Understanding gets stronger
when you implement it.

TensorReps is a practice workspace for machine learning engineers. It brings focused coding problems, a PyTorch editor, executable checks, and private progress into one place.

How to get the most out of a rep

1. Read the shape requirements and examples before writing code.
2. Implement the smallest version that works. Run sample checks along the way.
3. Submit to test the edge cases. Read the feedback and try again.
4. Compare the reference implementation after your attempt, then write down what you learned.

A practical beta

The published library and account features are free during beta. Cloud execution uses CPU-only Python and PyTorch, with a 25-second execution limit and a shared usage allowance. Some research and GPU-dependent exercises may need additional dependencies; unavailable checks never count as a fully accepted solution.

This is a learning environment. Test results provide feedback for personal practice, not a credential or a competitive ranking. The test suite cannot prove correctness for every possible input.

Built on generous work

The library combines original exercises with licensed contributions from the open-source community. Each problem identifies its source, and the credits page includes license notices.