Bare-Metal Workloads on AWS with EC2 Bare Metal Instances: Deploy SAP HANA or GPU-Heavy AI Workloads on Bare Metal Instances

Introduction

In the world of cloud computing, the demand for high-performance workloads such as SAP HANA and GPU-heavy AI applications has been steadily increasing. These workloads often require direct access to the underlying hardware to achieve optimal performance, which is where AWS EC2 Bare Metal Instances come into play. AWS EC2 Bare Metal Instances provide the benefits of bare metal performance while still offering the flexibility and scalability of the cloud.

In this article, we will explore how to deploy SAP HANA or GPU-heavy AI workloads on AWS EC2 Bare Metal Instances. We will cover both CLI-based and AWS Console-based approaches, including all the micro-steps required to get your workloads up and running.

What are AWS EC2 Bare Metal Instances?

AWS EC2 Bare Metal Instances are a type of Amazon EC2 instance that provides direct access to the underlying hardware. Unlike traditional EC2 instances, which are virtualized, Bare Metal Instances allow you to run your applications directly on the physical server. This eliminates the overhead of virtualization, providing you with the full performance of the hardware.

Bare Metal Instances are ideal for workloads that require:

• Direct access to hardware features, such as Intel VT-x or AMD-V.
• High-performance computing (HPC) workloads.
• Applications that require low-latency and high-throughput networking.
• Workloads that need to run in a non-virtualized environment, such as SAP HANA or GPU-heavy AI applications.

Use Cases for Bare Metal Instances

SAP HANA

SAP HANA is an in-memory, column-oriented, relational database management system that is designed to handle high volumes of data and complex queries. SAP HANA requires direct access to the underlying hardware to achieve optimal performance, making it an ideal candidate for Bare Metal Instances.

GPU-Heavy AI Workloads

AI workloads, particularly those involving deep learning, often require significant GPU resources. GPU-heavy AI workloads can benefit from the direct access to GPU hardware provided by Bare Metal Instances, allowing for faster training and inference times.

Prerequisites

Before we dive into the deployment process, there are a few prerequisites that you need to have in place:

• An AWS account with the necessary permissions to launch EC2 instances.
• An SSH key pair for accessing your EC2 instances.
• A basic understanding of AWS EC2 and the AWS CLI.
• A VPC with subnets and security groups configured to allow access to your instances.

Step 1: Launching a Bare Metal Instance

AWS Console-Based Approach

1. Log in to the AWS Management Console: Navigate to the EC2 Dashboard.
2. Launch Instance: Click on the "Launch Instance" button to start the instance creation process.
3. Choose an Amazon Machine Image (AMI): Select an AMI that is compatible with Bare Metal Instances. For SAP HANA, you can use the SAP-certified AMI provided by AWS. For GPU-heavy AI workloads, you can use an AMI that includes the necessary GPU drivers and frameworks, such as TensorFlow or PyTorch.
4. Choose an Instance Type: Select a Bare Metal Instance type. For SAP HANA, you can choose the m5.metal or m5d.metal instance types. For GPU-heavy AI workloads, you can choose the p3dn.24xlarge instance type, which includes NVIDIA Tesla V100 GPUs.
5. Configure Instance Details: Configure the instance details, such as the number of instances, network settings, and IAM roles. Ensure that you select the correct VPC and subnet.
6. Add Storage: Configure the storage for your instance. For SAP HANA, you may need to attach additional EBS volumes for data storage. For GPU-heavy AI workloads, you may want to use instance store volumes for high-performance storage.
7. Configure Security Group: Configure the security group to allow SSH access (port 22) and any other necessary ports for your application.
8. Review and Launch: Review your instance configuration and click "Launch" to start the instance.
9. Select Key Pair: Select the SSH key pair that you want to use to access your instance.
10. Launch Instance: Click "Launch Instances" to start your Bare Metal Instance.

CLI-Based Approach

1. Install and Configure AWS CLI: If you haven't already, install the AWS CLI and configure it with your credentials.
2. Launch Instance: Use the following command to launch a Bare Metal Instance:

aws ec2 run-instances \
       --image-id ami-0abcdef1234567890 \  # Replace with your AMI ID
       --instance-type m5.metal \         # Replace with your instance type
       --key-name MyKeyPair \             # Replace with your key pair name
       --subnet-id subnet-0abcdef1234567890 \  # Replace with your subnet ID
       --security-group-ids sg-0abcdef1234567890 \  # Replace with your security group ID
       --block-device-mappings '[{"DeviceName":"/dev/xvda","Ebs":{"VolumeSize":100,"VolumeType":"gp2"}}]' \
       --tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=MyBareMetalInstance}]'

Replace the placeholders with your specific values.

3. Verify Instance Launch: Use the following command to verify that your instance has been launched:

 aws ec2 describe-instances --instance-ids i-0abcdef1234567890

Replace i-0abcdef1234567890 with your instance ID.

