Train a Deep Learning Model with AWS Deep Learning Containers on Amazon EC2

You need an AWS account to follow this tutorial. There is no additional charge for using AWS Deep Learning Containers with this tutorial - you pay only for the Amazon c5.large instance used in this tutorial, which will be less than $1 after following termination steps at the end of this tutorial. 

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Open the AWS Management Console, so you can keep this step-by-step guide open. When the screen loads, enter your user name and password to get started. Then type IAM in the search bar and select IAM to open the service console.

Select Users from the navigation pane on the left.

You will now add permissions to a new IAM user you created or to an existing IAM user. Select Add Permissions on the IAM user summary page.

Select Attach existing policies directly and search for ECS_FullAccess. Select the Amazon_FullAccess policy and click through to Review and Add Permissions.

On the IAM user summary page, select Add inline policy.

Select the JSON tab and paste the following policy:

{
       "Version": "2012-10-17",
       "Statement": [
              {
                     "Action": "ecr:*",
                     "Effect": "Allow",
                     "Resource": "*"
              }
       ]
}

Save this policy as ‘ECR’ and select Create Policy.

 

Return to the AWS Management Console home screen and type EC2 in the search bar and select EC2 to open the service console.

Navigate to the Amazon EC2 console again and select the Launch Instance button.

Choose the AWS Marketplace tab on the left, then search for ‘deep learning base ubuntu’. Select Deep Learning Base AMI (Ubuntu). You can also select the Deep Learning Base AMI (Amazon Linux).

Choose an Amazon EC2 instance type. Amazon Elastic Compute Cloud (EC2) is the Amazon Web Service you use to create and run virtual machines in the cloud. AWS calls these virtual machines 'instances'.

For this tutorial, we will use a c5.large instance, but you can choose additional instance types, including GPU-based instances (such as G4, G5, P3, and P4).

Select Review and Launch.

Review the details of your instance and select Launch.

Select the instance ID to view the details of your newly created Amazon EC2 on the console. 

Under the Description tab, copy your Amazon EC2 instance’s Public DNS (IPv4).

On your terminal, use the following commands to change to the directory where your security key is located, then connect to your instance using SSH.

cd /Users/<your_username>/Downloads/

chmod 0400 <your .pem filename>

ssh -L localhost:8888:localhost:8888 -i <your .pem filename> ubuntu@<your instance DNS>

Stop any ongoing system update, so we’re free to install Docker.

sudo pkill -f "apt.systemd.daily"
sudo apt install docker.io

You need to provide your AWS Access Key ID and Secret Access Key. If you don’t already have this information, you can create an Access Key ID and Secret Access Key here.

You will use the command below to log in to Amazon ECR:

sudo su – 
$(aws ecr get-login --region us-east-1 --no-include-email --registry-ids 763104351884)

Note: You need to include ‘$’ and parantheses in your command. You will see ‘Login Succeeded’ when this step concludes.

 

You will now run AWS Deep Learning Container images on your EC2 instance using the command below. This command will automatically pull the Deep Learning Container image if it doesn’t exist locally.

If using CPU instance:

docker run -it 763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:2.8.0-cpu-py39-ubuntu20.04-e3

Note: This step may take a few minutes depending on the size of the image. If you are using a GPU instance, use ‘nvidia-docker’ instead of ‘docker.’ Once this step completes successfully, you will enter a bash prompt for your container.

We will clone the Keras repository, which includes example Python scripts to train models.

git clone https://github.com/gilinachum/keras

Start training the canonical MNIST CNN model with the following command:

python keras/mnist.py

You have just successfully commenced training with your AWS Deep Learning Container.

 

On the Amazon EC2 Console, select Running Instances.

 

Select the EC2 instance you created and choose Actions > Instance State > Terminate.

You will be asked to confirm your termination. Select Yes, Terminate.

Note: This process can take several seconds to complete. Once your instance has been terminated, the Instance State will change to terminated on your EC2 Console.