MATLAB Deep Learning Container on NVIDIA GPU Cloud for Amazon Web Services

Speed up your deep learning applications by training neural networks in the MATLAB^® Deep Learning Container, designed to take full advantage of high-performance NVIDIA^® GPUs. You can access the MATLAB Deep Learning Container remotely using a web browser or via a VNC connection.

The MATLAB Deep Learning Container contains MATLAB and a range of MATLAB toolboxes that are ideal for deep learning (see Additional Information).

This guide helps you run the MATLAB desktop in the cloud on an Amazon EC2^® P3 instance. For other cloud service vendors, the required steps are different. The MATLAB Deep Learning Container, a Docker container hosted on NVIDIA GPU Cloud, simplifies the process. The container is available at the NVIDIA GPU Cloud Container Registry.

Requirements

Amazon^® Web Services account
A MATLAB license valid for the products in the MATLAB Deep Learning Container. For more information about licensing for MathWorks Containers, see Configure License for MathWorks Containers (Licensing on Cloud Platforms).
You can obtain a trial license for products in the MATLAB Deep Learning Container at MATLAB Trial for Deep Learning on the Cloud.

Costs

You are responsible for the cost of the Amazon Web Services used when you create a cluster using this guide. Resource settings, such as instance type, affect the cost of deployment. For cost estimates, see the pricing pages for each AWS service you are using. Prices are subject to change.

Prepare Your AWS Account

If you do not have an Amazon Web Services account, create one at https://aws.amazon.com by following the on-screen instructions. Create a key pair using the Amazon EC2 Console.

Note

Make sure that you download the private key when you create a pair as it is the only way to connect to the instance as an administrator.

Launch the Docker Host Instance

On the Choose AMI page, navigate to the AWS Marketplace and search for the NVIDIA Deep Learning AMI. This Amazon Machine Image (AMI) is designed for use with NVIDIA GPU Cloud to take advantage of the Volta GPUs available in P3 instances.

Note that not all Availability Zones offer P3 instances. Your Availability Zone is defined during setup of your virtual private cloud (VPC).

On the Configure Instance, Add Storage, and Add Tags pages, configure your instance as needed.

If necessary, choose or create appropriate Security Groups for your instance on the Configure Security Group page.

When correctly configured, select the appropriate key pair option and start your instance. Make sure that you have access to your private key so you can log in to your instance.

Pull the Container

Pulling the container downloads the container image onto the Docker host instance, the machine on which the container is to be run. You have to pull the container only once per EC2 instance.

You can copy the pull command for the container image release from the NVIDIA Container Registry. 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. The command is of the form:

docker pull nvcr.io/partners/matlab:r20XYz

where the tag r20XYz must be replaced with the specific MATLAB release name, for example r2020a. Ensure the last part of the pull command matches the MATLAB release you want to use.

Connect to your instance via SSH from your client machine with your private key, using PuTTY or another SSH client. The default username is:

ubuntu@ec2-public-ipv4-address.region.amazonaws.com

You can find the public IPv4 DNS address of your instance in your Amazon Web Services console.

Paste the docker pull command into your SSH client, and run the command on your EC2 instance. You do not need to log in to the NVIDIA Container Registry to pull the container image.

Running the docker pull command downloads the MATLAB container image onto the host EC2 machine. It might take some time to download and extract the large container image.

Run the Container

Run the MATLAB Deep Learning Container using a command of the form:

docker run -it --rm -p 5901:5901 -p 6080:6080 --gpus all --shm-size=512M nvcr.io/partners/matlab:r20XYz

Ensure the last part of the run command matches the MATLAB release you want to use.

The options -p hostport:containerport map ports from inside the container to ports on the Docker host so that you can connect to the container desktop. Ports used in the container are 5901 (for VNC connection) and 6080 (for web browser connection). If you are deploying multiple containers on the same host instance, you must increment the host ports until you find a free port. For example:

-p 5902:5901 -p 6081:6080

The MATLAB Deep Learning Container is now running on your EC2 machine.

Connect Securely

You can use SSH tunneling to set up an encrypted channel between your client machine and the container session. You must do this to access the desktop of the container running in your EC2 instance.

These instructions forward a client port to a container port via the Docker host instance. After you set up an SSH tunnel, all communication between the client port and the container port is secure.

Using PuTTY on a Windows Client

If you are using PuTTY to connect to your Docker host instance, add a new forwarded port using the menu option Change Settings, accessible by clicking the title bar. In the PuTTY Reconfiguration dialog box, under Category, choose Connection > SSH > Tunnels.

To connect via VNC, set up a tunnel to the container port 5901.

In the Source port field, enter a free port on the client machine starting at 5900, for example 5901.
In the Destination field, enter the relevant host port that you connected to container port 5901 in Run the Container, for example, localhost:5901. Note that you must use localhost and not the name of the host instance.

To connect via a web browser, set up a tunnel to the container port 6080.

In the Source port field, enter a free port on the client machine, for example 6080.
In the Destination field, enter the relevant host port that you connected to container port 6080 in Run the Container, for example, localhost:6080. Note that you must use localhost and not the name of the host instance.

