andresnowak / dainemo

A Machine Learning framework from scratch in Mojo 🔥

Dainemo

A Machine Learning framework from scratch in Mojo 🔥

Examples Status

Task	Dataset	Forward	Backward	Training
REGRESSION	✅	✅	✅	✅
MNIST	✅	❌	❌	❌

_{Regression example:}

Getting started

Running a regression example

mojo run -I . examples/housing.mojo

Compare to a alternative PyTorch implementation:
Install the requirements in python-requirements.txt

python examples/housing.py

Progress

❌: Not implemented
✅: Working (but might require changes because of not implemented dependencies)
WIP: Work in progress

Autograd

Task	Status
NODE	✅
GRAPH	✅

Operators

Task	Status
ADD	✅
SUB	✅
MUL	✅
DIV	✅
DOT	✅
EXP	✅
LOG	✅
POW	✅
SUM	✅*
TRANSPOSE	✅
FLATTEN	✅
RESHAPE	✅
CONV2D	✅
CONV3D	❌
MAXPOOL2D	❌
MAXPOOL3D	❌

Loss Functions

Task	Status
MSE	✅
CE	❌
BCE	❌
SoftmaxCE	❌

Activations

Task	Status
RELU	❌
SIGMOID	❌
TANH	❌
SOFTMAX	❌
LEAKYRELU	❌

Optimizers

Task	Status
ADAM	✅

Layers

Task	Status
SEQUENTIAL	❌
LINEAR	✅
DROPOUT	❌
CONV2D	✅
CONV3D	❌
MAXPOOL2D	❌
MAXPOOL3D	❌

Other

Task	Status
Model abstractions (eval/save/load/...)	❌
Datasets (MNIST/Boston Housing)	✅
Dataloader	✅
Tensorutils	✅
Checkpoints	❌

Datasets

Task	Original	Included Kaggle CSV
Bosten Housing Dataset	names, data	kaggle
MNIST DataSet	training images, training labels, test images, test labels	(subset of) kaggle

Contributing

Policy WIP.

This project is in active development, and we welcome contributions:

1. Fork the repository on GitHub.
2. Create a new branch for your feature or bug fix.
3. Commit your changes with clear, descriptive commit messages.
4. Push your branch to GitHub and submit a pull request.

We appreciate any and all contributions, whether they're for bug fixes, new features, or documentation improvements.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgements

A huge thanks to pranftw for the inspiration and support! This project is inspired by neograd.