A Domain-Specific Language for Neural Networks
Neural is under active development—not production-ready yet. Bugs exist, feedback welcome!
Neural simplifies deep learning with a declarative syntax, cross-framework support, and built-in debugging via NeuralDbg. Define, train, and deploy neural networks with ease.
Neural tackles key challenges in deep learning:
| Criticality / Impact | Low Impact | Medium Impact | High Impact |
|---|---|---|---|
| High | - | - |
- Shape Mismatches: Pre-runtime validation. - Debugging Complexity: Real-time tracing. |
| Medium | - | - Steep Learning Curve: No-code GUI. |
- Framework Switching: One-flag swaps. - HPO Inconsistency: Unified tuning. |
| Low | - Boilerplate: Clean syntax. |
- Model Insight: FLOPs & diagrams. - Config Fragmentation: Centralized setup. |
- |
git clone https://github.com/Lemniscate-world/Neural.git
cd neural
pip install -r requirements.txt
neural run examples/mnist.neural --backend pytorch
Debug with: neural debug examples/mnist.neural
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