Neural
DSL for defining, training, debugging neural networks.
Listed in categories:
Developer ToolsArtificial IntelligenceOpen Source
Description
Neural is a domain-specific language (DSL) designed for defining, training, debugging, and deploying neural networks. It features a declarative syntax, cross-framework support, and built-in execution tracing through NeuralDbg, simplifying deep learning development and addressing common challenges such as debugging complexity and shape mismatches.
How to use Neural?
To use Neural, clone the repository, create a virtual environment, install dependencies, define your model using the DSL syntax, and run or compile the model using the provided commands. You can also visualize the architecture and debug using the NeuralDbg interface.
Core features of Neural:
1️⃣
YAML-like Syntax for intuitive model definition
2️⃣
Shape Propagation to catch dimension mismatches before runtime
3️⃣
Multi-Framework Hyperparameter Optimization (HPO)
4️⃣
Visual Debugging with interactive 3D architecture diagrams
5️⃣
Real-Time Execution Monitoring with NeuralDbg
Why could be used Neural?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Building and training neural networks for image classification | ✅ | |
| # 2 | Debugging deep learning models to identify issues like vanishing gradients | ✅ | |
| # 3 | Optimizing hyperparameters across different frameworks like TensorFlow and PyTorch | ✅ | |
Who developed Neural?
Lemniscate is the maker of Neural, focusing on simplifying deep learning development through innovative tools and frameworks. They aim to lower barriers for developers and enhance workflows in the machine learning community.