CMake explained

CMake: Empowering AI/ML and Data Science Projects

Table of contents

CMake, standing for "Cross-Platform Make," is an open-source build system that enables the seamless compilation and build process of software projects. It offers a high-level scripting language, allowing developers to define and manage their project's build process efficiently. In the context of AI/ML and Data Science, CMake plays a crucial role in simplifying the deployment and reproducibility of complex projects.

Origins and History

CMake was initially developed by Kitware in the year 2000 to address the challenges faced by developers in building software across multiple platforms. It aimed to provide a unified build system that could generate build files for various platforms, such as Unix, Windows, and macOS. CMake was designed to be simple, extensible, and platform-independent, making it an ideal choice for AI/ML and Data Science projects.

Key Features and Usage

Declarative Configuration

CMake uses a declarative configuration approach, allowing developers to define their project's build process in a clear and concise manner. Developers create a CMakeLists.txt file, where they specify the project's source files, dependencies, compiler flags, and other build-related information. This file acts as the project's build script and provides a comprehensive overview of the project's structure.

Cross-Platform Compatibility

One of the primary advantages of CMake is its ability to generate platform-specific build files, such as Makefiles on Unix-like systems or Visual Studio project files on Windows. This cross-platform compatibility ensures that a project can be easily built and deployed on different operating systems without significant modifications to the build system.

Dependency Management

CMake simplifies dependency management by providing built-in support for finding and linking external libraries. Developers can specify the required dependencies in the CMakeLists.txt file, and CMake automatically handles the process of locating and linking them during the build. This feature is particularly useful in AI/ML and Data Science projects, which often rely on numerous external libraries and frameworks.

Out-of-Source Builds

CMake promotes out-of-source builds, where the build artifacts are stored in a separate directory from the source code. This approach ensures a clean separation between the source code and the build files, making it easier to manage and clean up the build artifacts. Out-of-source builds also facilitate the creation of reproducible environments, a crucial aspect in AI/ML and Data Science.

Integration with IDEs and Build Systems

CMake integrates seamlessly with popular integrated development environments (IDEs) like Visual Studio Code, CLion, and Xcode, providing a user-friendly interface for managing the build process. It also allows for integration with different build systems, such as Make, Ninja, and MSBuild, offering flexibility to developers in choosing their preferred build environment.

Use Cases and Relevance in AI/ML and Data Science

CMake finds extensive application in the AI/ML and Data Science domains due to its ability to handle complex projects with numerous dependencies. Here are a few use cases that highlight CMake's relevance:

AI/ML Libraries and Frameworks

Many AI/ML libraries and frameworks, such as TensorFlow, PyTorch, and OpenCV, utilize CMake as their build system. CMake simplifies the installation and configuration process of these libraries, enabling developers to seamlessly integrate them into their projects. Additionally, CMake's cross-platform compatibility ensures that these libraries can be easily deployed across different operating systems.

Research Projects and Prototypes

In the realm of AI/ML research, where rapid Prototyping and experimentation are common, CMake provides a convenient way to manage project dependencies and build configurations. Researchers can easily set up their project environments, including all the required libraries and dependencies, by leveraging CMake's declarative configuration. This helps in ensuring reproducibility and sharing of research work.

Production Deployment

When transitioning AI/ML models from research to production, CMake plays a crucial role in simplifying the deployment process. By defining the project's dependencies and build configurations, developers can easily package their models along with the required dependencies into a deployable artifact. CMake's out-of-source builds also facilitate the creation of reproducible environments, ensuring consistent behavior across different deployment environments.

Standards and Best Practices

While CMake offers flexibility in its usage, adhering to certain standards and best practices can enhance the efficiency and maintainability of the build system. Here are a few recommendations:

• Organize the project structure into logical directories to improve readability and maintainability.
• Use target-based commands, such as add_executable and add_library, to define project targets explicitly.
• Leverage CMake's find_package command to locate and manage external dependencies efficiently.
• Utilize CMake's generator expressions to handle platform-specific build configurations.
• Separate the build artifacts from the source code by adopting out-of-source builds.

Career Aspects

Proficiency in CMake can significantly boost a data scientist's or AI/ML engineer's career prospects. Many organizations in the industry rely on CMake for managing their build systems, making CMake knowledge highly sought after. Demonstrating expertise in CMake showcases your ability to handle complex project dependencies, facilitate reproducibility, and streamline the deployment process.

Moreover, contributing to open-source projects that utilize CMake can provide valuable experience and exposure to the wider developer community. It allows you to collaborate with other professionals, improve your understanding of best practices, and build a strong portfolio of AI/ML or Data Science projects.

Conclusion

CMake, as a powerful build system, empowers AI/ML and Data Science projects by simplifying the build process, managing dependencies, and ensuring cross-platform compatibility. Its declarative configuration, cross-platform support, and integration with IDEs make it an invaluable tool for developers in the industry. By adhering to best practices and standards, professionals can leverage CMake to enhance project efficiency, reproducibility, and deployment.

References:

• CMake Official Documentation
• CMake Wikipedia Page