Explore Neural: The Next-Generation DSL and Debugging Solution for Neural Networks

👾 I'm excited to share "Neural," a project I've been working on to make neural network development easier. Neural is a domain-specific language (DSL) and debugger that allows you to define, train, and debug models effortlessly whether through code, CLI, or a no-code interface. 🎛

Neural-dsl is a WIP DSL and debugger, bugs exist, feedback is welcome!

Why Neural?

Building neural networks can be complex boilerplate code, shape mismatches, and debugging woes slow us down.

Neural tackles this with:

• YAML-like Syntax: Define models intuitively without framework boilerplate.

• Shape Propagation: Catch dimension mismatches before runtime.

  • ✅ Interactive shape flow diagrams included.

• Multi-Framework HPO: Optimize hyperparameters for both PyTorch and TensorFlow with a single DSL config (#434).

• Multi-Backend Export: Generate code for TensorFlow, PyTorch, or ONNX.

• Training Orchestration: Configure optimizers, schedulers, and metrics in one place.

• Visual Debugging: Render interactive 3D architecture diagrams.

• NeuralDbg: NeuralDbg provides real-time execution tracing, profiling, and debugging, allowing you to visualize and analyze deep learning models in action.

• Extensible: Add custom layers/losses via Python plugins.Example: MNIST Classifier

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Here’s a quick .neural file:

network MNISTClassifier { 
input: (28, 28, 1) 
layers: Conv2D(filters=32, kernel_size=(3,3), activation="relu") 
        MaxPooling2D(pool_size=(2,2)) 
        Flatten() Dense(units=128, activation="relu") 
        Output(units=10, activation="softmax") 
loss: "sparse_categorical_crossentropy" 
optimizer: Adam 
}

Compile And Run

pip install neural-dsl neural compile mnist.neural --backend pytorch neural run mnist_pytorch.py

Debug And Test

neural debug mnist.neural --hacky

Visit http://localhost:8050 to see gradients, resource usage, and potential security breaches.

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What’s Next?

I’m adding automatic hyperparameter optimization (HPO), research paper generation, and TensorBoard integration.

Try it out on GitHub and let me know what you think!

🦾 Share your feedback. I’d love to hear from the community