Tutorial / Cram Notes

Auto Scaling groups are collections of EC2 instances that are treated as a logical grouping for the purposes of automatic scaling and management. AWS Auto Scaling monitors your applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost.

Why Auto Scaling is Important for Machine Learning?

In the context of machine learning, workloads can be highly variable. Training models may require significant computational resources for short periods, while inference may see spikes in demand. Auto Scaling groups can help manage these fluctuations by adding or removing resources as needed.

How to Deploy Auto Scaling Groups?

Deploying Auto Scaling groups involves several steps:

  1. Create a launch template or launch configuration:
    Define the EC2 instance configuration that will be used by the Auto Scaling group. This includes the AMI, instance type, key pair, security groups, etc.
  2. Define the Auto Scaling group:
    Specify the minimum, maximum, and desired number of instances, as well as the availability zones for your instances. Attach the previously created launch template or launch configuration.
  3. Configure scaling policies:
    Establish the guidelines under which the Auto Scaling group will scale out (add more instances) or scale in (remove instances). Scaling policies can be based on criteria such as CPU utilization, network usage, or custom metrics.
  4. Set up notifications (optional):
    Create notifications to alert you when the Auto Scaling group launches or terminates instances, or when instances fail health checks.

Example Scenario:

Let’s consider deploying an Auto Scaling group for a machine learning inference endpoint:

  1. Launch Template:

{
“LaunchTemplateName”: “ML-Inference-Template”,
“VersionDescription”: “v1”,
“LaunchTemplateData”: {
“ImageId”: “ami-0abcdef1234567890”,
“InstanceType”: “ml.c5.xlarge”,
“KeyName”: “MyKeyPair”,
“SecurityGroupIds”: [“sg-123abc123abc123ab”],

}
}

  1. Auto Scaling Group:
    You would then create an Auto Scaling Group using the AWS Management Console, AWS CLI, or an Infrastructure as Code tool like AWS CloudFormation.

    If using the AWS CLI, the command might look like:

aws autoscaling create-auto-scaling-group \
–auto-scaling-group-name ML-Inference-ASG \
–launch-template “LaunchTemplateName=ML-Inference-Template” \
–min-size 2 \
–max-size 10 \
–desired-capacity 4 \
–vpc-zone-identifier “subnet-123ab45c,subnet-678de90f” \

  1. Scaling Policies:
    Define a policy that increases the capacity by adding 2 EC2 instances when the average CPU utilization exceeds 70%, and another policy that decreases the capacity by detaching 1 EC2 instance when the average CPU utilization falls below 30%.

    Here is an example of how to create a scaling policy through the AWS CLI:

aws autoscaling put-scaling-policy \
–auto-scaling-group-name ML-Inference-ASG \
–policy-name scale-out-policy \
–policy-type SimpleScaling \
–adjustment-type ChangeInCapacity \
–scaling-adjustment 2 \
–cooldown 300 \
–alarm-name add-instances \
–metric-name CPUUtilization \
–namespace AWS/EC2 \
–statistic Average \
–comparison-operator GreaterThanOrEqualToThreshold \
–threshold 70 \

Monitoring and Maintaining Auto Scaling Groups

After deployment, it’s critical to monitor the health and performance of your Auto Scaling groups. AWS provides CloudWatch metrics for Auto Scaling groups that you can use to monitor events such as:

  • EC2 instance launch or termination
  • Failed health checks
  • Scaling activities triggered by your policies

By carefully planning and managing Auto Scaling groups, you can ensure that your machine learning applications scale efficiently and cost-effectively in response to workload demands. Understanding and applying these concepts will be invaluable for success in both real-world scenarios and in the AWS Certified Machine Learning – Specialty (MLS-C01) exam.

Practice Test with Explanation

True or False: Auto Scaling groups are used in AWS to ensure a fixed number of EC2 instances are running.

  • A. True
  • B. False

Answer: B. False

Explanation: Auto Scaling groups are used to automatically adjust the number of EC2 instances in response to the demand, not to maintain a fixed number of instances.

When setting up an Auto Scaling group in AWS, which of the following parameters must be specified? (Select TWO)

  • A. Minimum number of instances
  • B. Maximum number of instances
  • C. Exact number of instances
  • D. Desired capacity of instances

Answer: A. Minimum number of instances, B. Maximum number of instances

Explanation: When setting up an Auto Scaling group, specifying the minimum and maximum number of instances is required to define the scaling boundaries.