If you are using multiple containers or running a VNC server on the client machine, you must increment the source ports on the client machine until you find a free port, for example 5902 or 6081.

Using a Command-Line Interface

If you are using a command-line interface to SSH-tunnel to a container port, use a command of this form:

ssh -L clientport:hostport ubuntu@ec2-public-ipv4-address.region.amazonaws.com

clientport is a free port on the client machine, for example 5901 or 6080. hostport is the host port that you connected to the container port in Run the Container, for example localhost:5901 or localhost:6080. Note that you must use localhost and not the name of the host instance.

For example, use this command:

ssh -L 5901:localhost:5901 ubuntu@ec2-public-ipv4-address.region.amazonaws.com

If you are running a VNC server on the client machine, you must increment the client ports until you find a free port, for example 5902 or 6081.

Run MATLAB in the Container

There are three ways to access MATLAB in the container:

Use a web browser to connect to the container desktop and run MATLAB desktop
Use VNC to connect to the container desktop and run MATLAB desktop

Run MATLAB using the command-line interface

Connect using a Web Browser

To connect using a web browser, use the URL:

http://localhost:6080

Note that you must use localhost and not the name of the host instance.

If you incremented the client port in Connect Securely, use the appropriate host port number, for example 6081.

You will see a login screen for noVNC. Click connect. When you are prompted for a password to access the desktop, use the password:

matlab

You can run MATLAB using the desktop icon. Log in using your MathWorks^® Account.

If you cannot log in using your MathWorks Account, check that your account is connected to a license that is configured for cloud use. To check, visit License Center.

Connect using VNC

To connect via VNC, use your VNC client to connect to the appropriate display port on the client, for example:

localhost:1

Note that you must use localhost and not the name of the host instance.

If you incremented the client port in Connect Securely, use the appropriate host display port number, for example 2 for 5902.

To log in and connect to the container desktop, use the password:

matlab

You can run MATLAB using the desktop icon. Log in using your MathWorks Account.

If you cannot log in using your MathWorks Account, check that your account is connected to a license that is configured for cloud use. To check, visit License Center.

Run MATLAB Using the Command-Line Interface

You can also run MATLAB from the terminal using the command-line interface using the command:

matlab

Note that there is no graphical desktop in this case.

If you cannot log in using your MathWorks Account, check that your account is connected to a license that is configured for cloud use. To check, visit License Center.

Select GPUs for Training in the Container

MATLAB supports training a single network in parallel using multiple GPUs. To enable multi-GPU training in the MATLAB Deep Learning Container, use the trainingOptions function to set 'ExecutionEnvironment' to 'multi-gpu'.

Train your network using the trainNetwork function. MATLAB opens a parallel pool of workers on all available GPUs. To select only specific GPUs for training, you can use gpuDevice. For further information, see Select Particular GPUs to Use for Training (Deep Learning Toolbox).

To test your container, you can run the Create Simple Deep Learning Network for Classification (Deep Learning Toolbox) example. To try this example, double-click the file MNISTExample.mlx in the Current Folder pane in the MATLAB startup folder. To run this example on all available GPUs, in the trainingOptions function, set the 'ExecutionEnvironment' to 'multi-gpu'.

Import and Export Data

Importing Data from Amazon S3

If you have data stored in Amazon S3™, you can access it directly from MATLAB. For more information, see Work with Remote Data (MATLAB).

Read and Write Cloud Data

You can access data for deep learning and save trained networks in the cloud. Read and write data in Amazon EFS and Amazon EBS by connecting your storage to your EC2 instance and mounting it to your container.

First, connect the EFS or EBS to your EC2 instance. To do this, make sure that you created your EC2 and storage with the appropriate Security Group settings and access rules. For detailed instructions, see Using the Amazon Elastic File System (EFS) for Persistent Data Storage and Using the Amazon Elastic Block Storage (EBS) for Persistent Data Storage.

For example, suppose you have mounted an EFS volume into your host EC2 instance under a folder named efs. The following option mounts the EC2 source folder efs into a target folder in the container, ContainerDeepLearningData:

-v /efs:/ContainerDeepLearningData

Add the option to the docker run command in Run the Container.

You can now access and save data within the container in the folder ContainerDeepLearningData. Saved data is also available in the EFS volume. These files persist once the container is stopped. Modifying files from within the container overwrites the files in the EFS volume.

If you have several storage volumes mounted to the host EC2 instance, you can mount as many folders to the container as you need using additional -v options.

Run Options

Configure Desktop Access Password

To change the password to access the desktop in the container via VNC or from a web browser, you can use the following option:

-e PASSWORD=MyNewPassword

Add this option to the docker run command when deploying the container.

Network License Manager

If your license is administrated via a network license manager, you can use this license with the MATLAB Deep Learning container. You can point to the location of the license in the docker run command. Add the option:

-e MLM_LICENSE_FILE=27000@MyLicenseServer

Add this option to the docker run command when deploying the container.

For more information, see Configure License for MathWorks Containers (Licensing on Cloud Platforms).

