Why MATLAB Containers Are a Gamechanger

Organizations are increasingly adopting containerization to streamline software development and deployment processes. Containers provide portability, consistency across environments, and efficient resource utilization. In MLOps projects, where data scientists often rely on MATLAB, containerization is particularly beneficial.

Consider a team developing machine learning models in MATLAB. Without containers, deploying these models across different environments can be challenging. By packaging MATLAB functions with their dependencies into containers, teams ensure reliable execution across systems.

Key advantages include seamless migration between development, testing, and production environments, eliminating compatibility issues. Combined with Kubernetes, MATLAB workloads can be scaled dynamically. This approach also enhances collaboration by allowing data scientists to focus on model development while DevOps teams manage deployment.

MATLAB Runtime – The Foundation for Containerization

MATLAB Runtime is essential for running MATLAB functions in containers without requiring a full MATLAB installation. It consists of shared libraries that enable executing compiled MATLAB applications. This approach reduces licensing overhead and simplifies large-scale deployments.

There are two ways to use MATLAB Runtime in containers:

• Pre-built MathWorks containers: Available via Docker Hub, these images are optimized for standard deployments.
• Custom-built containers: Ideal for projects requiring additional configurations, such as integrating Python libraries or streaming tools like Apache Kafka.

Custom images provide greater flexibility, enabling seamless integration with other programming environments, custom system libraries, or optimized container sizes.

Preparing MATLAB Code for Containers

Several methods exist for exporting MATLAB functions for container use, depending on project needs:

• MATLAB Compiler for standalone applications:
  • Best for distributing fully executable applications.
  • Suitable for GUI-based applications.
  • Does not require MATLAB installation on the end-user’s system.
• MATLAB Coder for C/C++ integration:
  • Ideal for high-performance applications or embedding MATLAB functions into existing C/C++ projects.
  • Useful for embedded systems where MATLAB Runtime is not available.
• MATLAB Compiler SDK for Python workflows:
  • Enables MATLAB functions to be used within Python environments.
  • Suitable for machine learning and data science pipelines.

Choosing the right method depends on deployment needs, performance requirements, and the target environment.

How to Containerize MATLAB Functions

To containerize MATLAB functions efficiently, follow these steps:

1. Prepare the MATLAB code: Ensure functions are well-structured with clearly defined dependencies.
2. Select an export method: Decide between standalone applications, C/C++ modules, or Python packages.
3. Create a Dockerfile: Define the container environment, including the base MATLAB Runtime image and required dependencies.
4. Build the container image: Use Docker commands to create and optimize the image.
5. Test locally: Verify that the MATLAB application runs as expected within the container.
6. Push to a registry: Store the container in a repository for easy deployment.

By following these steps, MATLAB containers can be efficiently built, scaled, and maintained.

Scaling MATLAB Containers with Kubernetes

Kubernetes provides a robust framework for deploying and managing MATLAB containers at scale. It automates deployment, scaling, and monitoring of workloads.To run MATLAB containers in Kubernetes:

• Prepare container images and upload them to a container registry accessible by the Kubernetes cluster.
• Define Kubernetes deployments specifying runtime parameters and resource allocations.
• Expose MATLAB services via Kubernetes networking tools, such as REST APIs or Apache Kafka for real-time data processing.
• Implement auto-scaling to dynamically adjust the number of MATLAB containers based on demand.
• Enable rolling updates for seamless deployment of new versions without downtime.

Kubernetes ensures efficient resource utilization and high availability for MATLAB workloads in production.

Conclusion: Why MATLAB Containers Are Worth It

Containerizing MATLAB functions unlocks new levels of scalability, flexibility, and reliability. It eliminates deployment inconsistencies and enables smooth integration with modern cloud-based workflows. However, challenges like licensing management and performance optimization remain key considerations.Future developments in MATLAB containerization may include:

• Stronger integrations with container orchestration platforms.
• Enhanced performance tuning for MATLAB containers.
• Increased security measures for regulated industries.
• Greater adoption of MATLAB in microservices architectures.

As containerization evolves, MATLAB users will experience even greater efficiency in cloud-native environments, transforming how MATLAB applications are deployed and managed.