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^® GPU enabled 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

To use the MATLAB Deep Learning Container, you need:

Amazon^® Web Services account.
A MATLAB license that meets the following conditions:
- Valid for all the MathWorks^® products installed in the container. You can obtain a trial license for products in the MATLAB Deep Learning Container at MATLAB Trial for Deep Learning on the Cloud.
- Current on Software Maintenance Service (SMS).
- Linked to a MathWorks Account.
- Configured for cloud use. Individual and Campus-Wide licenses are already configured. 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 have a Concurrent license type, you must supply the port number and DNS address of the network license manager when you run the container. Add an option of the following form to the docker run command when you start the container:
```
-e MLM_LICENSE_FILE=27000@MyLicenseServer
```

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.

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, first set up a tunnel to the container port 6080 (default noVNC port). For more information on how to set up an SSH tunnel, see Create Encrypted Connection to Remote Applications and Containers. Then, use a URL like the one below to connect to the appropriate port:

http://localhost:6080

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

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, first set up a tunnel to the container port 5901 (default VNC port). For more information on how to set up an SSH tunnel, see Create Encrypted Connection to Remote Applications and Containers. Then, use your VNC client to connect to the appropriate display port on the client:

localhost:1

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

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.

Test Container Using Deep Learning Example

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'.

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 Share Data with Containers.

Additional Information

How do I Configure the MATLAB Deep Learning Container?

You can configure and customize the behavior of a MathWorks container by setting specific environment variables. For more information, see Configure Containers.

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 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 and Export Networks (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.