AI with C++ is also gaining popularity across artificial intelligence applications, Although it is common to use programming languages such as Python, Java, R, JavaScript, and Julia. It is beneficial across systems that necessitate hardware control, efficiency, and performance as critical criteria.
Applications of AI with C++
C++ finds applications to deploy models trained using the Python programming across production systems to ensure speed and efficiency.
Libraries in Machine Learning:
Several machine learning libraries, such as dlib, OpenCV, PyTorch (LibTorch), and Tensorflow, are popular examples that use C++ as a backend. OpenCV with C++ as the fundamental programming language is popular across computer vision applications. Likewise, dlib uses the C++ toolkit along with ML tools and algorithms for computer vision. Developers can write the code for TensorFlow in C++, although Python is used many times.
Performance-Crucial Applications:
Specific applications, such as gaming apps, trading systems, and real-time systems, require higher performance. For example, in Gaming AI, applications in Unreal Engine, trading systems for high-frequency trading, and real-time systems like embedded AI and robotics prefer performance. Such applications employ AI with C++.
Pros and Cons of using C++ in AI:
Employing AI with C++ offers several advantages, including excellent memory management, rapid execution, optimized performance, and greater control over hardware. AI, combined with C++, finds extensive use across constrained environments, such as robotics, mobile apps, and IoT devices.
However, certain challenges arise when employing AI with C++, including reduced intuitiveness and increased verbosity, which are not common in high-level programming languages such as Python. Also, the developer community is smaller, with only a few AI libraries with high-level capabilities. Slower experimentation and prototyping is also another challenge.
What is the typical workflow format for using AI with C++?
In the first step, the AI model requires training in Python using tools such as PyTorch and TensorFlow. Next, uses libraries such as ONNX to export the model. Lastly, deploy and run the interface in a C++ environment using LibTorch and ONNX Runtime libraries.
When to Deploy AI with C++:
It is ideal to use C++ across projects that require higher performance, particularly when integrating artificial intelligence into larger C++ systems, such as custom hardware and game engines. Additionally, for projects that do not need Python as the primary programming language, C++ is a better choice across various platforms, including embedded systems and microcontrollers.
Let’s discuss some advantages of AI with C++:
Libraries and Ecosystem:
Several high-performance AI/ML libraries also offer C++ APIs. Some include TensorFlow for the C++ backend, OpenCV for computer vision, and PyTorch for LibTorch.
System-wide Integration:
It is common to use C++ across large-scale systems and production environments. It also finds use during AI integration with existing C++ codebases, such as embedded systems.
Accessing Low-Level Hardware:
It is essential to integrate AI with edge devices or embedded systems, including drones or IoT devices. Furthermore, it becomes easy to work directly with hardware services such as robotics, sensors, and GPU drivers.
High Performance:
Among the various programming languages, C++ fares higher in terms of speed. As a result, it offers fine-grained control while using CPU, GPU, and memory allocation. Among the various devices, AI with C++ is more profoundly beneficial across real-time AI systems such as gaming, robotics, and autonomous vehicles.
Deployment:
It is more useful when deploying AI across environments with limited resources, as it requires minimal memory usage.
Determinism and Control:
It is easier to enforce deterministic execution across AI-powered devices with safety as a critical factor. Automotive systems and medical devices are a few examples.
Compatibility with Python Models:
Another advantage is that it can run Python-trained models by using LibTorch, TensorRT, and ONNX Runtime.