True or False: Auto Scaling groups can scale based on schedule.

  • A. True
  • B. False

Answer: A. True

Explanation: Auto Scaling groups can scale based on specific triggers such as a schedule, giving the flexibility to scale resources as needed.

Which AWS service provides metrics that Auto Scaling can use to trigger scaling policies?

  • A. AWS Lambda
  • B. AWS CloudFormation
  • C. Amazon CloudWatch
  • D. AWS Config

Answer: C. Amazon CloudWatch

Explanation: Amazon CloudWatch provides the monitoring metrics that Auto Scaling can use to decide when to scale in or out.

True or False: Auto Scaling groups can only use launch configurations and not launch templates.

  • A. True
  • B. False

Answer: B. False

Explanation: Auto Scaling groups can use both launch configurations and launch templates to define the settings for the EC2 instances.

When creating an Auto Scaling group, which of the following is an optional attribute?

  • A. VPC selection
  • B. Scaling policies
  • C. Health check type
  • D. Instance type

Answer: B. Scaling policies

Explanation: Scaling policies are optional when creating an Auto Scaling group. You can have an Auto Scaling group without specific scaling policies and add them later.

True or False: Auto Scaling allows automatic updates to instances in the group when the launch configuration or template is updated.

  • A. True
  • B. False

Answer: B. False

Explanation: Auto Scaling does not automatically update existing instances with changes in launch configurations or templates. New instances will use the updated configuration, but existing ones will not unless specifically replaced.

An Auto Scaling group can be attached to which AWS load balancer types? (Select TWO)

  • A. Classic Load Balancer
  • B. AWS App Mesh
  • C. Network Load Balancer
  • D. Gateway Load Balancer

Answer: A. Classic Load Balancer, C. Network Load Balancer

Explanation: Auto Scaling groups can be attached to Classic Load Balancers and Network Load Balancers to distribute incoming application traffic across the multiple instances.

True or False: Instances launched by Auto Scaling cannot be assigned public IP addresses.

  • A. True
  • B. False

Answer: B. False

Explanation: Instances launched by an Auto Scaling group can be assigned public IP addresses if the subnet they are in is configured to assign a public IP to instances.

The cooldown period in an Auto Scaling group…

  • A. Prevents scaling activities from disrupting the application performance
  • B. Is the period after a scaling activity during which no further scaling activities can be started
  • C. Both A and B
  • D. Is configurable by users only when creating the Auto Scaling group

Answer: C. Both A and B

Explanation: The cooldown period is a configurable setting that ensures that the Auto Scaling group does not trigger another scaling activity immediately after an ongoing one, preventing rapid, frequent changes that could destabilize performance.

True or False: When an Auto Scaling group scales in, it terminates instances that are closest to the next billing hour by default.

  • A. True
  • B. False

Answer: B. False

Explanation: By default, when an Auto Scaling group scales in, it uses a default termination policy that balances the number of instances across Availability Zones and terminates instances that are excess capacity.

Auto Scaling group termination policies can be:

  • A. Set to terminate instances at random
  • B. Configured to prioritize instance termination based on specific criteria
  • C. Not changed once an Auto Scaling group has been created
  • D. Both A and B

Answer: D. Both A and B

Explanation: AWS Auto Scaling allows for the customization of termination policies to control which instances are terminated during a scale-in event. This can include random termination or termination based on specific criteria. They can also be changed after the Auto Scaling group has been created.

Interview Questions

Describe what an Auto Scaling group is and how it can be beneficial in a Machine Learning workflow on AWS.

An Auto Scaling group allows you to automatically scale your EC2 instances up or down according to conditions you define. In a Machine Learning workflow on AWS, it can be beneficial to ensure that the computational power scales with the workload’s demands, such as varying inference loads or changing training batch sizes, resulting in cost efficiency and performance optimization.

What are the basic components required to set up an Auto Scaling group in AWS?

The basic components required to set up an Auto Scaling group in AWS are a launch configuration or launch template, minimum and maximum number of instances, desired capacity, and scaling policies. A launch configuration/template defines the instance configuration, while the policies dictate how the group should scale in response to demand.

How do scaling policies in AWS Auto Scaling groups work?

