PyTorchLabFlow 0.2

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.

PyTorchLabFlow

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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

Features

These are not all features that PyTorchLabFlow provides, here are ony few. Read more features with more detailing at

Setting up project

- use `setup_project` for initiate a project.

Read more at github

Training multiple experiments sequentialy

- use `multi_train` to train multiple experiments to a specified epoch (`last_epoch`).

Read more at github

Test model dataset compactibility at the time of model designing

- use `test_mods` to check model's compactibility to dataset.

Read more at github

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

Use previous experiment configurations

- use `use_ppl` to initiate a new experiment with some modified configurations generaly for hyperparameter tuning.

Read more at github

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

License

This project is licensed under the MIT License.