I visually created and trained a CNN in 2 minutes, with no code.

For those who are tired of writing the same ML boilerplate every single time or to beginners who don't have coding experience.

MLForge is an app that lets you visually craft a machine learning pipeline.

You build your pipeline like a node graph across three tabs:

Data Prep - drag in a dataset (MNIST, CIFAR10, etc), chain transforms, end with a DataLoader. Add a second chain with a val DataLoader for proper validation splits.

Model - connect layers visually. Input -> Linear -> ReLU -> Output. A few things that make this less painful than it sounds:

• Drop in a MNIST (or any dataset) node and the Input shape auto-fills to 1, 28, 28


• Connect layers and in_channels / in_features propagate automatically


• After a Flatten, the next Linear's in_features is calculated from the conv stack above it, so no more manually doing that math


• Robust error checking system that tries its best to prevent shape errors.

Training - Drop in your model and data node, wire them to the Loss and Optimizer node, press RUN. Watch loss curves update live, saves best checkpoint automatically.

Inference - Open up the inference window where you can drop in your checkpoints and evaluate your model on test data.

Pytorch Export - After your done with your project, you have the option of exporting your project into pure PyTorch, just a standalone file that you can run and experiment with.

Free, open source. Project showcase is on README in Github repo.

GitHub: https://github.com/zaina-ml/ml_forge

To install MLForge, enter the following in your command prompt

pip install zaina-ml-forge

Then

ml-forge

Please, if you have any feedback feel free to comment it below. My goal is to make this software that can be used by beginners and pros.

This is v1.0 so there will be rough edges, if you find one, drop it in the comments and I'll fix it.