Scaling policies in AWS Auto Scaling groups define when and how the group should scale out (add instances) or scale in (remove instances). There are different types of policies, such as target tracking scaling, step scaling, and simple scaling policies. These policies adjust the number of EC2 instances in response to metrics (like CPU utilization) crossing certain thresholds or on a schedule.

Can you explain the role of Amazon Machine Learning-based predictions in configuring Auto Scaling for Machine Learning workloads?

Amazon Machine Learning-based predictions can be used to forecast demand or workload patterns and inform Auto Scaling policies. By analyzing historical data, Machine Learning models can predict future usage trends, allowing you to proactively adjust the Auto Scaling group’s desired capacity in anticipation of changing workloads, leading to more efficient resource management.

What is the difference between dynamic scaling and predictive scaling in AWS Auto Scaling groups?

Dynamic scaling responds to real-time changes in demand by adjusting the number of EC2 instances, while predictive scaling analyzes historical data to predict future demands and schedule the right number of instances in advance. Predictive scaling can be more efficient as it anticipates changes rather than reacting to them.

Can Auto Scaling groups span multiple Availability Zones? If so, what is the advantage of doing so?

Yes, Auto Scaling groups can span multiple Availability Zones. The advantage is increased fault tolerance and high availability, as the application can remain operational even if one Availability Zone becomes unavailable. It also allows for more equal distribution of instances within a region.

Explain what a cooldown period is in the context of AWS Auto Scaling.

A cooldown period is the time after a scale-out operation during which the Auto Scaling group does not initiate further scale-out activities. This allows the new instances to start handling traffic and stabilize before additional scaling decisions are made, preventing unnecessary oscillations in the number of instances.

How can you integrate AWS Auto Scaling with other AWS services for Machine Learning workloads?

AWS Auto Scaling can be integrated with AWS CloudWatch for monitoring metrics, AWS SNS for notifications, AWS Lambda for invoking functions in response to scaling events, and Amazon EC2 Spot Instances to leverage cost efficiencies. It can also interface with Amazon SageMaker for optimizing resource usage during Machine Learning model training and deployment.

What factors should you consider when choosing between using On-Demand, Reserved, and Spot Instances in your Auto Scaling group for Machine Learning purposes?

When choosing between these instance types, you should consider cost, availability, and workload flexibility. On-Demand Instances are best for short-term and irregular workloads without commitment, Reserved Instances offer a significant discount for a specified term commitment, and Spot Instances are ideal for flexible workloads where the application can handle interruptions and take advantage of the cost savings.

How can you ensure that the scaling of EC2 instances within an Auto Scaling group doesn’t impact the performance of a Machine Learning model’s inference service?

To minimize the impact, you can implement instance warm-up time within scaling policies, use Elastic Load Balancing to evenly distribute the load, and gradually increase capacity in small increments. Additionally, you may use lifecycle hooks to delay putting instances into service until they have fully loaded required models and data.

If a Machine Learning workload must comply with data residency requirements, how does this affect the configuration of Auto Scaling groups?

Data residency requirements would constrain the Auto Scaling group to specific Geographic locations and Availability Zones within AWS Regions that comply with the necessary legal or regulatory standards. It might also necessitate the use of Amazon EC2 placement groups to ensure instances are physically located within a specific area.

In terms of cost optimization, how does Auto Scaling help reduce expenses for Machine Learning projects on AWS?

Auto Scaling helps to match resource supply to workload demands precisely, adding instances only when necessary and removing them during times of low demand. This ensures that you avoid paying for idle resources. Combining Auto Scaling with different instance purchasing options (like Spot Instances) can further increase cost efficiency.

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Dina Frenz
6 months ago

Great blog! The explanation on setting up Auto Scaling groups was super clear.

Rosângela da Rosa
7 months ago

I have a question regarding the scaling policies. Can anyone explain the difference between step scaling and simple scaling in AWS?

Sara Olsen
6 months ago

Can you use target tracking scaling policies for custom metrics?

Hotze Matthijs
6 months ago

This blog is exactly what I was looking for, thanks!

Nash Steenkamp
6 months ago

Can anyone share their experience with predictive scaling? Has it been effective?

Volkan Öztonga
6 months ago

Appreciate the detailed steps on configuring Auto Scaling groups.

Ülkü Abacı
7 months ago

Can Auto Scaling groups be used with spot instances?

Caroline Ortiz
5 months ago

Is there a way to prevent flapping in Auto Scaling groups?

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