Use containers to create software environments that you can use exactly where you need them, whether that’s desktop, server, or cloud environments. Because you install only the software libraries and packages you need for your applications, containers are lightweight and provide a reproducible and reliable way of sharing applications without worrying about configuring installations each time. You can dockerize MATLAB to integrate it with a Continuous Integration/Continuous Delivery (CI/CD) pipeline that is completely containerized. MATLAB has integrations for CircleCI®, Jenkins®, and Travis CI.
The MATLAB Deep Learning Container is designed to take full advantage of high-performance NVIDIA® GPUs to speed up your deep learning applications. You can access the MATLAB Deep Learning Container from anywhere using a web browser or VNC connection.
You can create your own container image with a customized MATLAB installation using the reference Dockerfile on GitHub®. The README file in the GitHub repository shows you how to install MATLAB toolboxes in the container image.
You can also package MATLAB applications in a Docker® container. The resulting image has a much smaller size than that of a fully containerized MATLAB.
- MATLAB Container on Docker Hub
Access MATLAB in the cloud or in server environments by using the MATLAB container image available on Docker Hub.
- MATLAB Deep Learning Container on Docker Hub
Run the MATLAB Deep Learning Container available on Docker Hub in cloud or server environments.
- MATLAB Deep Learning Container on NVIDIA GPU Cloud for Amazon Web Services
Run the MATLAB Deep Learning Container in the cloud on Amazon® Web Services.
- MATLAB Deep Learning Container on NVIDIA GPU Cloud for NVIDIA DGX
Run the MATLAB Deep Learning Container on an NVIDIA DGX machine.
- Cloud AI Workflow Using the Deep Learning Container (Deep Learning Toolbox)
An example workflow for training, importing data, and optimizing a deep neural network in the cloud using the Deep Learning Container.
- Run MATLAB Production Server on Kubernetes Using Containers (MATLAB Production Server)
Deploy MATLAB Production Server™ on a Kubernetes® cluster using Docker containers and Helm® charts.
- MATLAB Runtime Container (MATLAB Compiler)
Learn how to get a MATLAB Runtime container image for use in CI/CD workflows.
- Package MATLAB Standalone Applications into Docker Images (MATLAB Compiler)
Package a MATLAB standalone application into a Docker image.
- Deploy Object Detection Model as Microservice (MATLAB Compiler SDK)
Use a microservice to detect objects in images.
Create Your Own
- Create a Custom MATLAB Container
Create a Docker container image with a custom MATLAB installation.
Learn About Containers
- Get Started with Containers
Learn about containers and the benefits of using them with MATLAB.
- Create Encrypted Connection to Remote Applications and Containers
Learn how to create and encrypted connection to remote applications and containers.
- Configure Containers
Configure containers by specifying environment variables.
- Use GPUs in Containers
Enable GPU use in a container.
- Share Data with Containers
Import and export data in containers.
- Install Updates, Toolboxes, Support Packages, and Add-Ons in Containers
Install updates, toolboxes, and add-ons in containers.
- Save Changes in Containers
Save the changes made in a container for future use.