SynapseGrid

SynapseGrid is an autonomous neural architect system designed to streamline the process of building, training, and explaining deep learning models. it acts as an AI-powered assistant that analyzes your task, designs the optimal neural architecture, and generates production-ready pytorch code

features

• intelligent analysis: automatically analyzes task descriptions (e.g., "classify images", "predict housing prices") and data types to determine the best modeling approach

• automated architecture design: designs neural networks (CNNs, MLPs, transformers) tailored to your specific task and compute budget

• code generation: generates a complete, runnable pytorch project structure including:

  • model.py: the model definition

  • train.py: training loop with validation and logging

  • hpo.py: hyperparameter optimization scripts (using optuna)

  • explain.py: explainable AI (XAI) scripts using SHAP

• explainable AI (XAI): built-in support for generating explanation scripts to understand model decisions (SHAP/GradCAM)

• deployment ready: tools to package and deploy trained models

installation

1. clone the repository:

   git clone https://github.com/Youcef3939/SynapseGrid.git 
   cd SynapseGrid

2. install dependencies:

   pip install -r requirements.txt

usage

SynapseGrid provides a powerful command line interface (CLI)

1. build a model

to generate a project for a specific task:

python -m synapse_grid.cli build --task "classify images of cats and dogs" --data ./data/cats_dogs --compute medium

options:

• --task: description of what you want to solve

• --data: path to your dataset

• --compute: compute budget (low, medium, high)

• --hpo: enable hyperparameter optimization (flag)

2. deploy a model

to prepare a trained model for deployment:

python -m synapse_grid.cli deploy --model-path ./output/checkpoints/model.pth

generated project structure

when you run build, SynapseGrid creates a directory with:

output/
├── model.py       # the neural network architecture
├── train.py       # training script
├── explain.py     # XAI explanation script
├── hpo.py         # HPO script
├── data/          # data directory
└── checkpoints/   # model checkpoints

dataflow

interface architecture

pipeline architecture

running tests

to run the internal test suite:

pytest tests/

---

this project is designed for researchers, engineers, and teams who need fast, reliable neural architectures without spending hours tuning models by hand<3