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build or use a prebuilt docker container image to deploy matlab^® in cloud and server environments

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.

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 with a customized matlab installation using a reference dockerfile.

topics

matlab

access matlab on the cloud or in server environments by using the matlab container available on docker^® hub.

deep learning

run the matlab deep learning container available on docker hub in cloud or server environments.
run the matlab deep learning container in the cloud on amazon^® web services.
run the matlab deep learning container on an nvidia^® dgx machine.
(deep learning toolbox)
an example workflow for training, importing data, and optimizing a deep neural network in the cloud using the deep learning container.

matlab production server

(matlab production server)
deploy matlab production server™ on a kubernetes^® cluster using docker containers and helm^® charts.

create your own

create a docker container image with a custom matlab installation.

learn about containers

learn about containers and the benefits of using them.
learn how to create and encrypted connection to remote applications and containers.
configure containers by specifying environment variables.
enable gpu use in a container.
import and export data in containers.
install updates, toolboxes, and add-ons in containers.
save the changes made in a container for future use.