C++ Generative AI Inference: Production Ready Speed and Control

C++ methods to improve speed and control of Generative AI inferencing will be covered. C++ is an object-oriented programming language which gives a clear structure to programs and allows code to be reused. (1)

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Prompt: What are all the ways to use pure C++ with Generative AI for inferencing, in a brief summary?

Output: Here's a brief summary of various ways to use pure C++ with generative AI for inference:

1. **TensorFlow Lite**: Use TensorFlow's lightweight version, TensorFlow Lite (TFLite), which allows you to run trained models on embedded systems or mobile devices using only C++. TFLite provides an API for building and running machine learning models in C++.
2. **OpenVINO**: OpenVINO is a software development kit that enables developers to optimize computer vision and deep learning applications for various platforms, including edge devices. It supports C++ as one of its programming languages.
3. **Caffe2**: Caffe2 is an open-source machine learning framework developed by Facebook. It provides a C++ API for building and running neural networks on various platforms, including embedded systems.
4. **CNTK**: CNTK (Cognitive Toolkit) is another popular deep learning library that supports C++. You can use it to build and run generative AI models in pure C++.
5. **MLPACK**: MLPACK is a machine learning library written entirely in C++. It provides an API for building and running various types of machine learning models, including neural networks.
6. **Dlib**: Dlib is a modern C++ library containing machine learning algorithms, image processing tools, and other utilities. You can use it to build generative AI models using pure C++.
7. **Eigen**: Eigen is an efficient linear algebra library written in C++. While not specifically designed for deep learning or generative AI, you can still use it as a building block for your own machine learning projects.

These libraries and frameworks provide various levels of support for running generative AI models on embedded systems or mobile devices using pure C++:

* Some (like TensorFlow Lite) are optimized for specific platforms.
* Others (like OpenVINO) offer more general-purpose support across multiple platforms.
* A few (like MLPACK, Dlib, and Eigen) provide building blocks that you can use to create your own machine learning projects.

Keep in mind that while these libraries allow you to write pure C++ code for generative AI inference, they may still rely on other dependencies or frameworks under the hood. End Generation

References:

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