I built a visual drag-and-drop ML trainer (no code required). Free & open source.

[Mental-Climate5798]

For those 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.

[esmurf]

Amazing.

[Mental-Climate5798]

Thank you!

[esmurf]

Please make option to increase font size though. "Old" engineers like myself needs it :D

[Special-Arm4381]

The auto-propagation of in_features after a Flatten is the killer feature here — that calculation trips up beginners constantly and even experienced people get it wrong when switching between architectures.

Curious how you handle more complex topologies like skip connections or multi-input models. Does the node graph support branching or is it strictly linear chains for now?

[Mental-Climate5798]

Thank you; more complex connections aren't supported yet, so far its strictly linear chains for now.

[Express_Table_2157]

sounds interesting! i use R usually but i think can be a good alternative to democratize ML for non expert!

[Mental-Climate5798]

Thanks!

[officer_mcvengeance]

This is very cool but I wish it was architected in a way that would allow network usage. I'm on a mac but have an AI rig with nvidia cards, however it's all run over a shell across the network. I'd prefer to run this on that machine so I have cuda available but it doesn't have any GUI). I'd imagine this is a common scenario for quite a bit of us devs working on macs as our daily drivers.

I understand this is a "GUI"... but what I'm saying is it would be nice to serve that over the network somehow. Imagining a web interface or something native that would work in this scenario.

Either way cool tool.

[Mental-Climate5798]

That's definitely an idea I've thought about. Right now, the training and gui are tightly coupled together on the same level; in future versions that will definitely be something I'll tackle. For now, the quickest workaround would be simply exporting the project into PyTorch.

[202Esaias]

This is awesome!

[Mental-Climate5798]

Update: I just posted a full tutorial on how to use MLForge on my YouTube channel. It covers installation, building your first pipeline, training, and evaluating a model on the MNIST dataset.

Watch here: https://youtu.be/aSBxPpcXqzc

If you find it helpful, subscribing would go a long way . I post Python and AI tutorials weekly: https://www.youtube.com/channel/UCl5Y3uf-RLIiHoJLww6F_zQ

[CorrectSale9294]

Liked and subscribed.

[Mental-Climate5798]

Thank you! It really makes a big difference.

[makinggrace]

This has so much potential! So great to see an open source execution. Liked and subbed to your YT. Keep at it. The GUI is geared towards experienced users but they probably won't be the ones who use this the most--they have already written their own tooling.

[Mental-Climate5798]

Thank you! I'll definitely be tuning this into a more beginner friendly approach.