Minimal DL library in C – 24 NAIVE CUDA/CPU ops, autodiff, Python API

In plain words

Minimal DL library in C is a deep learning framework that implements 24 basic operations for both CUDA and CPU, with automatic differentiation support. It's designed for developers who want a lightweight alternative to larger frameworks, offering a Python API for ease of use. The library focuses on simplicity and core functionality rather than comprehensive features, making it suitable for learning, prototyping, or embedding in C-based projects.

written from the facts on this page · September 2026

From the sources

minimal DL library in C: 24 NAIVE cuda/cpu ops, autodiff engine, python API (ops bindings/layers/models), tensor abstraction, strides, complex indexing (multi-dim slices like numpy), computation-graph visualizer, saving/loading params, primitive garbage collection, autogenerated tests, optimizers, weight initializers, etc. (no AI assistance used) - IaroslavElistratov/ml-systems-course

ML systems + GPU programming exercise -- build a small (but non-toy) DL stack end-to-end and learn by implementing the internals. rm generated/ * # define Conv-Net and train on GPU # (you can change DEVICE macro to run on CPU instead) nvcc models/conv_net.cu && ./a.out # optionally, visualize model graph cat generated/graph.txt | dot -Tsvg -o generated/conv_net.svg # optionally, run the generated test code python generated/codegen.py MLP rm generated/ * # define MLP and train on CPU # (you can change DEVICE macro to run on GPU instead) nvcc models/mlp.cu && ./a.out # optionally, visualize model graph cat generated/graph.txt | dot -Tsvg -o generated/mlp.svg # optionally, run the generated…from github.com

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