Further Configuration Options

For advanced users, further configuration of the container deployment is available through additional Docker options. For more information, see the Docker documentation

Close the Container Session

To close the container session, type exit from the container terminal. The container is stopped and removed. No processes or data are saved by default when the container is closed, unless you have saved data in the cloud by mounting cloud storage, as described in Import and Export Data.

Install Updates, Toolboxes, and Add-Ons in the Container

You can install the latest MATLAB updates or install additional toolboxes and add-ons. Then, you can save the container for later use to avoid having to reinstall your changes.

To install or update MATLAB, you must run MATLAB with superuser privileges. At a terminal inside the container session, start MATLAB with root privileges using the following command.

sudo matlab

You can install updates and toolboxes from within MATLAB. For information on how to install updates, see Update an Existing Installation (MATLAB). For information on how to install toolboxes and add-ons, see Get and Manage Add-Ons (MATLAB).

Save Changes in the Container

If you make changes within the container, such as installing updates, additional toolboxes, or add-ons, you can save the container for later use. You can then deploy the updated version of the container, without needing to install the updates or toolboxes again.

Perform these steps after you have made your changes in the container and while the container is still running.

On your client machine, open another connection to the Docker host, the machine running the container, using PuTTY or the same method you used to access the Docker host and launch the container.

Obtain the container ID of the running container using the following command.

docker ps

This command displays the details of all containers currently running. Identify your MATLAB Deep Learning Container and take note of the container ID.

To save the container, use the docker commit command.

docker commit <containerID> <repository>:<tag>

The docker commit command locally saves a new container image based on the specified container ID; in this case, based on the currently running container. You can view the images available locally using the docker images command.

As an example, suppose you are using MATLAB R2020a in the MATLAB Deep Learning Container. You have updated MATLAB in the container you are running currently and you want to save it for later use.

First, find the container ID of the running container.

docker ps

CONTAINER ID        IMAGE                            COMMAND             CREATED              STATUS              PORTS                                            NAMES
3d555451f07a        nvcr.io/partners/matlab:r2020a   "/bin/run.sh"       24 minutes ago       Up 24 minutes       0.0.0.0:5901->5901/tcp, 0.0.0.0:6080->6080/tcp   relaxed_pasteur

Next, commit the container image.

docker commit 3d555451f07a mymatlab:r2020a

You can now launch the updated container image by specifying the tag of the updated container in the docker run command.

docker run -it --rm -p 5901:5901 -p 6080:6080 --gpus all --shm-size=512M mymatlab:r2020a

Additional Information

What is NVIDIA GPU Cloud?

NVIDIA GPU Cloud is a Docker repository of containers that are designed to run applications on high-performance NVIDIA GPUs.

What is a Container?

When you want to run an application or a piece of software in a reliable way in multiple different locations, you can use a container. A container is a lightweight package that contains everything required to run a specific application (or set of applications), including code, libraries, drivers, and settings. Because applications in a container always run in the same way, they are useful for managing well-defined test and deployment environments.

Unlike a virtual machine, containers share the operating system of the host machine and multiple instances of the same container share common resources. This makes them lightweight. However, containers are isolated from each other and the host operating system. That is, applications inside a container can only access files and processes provided within that container.

You can deploy multiple instances of the same container on the same or different hosts to test your applications simultaneously. To extend functionality, you can mount additional files, folders, and devices to containers.

What is in the MATLAB Deep Learning Container?

The MATLAB Deep Learning Container contains MATLAB and several other toolboxes that are useful in deep learning applications.

Computer Vision Toolbox™
GPU Coder™
Image Processing Toolbox™
MATLAB Coder™
Deep Learning Toolbox™
Parallel Computing Toolbox™
Signal Processing Toolbox™
Statistics and Machine Learning Toolbox™
Text Analytics Toolbox™

To perform deep learning using GPUs in the MATLAB Deep Learning Container, you must have a license valid for MATLAB, Deep Learning Toolbox, and Parallel Computing Toolbox. A license valid for the other products in the container is required to access the full functionality of the container.

If you do not have a license valid for Deep Learning Toolbox or Parallel Computing Toolbox, MATLAB displays a warning on startup indicating that you cannot use these products.
If you do not have a license valid for other products in the MATLAB Deep Learning Container, MATLAB displays a message on startup indicating that you cannot use these products.

You can obtain a trial license for products in the MATLAB Deep Learning Container at MATLAB Trial for Deep Learning on the Cloud.In addition, the container contains several Pretrained Deep Neural Networks (Deep Learning Toolbox).

You can import networks and network architectures into the container from TensorFlow™-Keras and Caffe, with or without layer weights. You can also convert trained networks to the Open Neural Network Exchange (ONNX) model format.

Import from Keras (Deep Learning Toolbox)
Import from Caffe (Deep Learning Toolbox)
ONNX Converter

The MATLAB Deep Learning Container also contains:

GPUBench

By deploying this software in a container, you can avoid the set-up time needed to install and configure these products. You can run multiple containers to train several networks at once or in different locations with reproducible results.

Technical Support

For technical support, comments, and queries about the MATLAB Deep Learning Container, contact cloud-support@mathworks.com.