scicomp-neural-mcp

mcp-name: io.github.andylbrummer/neural-mcp

MCP server for neural network training and experimentation.

Overview

This server provides tools for building, training, and analyzing neural networks:

• Model building - Pre-trained models (ResNet, MobileNet) and custom architectures
• Dataset loading - CIFAR-10, MNIST, ImageNet datasets with standard preprocessing
• Training - Full training loops with configurable learning rates and batch sizes
• Evaluation - Model evaluation, metrics computation, and analysis
• Hyperparameter tuning - Automated hyperparameter search
• Export - Export models to ONNX and TorchScript formats
• GPU acceleration - Optional CUDA acceleration for training

Installation & Usage

# Run directly with uvx (no installation required)
uvx scicomp-neural-mcp

# Or install with pip
pip install scicomp-neural-mcp

# With GPU support (recommended for training)
pip install scicomp-neural-mcp[gpu]

# Run as command
scicomp-neural-mcp

Available Tools

Model Management

• define_model - Create neural network models (ResNet18, MobileNet, custom)
• load_pretrained - Load pretrained models from torchvision or Hugging Face
• get_model_summary - Get detailed layer-by-layer breakdown
• export_model - Export to ONNX or TorchScript

Data Loading

• load_dataset - Load standard datasets (CIFAR-10, MNIST, ImageNet)
• create_dataloader - Create batched dataloaders with shuffling

Training

• train_model - Train model on dataset with configurable parameters
• get_experiment_status - Monitor training progress
• evaluate_model - Evaluate on test set

Analysis

• compute_metrics - Compute detailed performance metrics
• confusion_matrix - Generate confusion matrices
• plot_training_curves - Visualize loss and accuracy curves
• visualize_predictions - Inspect model predictions on samples

Hyperparameter Optimization

• tune_hyperparameters - Automated hyperparameter search

Configuration

Enable GPU acceleration with environment variable:

MCP_USE_GPU=1 scicomp-neural-mcp

Examples

📖 Code Examples

Practical tutorials in EXAMPLES.md:

• MNIST digit recognition (complete workflow)
• Transfer learning with ResNet
• Hyperparameter optimization
• Confusion matrix analysis
• Progressive learning path (beginner → advanced)

📚 Full Documentation

See the API documentation for complete API reference.

Part of Math-Physics-ML MCP System

Part of a comprehensive system for scientific computing. See the documentation for the complete ecosystem.