MATLAB® is a programming platform designed for engineers and scientists. It combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly.
The MATLAB Deep Learning Container provides algorithms, pretrained models, and apps to create, train, visualize, and optimize deep neural networks. You can also access tools for image and signal processing, text analytics, and automatically generating C and CUDA® code for deployment on NVIDIA® GPUs in data centers and embedded systems.
To use the MATLAB Deep Learning Container, you need a license for the MathWorks® products in the container.
To train deep learning models, you need a license for MATLAB, Deep Learning and Parallel Computing toolboxes. If you are licensed to use the additional products in the container, its functionality is extended.
On public cloud instances like Amazon EC2®, you can use a license that is enabled for cloud use. For on-premise DGX use, you can use a concurrent license by specifying the location of the network license manager when you run the container. Individual and Campus-Wide licenses are already configured for cloud use. For other license types, contact your license administrator. You can identify your license type and administrator by viewing your MathWorks Account. Administrators can consult Administer Network Licenses.
If you don't have the necessary products on your individual license, you can get a trial license at MATLAB Trial for Deep Learning on the Cloud.
This quickstart guide will help you launch the MATLAB Deep Learning Container. For more information about MathWorks containers, see the MathWorks Containers documentation.
Before you start the container, check that your graphics driver is up to date. See MATLAB GPU Computing Requirements for details.
If you are using a cloud service provider, ensure that you set up appropriate firewall settings when you launch your instance so that you can connect to the instance from your client machine (in AWS® this is via the Security Group settings).
Open a terminal and connect to the Docker® host from your client machine. Log in to NVIDIA GPU Cloud container registry.
Before running the container, use
docker pull to ensure an up-to-date image is
In the Tags section, locate the container image release that you want to run.
In the Pull column, click the icon to copy the
docker pull command.
pull command on the Docker host machine. It may take several
minutes to pull the container image. Once the pull is complete, you can
launch the container.
Launch the container.
If you have Docker 19.03 or later, a typical command to launch the container is
docker run --gpus all -it --rm -p 5901:5901 -p 6080:6080 --shm-size=512M nvcr.io/partners/matlab:r2023b
If you have Docker 19.02 or earlier, a typical command to launch the container is
nvidia-docker run -it --rm -p 5901:5901 -p 6080:6080 --shm-size=512M nvcr.io/partners/matlab:r2023b
-it means run in interactive mode
--rm will delete the container when finished
-p 5901:5901 and
-p 6080:6080 expose ports 5901 for VNC connection and 6080
for web browser connection
--shm-size=512M sets the size of shared memory to 512MB
:r2023b chooses the R2023b version of the MATLAB Deep Learning Container
You can mount directories to the container for importing and exporting data. For instructions, the MathWorks Containers documentation.
If you are using a cloud service provider or your host or client machines are protected by a firewall, you must set up SSH tunnels between your client machine and the Docker host to access the container desktop. For instructions, see the MathWorks Containers documentation.
When you are done, close the container session by typing
exit from the container
This container includes commercial software products of The MathWorks, Inc. ("MathWorks Programs") and related materials. MathWorks Programs are licensed under the MathWorks Software License Agreement, available in the MATLAB installation in this container. Related materials in this container are licensed under separate licenses which can be found in their respective folders.
For more information about working with MathWorks containers, see MathWorks Containers documentation.
Learn more about using MATLAB for Deep Learning.
To provide suggestions for additional features or capabilities, contact us.
If you require assistance or have a request for additional features or capabilities, please contact MathWorks Technical Support.
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