Downloads](https://static.pepy.tech/badge/Pytorchlabflow)](https://pepy.tech/project/Pytorchlabflow)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](./LICENSE)
[![GitHub](https://img.shields.io/badge/GitHub-Repo-black?style=flat&logo=github)](https://github.com/BBEK-Anand/PyTorchLabFlow)

---

**PyTorchLabFlow** simplifies managing deep learning experiments, tracking models, components, performance, and configurations, letting you focus on research.

For end to end use case check [Military_AirCraft_Classification](https://github.com/BBEK-Anand/Military_AirCraft_Classification)

## Features
These are not all features that **PyTorchLabFlow** provides, here are ony few. Read more features with more detailing at[![GitHub](https://img.shields.io/static/v1?label=&message=GitHub&color=black&logo=github&logoColor=white&style=flat-square)](https://github.com/BBEK-Anand/PyTorchLabFlow)

### Setting up project
    - use `setup_project` for initiate a project.
   [Read more at github](https://github.com/BBEK-Anand/PyTorchLabFlow#setup_project)
    
### Training multiple experiments sequentialy
    - use `multi_train` to train multiple experiments to a specified epoch (`last_epoch`).
   [Read more at github](https://github.com/BBEK-Anand/PyTorchLabFlow#multi_train)

### Test model dataset compactibility at the time of model designing
    - use `test_mods` to check model's compactibility to dataset.
   [Read more at github](https://github.com/BBEK-Anand/PyTorchLabFlow#test_mods)
   
### Transfer experiment to a high-end system
    - use `transfer` to make all nessessary files of experiments to `internal/Transfer` folder, and then copy the folder to other system.
   [Read more at github](https://github.com/BBEK-Anand/PyTorchLabFlow#transfer)

### Use previous experiment configurations
    - use `use_ppl` to initiate a new experiment with some modified configurations generaly for hyperparameter tuning.
   [Read more at github](https://github.com/BBEK-Anand/PyTorchLabFlow#use_ppl)

### Plot performance of multiple experiments at a time
    - use `performance_plot` to plot experiments' performance over epochs individualy but at a time.
   [Read more at github](https://github.com/BBEK-Anand/PyTorchLabFlow#performance_plot)


# License
This project is licensed under the Apache License 2.0.

