VGSLify Documentation

vgslify is a powerful tool for creating and managing models using the Variable-size Graph Specification Language (VGSL). It offers flexibility by supporting both TensorFlow and (planned) PyTorch backends, making it suitable for various deep learning workflows.

This documentation provides a comprehensive guide to getting started with VGSLify, in-depth tutorials, and detailed API references for developers.

Getting Started

New to VGSLify? Follow these guides to install and start building models with VGSL:

Getting Started:

• Introduction
  • Overview of VGSLify
  • Why Use VGSLify?
  • Links to Documentation
• Installation
  • Prerequisites
  • Installing VGSLify
  • Verifying Installation
• Getting Started
  • Overview
  • Simple Example: Building a Model
  • Explanation of Layers
  • Next Steps
• Tutorials
  • Tutorial 1: Building a CNN for Image Classification
  • Tutorial 2: Creating an LSTM for Sequence Prediction

User Guides

Explore advanced usage, examples, and details on the VGSL specification:

User Guides:

• Supported Layers
  • Layer Specifications
  • More Examples
• Converting Models Back to VGSL Spec
  • How It Works

API Reference

For developers looking for more technical details, the following API documentation will help you understand and use the library:

API Reference:

• vgslify package
  • Subpackages
  • Submodules
  • vgslify.generator module
  • vgslify.parser module
  • Module contents
• vgslify.core package
  • Submodules
  • vgslify.core.config module
  • vgslify.core.factory module
  • vgslify.core.parser module
  • vgslify.core.utils module
  • Module contents
• vgslify.parser package
  • Submodules
  • vgslify.parser.tf_parser module
  • Module contents
• vgslify.utils package
  • Submodules
  • vgslify.utils.model_to_spec module
  • Module contents
• vgslify.tensorflow package
  • Submodules
  • vgslify.tensorflow.layers module
  • Module contents

Indices and tables

• Index

• Module Index

• Search Page