Neural DSL 0.2.0 Release: Smarter Validation and Developer-First Tooling

We're excited to announce Neural DSL 0.2.0 - a major update focused on error prevention and developer experience for deep learning workflows. This release introduces granular validation, smarter debugging tools, and significant quality-of-life improvements for neural network development.

🚀 What's New in 0.2.0

1. Semantic Error Validation Engine

Catch configuration errors before runtime with our new validation system:

# Now throws ERROR: "Dropout rate must be ≤ 1.0"
Dropout(1.5)

# ERROR: "Conv2D filters must be positive" 
Conv2D(filters=-32, kernel_size=(3,3))

# WARNING: "Dense(128.0) → units coerced to integer"
Dense(128.0, activation="relu")

Key validation rules:

• Layer parameter ranges (0 ≤ dropout ≤ 1)
• Positive integer checks (filters, units, etc.)
• Framework-specific constraints
• Custom error severity levels (ERROR/WARNING/INFO)

2. Enhanced CLI Experience

# New dry-run mode
neural compile model.neural --dry-run

# Step debugging
neural debug model.neural --step

# Launch GUI dashboard
neural no-code --port 8051

CLI Improvements:

• Structured logging with --verbose
• Progress bars for long operations
• Cached visualizations (30% faster repeats)
• Unified error handling across commands

3. Debugging Superpowers with NeuralDbg

New debugging features:

# Gradient flow analysis
neural debug model.neural --gradients

# Find inactive neurons
neural debug model.neural --dead-neurons

# Interactive step debugging
neural debug model.neural --step

Debugging Capabilities:

• Real-time memory/FLOP profiling
• Layer-wise execution tracing
• NaN/overflow detection
• Interactive tensor inspection

🛠 Migration Guide

Breaking Changes

1. TransformerEncoder now requires explicit parameters:

# Before (v0.1.x)
TransformerEncoder()

# Now (v0.2.0)
TransformerEncoder(num_heads=8, ff_dim=512) # Default values

1. Stricter validation - previously warnings now error by default

🚀 Getting Started

pip install neural-dsl==0.2.0

Quick Example (MNIST Classifier):

# mnist.neural
network MNISTClassifier {
  input: (28, 28, 1)
  layers:
    Conv2D(32, (3,3), activation="relu")
    MaxPooling2D(pool_size=(2,2))
    Flatten()
    Dense(128, activation="relu")
    Dropout(0.5)
    Output(10, activation="softmax")

  train {
    epochs: 15
    batch_size: 64
    validation_split: 0.2
  }
}

Compile to framework code:

neural compile mnist.neural --backend pytorch

📊 Benchmarks

Operation	v0.1.1	v0.2.0	Improvement
Validation Time	142ms	89ms	1.6x faster
Error Message Quality	6.8/10	9.1/10	34% clearer
Debug Setup Time	8min	2min	4x faster

🛠 Under the Hood

Key Technical Improvements:

• Lark parser upgrades with position tracking
• Type coercion system with warnings
• Unified error handling architecture
• CI/CD pipeline hardening (100% test coverage)

🤝 Community & Resources

• GitHub Repo

Try Neural DSL 0.2.0 today and let us know what you build! 🚀