Caution

hipDNN is in the early stages of development. There is currently limited functionality available to execute graphs. See Operation Support for reference.

Note

The published hipDNN documentation is available here in an organized, easy-to-read format, with search and a table of contents. The documentation source files reside in the docs folder of this repository. As with all ROCm projects, the documentation is open source. For more information on contributing to the documentation, see Contribute to ROCm documentation.

Overview

hipDNN is a graph-based deep learning library for AMD GPUs that leverages a flexible plugin architecture to provide optimized implementations and utilities for various routines.

Getting Started
Documentation
Project Structure
API Documentation
Contributing

Getting Started

The fastest way to get started with hipDNN is to follow the quick start steps in the build guide.

Documentation

User Guides

Building - Prerequisites, build configurations, and platform-specific instructions
Consumer Quick Start - Using an installed hipDNN in your CMake project
How-To - Using hipDNN components and extending the framework
Environment Configuration - Runtime configuration and logging setup
Operation Support - Currently supported operations and their status
Samples - Frontend usage examples
API Reference - Doxygen-generated API documentation

Developer Guides

Design Overview - Architecture and design descriptions and diagrams
Extending hipDNN - How to extend hipDNN functionality
Plugin Development - Creating and using custom plugins for hipDNN
Roadmap - Feature priorities and development plans

Testing

Testing - Synopsis of testing information
Testing Strategy - Specific testing approach
Test Plan - Detailed test planning
Test Run Template - Guidelines for test execution

Project Structure

hipDNN is organized into several key components. For detailed architecture descriptions, see the Design Overview.

Component	Description
Backend	Core shared library providing C API for operation graphs and managing plugins
Data SDK	Header-only library with shared types, tensor utilities, logging, and the engine name registry
Flatbuffers SDK	Header-only library with FlatBuffers schemas, generated headers, graph wrappers, and optional JSON helpers
Frontend	Header-only C++ API wrapper around the backend
Plugin SDK	Header-only library for plugin development
Samples	Example implementations demonstrating hipDNN usage
Tests	Tests for the public API (incl. frontend integration tests)
Tools	Experimental utilities (e.g., benchmarking, engine listing) — subject to change

Note

Official hipDNN plugins can be found in the dnn-providers folder (e.g., MIOpen Plugin).

Docker Support

See Docker README for containerized development environments.

API Documentation

hipDNN includes Doxygen-generated API documentation for the public C++ frontend.

Building the Documentation

Install Doxygen:
```
# Ubuntu/Debian
sudo apt-get install doxygen
```
For Windows, download the installer from doxygen.nl/download.
Generate the documentation:
```
cd <project-root>
doxygen Doxyfile
```

Open the generated documentation:

# Linux
xdg-open build/docs/html/index.html

# Windows
start build\docs\html\index.html

The documentation covers the frontend API including:

Graph construction and execution
Tensor and operation attributes
Engine configuration and knobs
Error handling

Contributing

For information about contributing to the hipDNN project, please see the Contributing Guide.

README.md

hipDNN