In this step-by-step tutorial, you'll learn how to launch an AWS Deep Learning AMI. The AMIs are machine images loaded with deep learning frameworks that make it simple to get started with deep learning in minutes.

Using the AMI, you can train custom models, experiment with new algorithms, and learn new deep learning skills and techniques. The AMIs come with pre-installed open source deep learning frameworks including TensorFlow, Apache MXNet, PyTorch, Chainer, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, and Keras, optimized for high performance on Amazon EC2 instances. The AMIs also offer GPU and CPU-acceleration through pre-configured drivers, and come with popular Python packages.

In the next few minutes, you will launch an EC2 instance using a Deep Learning AMI, connect to the instance via SSH, and access a Jupyter Notebook from your workstation.

The cost of doing this tutorial is the charge for the underlying EC2 instance. We recommend using an EC2 C5.large instance which will generate a charge of less than $0.13 per hour until you terminate it. This tutorial will instruct you how to terminate the instance to avoid unnecessary charges.

Sign into the AWS Management Console with your user name and password to get started. Then type EC2 in the search bar and open the EC2 service console.

Step 2a: Choose the Launch Instance button

Step 2b: Select a AWS Deep Learning AMI

Choose the AWS Marketplace tab on the left, and then search for deep learning ubuntu. Select the Deep Learning AMI (Ubuntu). You can also select the Base AMI to set up custom builds of deep learning frameworks. Both AMIs are available for Ubuntu or Amazon Linux.

Step 2c: On the details page, choose Continue.

Step 2d: Select an instance type.

Choose an instance type for your deep learning training and deployment needs, and then click Review and Launch. Here we have used a c5.large instance, but you can choose from additional instance types including GPU-based P3 instances.

Step 2e: Launch your instance.

Choose Launch on the Review page.

Step 2f: Create a private key file by selecting Create a new key pair, and download it to a safe location. Then launch your instance.

Step 2g: Click View Instance to see your instance status.

Step 2h: Find your instance’s public DNS and copy it.

Step 3a: Open your command line terminal.

In the terminal, use the following commands to change to the directory where your security key is located, then connect to your instance using SSH. (NOTE: Replace text below in bold)

Step 3b: In the terminal, use the command: jupyter notebook. Then copy the URL indicated.

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Open a browser window and navigate to the URL indicated in the last step. Choose New and start a new notebook using any popular deep learning framework including TensorFlow, Apache MXNet and Gluon, Torch, PyTorch, Caffe, Caffe2, Keras, Theano, Microsoft Cognitive Toolkit, and Chainer.

Step 5a: Back on the EC2 Console, select the box next to the instance you created. Then click the Actions button, navigate to Instance State, and click Terminate.

Step 5b: You will be asked to confirm your termination - 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.

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