Step 2: Configuring the Bare Metal Instance

AWS Console-Based Approach

1. Connect to Your Instance: Use SSH to connect to your Bare Metal Instance using the key pair you selected during the launch process.

 ssh -i /path/to/MyKeyPair.pem ec2-user@<instance-public-ip>

2. Update the System: Once connected, update the system packages.

sudo yum update -y

3. Install Required Software: Depending on your workload, install the necessary software. For SAP HANA, follow the SAP installation guide. For GPU-heavy AI workloads, install the necessary GPU drivers and frameworks.

   # Example for GPU-heavy AI workloads
   sudo yum install -y cuda-drivers
   sudo pip install tensorflow-gpu

CLI-Based Approach

1. Connect to Your Instance: Use SSH to connect to your Bare Metal Instance.

   ssh -i /path/to/MyKeyPair.pem ec2-user@<instance-public-ip>

2. Update the System: Update the system packages.

sudo yum update -y

3. Install Required Software: Install the necessary software for your workload.

   # Example for GPU-heavy AI workloads
   sudo yum install -y cuda-drivers
   sudo pip install tensorflow-gpu

Step 3: Deploying SAP HANA on Bare Metal Instances

AWS Console-Based Approach

1. Download SAP HANA Installation Media: Download the SAP HANA installation media from the SAP Support Portal.
2. Transfer Installation Media to Instance: Use SCP to transfer the installation media to your Bare Metal Instance.

   scp -i /path/to/MyKeyPair.pem /path/to/sap-hana-installation-media.zip ec2-user@<instance-public-ip>:/home/ec2-user/

3. Unzip Installation Media: Unzip the installation media on the instance.

   unzip sap-hana-installation-media.zip

4. Run SAP HANA Installation: Follow the SAP HANA installation guide to install and configure SAP HANA on your Bare Metal Instance.

CLI-Based Approach

1. Download SAP HANA Installation Media: Download the SAP HANA installation media from the SAP Support Portal.
2. Transfer Installation Media to Instance: Use SCP to transfer the installation media to your Bare Metal Instance.

   scp -i /path/to/MyKeyPair.pem /path/to/sap-hana-installation-media.zip ec2-user@<instance-public-ip>:/home/ec2-user/

3. Unzip Installation Media: Unzip the installation media on the instance.

   unzip sap-hana-installation-media.zip

4. Run SAP HANA Installation: Follow the SAP HANA installation guide to install and configure SAP HANA on your Bare Metal Instance.

Step 4: Deploying GPU-Heavy AI Workloads on Bare Metal Instances

AWS Console-Based Approach

1. Install GPU Drivers: Ensure that the necessary GPU drivers are installed on your Bare Metal Instance.

   sudo yum install -y cuda-drivers

2. Install AI Frameworks: Install the necessary AI frameworks, such as TensorFlow or PyTorch.

   sudo pip install tensorflow-gpu

3. Deploy AI Workload: Deploy your AI workload on the Bare Metal Instance. This could involve training a deep learning model or running inference on a pre-trained model.

CLI-Based Approach

1. Install GPU Drivers: Ensure that the necessary GPU drivers are installed on your Bare Metal Instance.

   sudo yum install -y cuda-drivers

2. Install AI Frameworks: Install the necessary AI frameworks, such as TensorFlow or PyTorch.

   sudo pip install tensorflow-gpu

3. Deploy AI Workload: Deploy your AI workload on the Bare Metal Instance. This could involve training a deep learning model or running inference on a pre-trained model.

Step 5: Monitoring and Scaling

AWS Console-Based Approach

1. Monitor Performance: Use AWS CloudWatch to monitor the performance of your Bare Metal Instance. You can set up alarms to notify you of any performance issues.
2. Scale Workloads: If your workload requires additional resources, you can scale vertically by upgrading to a larger Bare Metal Instance type or horizontally by adding more instances.

CLI-Based Approach

1. Monitor Performance: Use the following command to monitor the performance of your Bare Metal Instance:

aws cloudwatch get-metric-statistics \
       --namespace AWS/EC2 \
       --metric-name CPUUtilization \
       --dimensions Name=InstanceId,Value=i-0abcdef1234567890 \
       --start-time 2023-10-01T00:00:00Z \
       --end-time 2023-10-01T23:59:59Z \
       --period 3600 \
       --statistics Average

2. Scale Workloads: If your workload requires additional resources, you can scale vertically by upgrading to a larger Bare Metal Instance type or horizontally by adding more instances.

Conclusion

AWS EC2 Bare Metal Instances provide a powerful solution for deploying high-performance workloads such as SAP HANA and GPU-heavy AI applications. By following the steps outlined in this article, you can successfully deploy and manage these workloads on AWS, taking full advantage of the performance benefits offered by Bare Metal Instances.

Whether you prefer using the AWS Management Console or the AWS CLI, the process of launching, configuring, and managing Bare Metal Instances is straightforward and well-documented. With the right setup, you can achieve the performance and scalability needed to meet the demands of your most resource-intensive workloads.

By leveraging AWS EC2 Bare Metal Instances, you can ensure that your SAP HANA or GPU-heavy AI workloads run at peak performance, delivering the results you need to drive your business forward.