Introduction to ai.deploy.box Project
The ai.deploy.box project, abbreviated as AiDB, is an advanced toolbox designed for deploying deep learning models using C++. The project serves as a unifying platform, integrating multiple mainstream AI inference frameworks, and provides a consistent interface for easier model deployment.
Overview of AiDB
AiDB is a compilation of various deep learning inference frameworks unified under a single project. The primary goal of AiDB is to simplify the deployment process across different environments and use cases by abstracting these frameworks into a common interface.
Supported Platforms and Technologies
The project is remarkably versatile, supporting various operating systems and technologies. Here's a brief overview of the supported platforms and technologies:
- Operating Systems: Linux, MacOS, Android, and Raspberry Pi.
- Development Languages: C, C++, Python, Lua, and Go.
- Inference Frameworks: ONNXRUNTIME, MNN, NCNN, TNN, PaddleLite, and OpenVINO.
Key Features of AiDB
AiDB is packed with numerous features that make it a valuable tool for developers needing to deploy AI models:
- Rich Backend: It incorporates several high-performance inference frameworks, allowing developers to choose the best fit for their specific needs.
- User-Friendly: All frameworks are abstracted into a unified interface, simplifying the selection process through configuration files.
- Multiple Scenarios: AiDB supports diverse environments, suitable for Linux, MacOS, and Android. It provides demo support for PCs (using Qt), Android apps (Kotlin), Lua, and server-side deployments using Go and Python.
- Multi-Language Support: The project includes examples and instances in languages like Python, Lua, and Go, facilitating cross-language compatibility.
Demonstration
AiDB offers an array of demo applications to illustrate its capabilities:
- Web Demo: Users can experience the framework in action through a WebAssembly demo.
- Colab Demos: There are opportunities for users to try out C++, Python, Go, and Lua demos in Google Colab, allowing hands-on exploration of AiDB's features without needing complex setups.
Getting Started
AiDB is designed for quick set-up and use. Although Docker is recommended for a straightforward and consistent environment, users can build AiDB from the source code if preferred. Docker images are readily available for download and deployment.
Model Support and Resources
AiDB supports various models that can be downloaded from platforms like MEGA and Baidu. The supported models range across different types and functionalities, such as face detection, face landmark recognition, and more. There is comprehensive documentation available guiding users through model setup and usage.
Framework Design
The project architecture supports two modes:
- S Mode (C API): For streamlined integration requiring C language compatibility.
- H Mode (C++ API): Leveraging advanced features and functions available in C++.
Conclusion
AiDB is rich in features offering robust support for AI model deployment. It's an essential toolkit for developers seeking to leverage multiple inference frameworks without the hassle of learning each one independently. By harmonizing these technologies under a single interface, AiDB streamlines the deployment process and accelerates development, making it a valuable asset in the field of AI and machine learning.