Amazon EC2 Auto Scaling Construct Library

This module is part of the AWS Cloud Development Kit project.

Auto Scaling Group

An AutoScalingGroup represents a number of instances on which you run your code. You pick the size of the fleet, the instance type and the OS image:

# vpc: ec2.Vpc


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=ec2.InstanceType.of(ec2.InstanceClass.BURSTABLE2, ec2.InstanceSize.MICRO),

    # The latest Amazon Linux 2 image
    machine_image=ec2.MachineImage.latest_amazon_linux2()
)

Creating an AutoScalingGroup from a Launch Configuration has been deprecated. All new accounts created after December 31, 2023 will no longer be able to create Launch Configurations. With the @aws-cdk/aws-autoscaling:generateLaunchTemplateInsteadOfLaunchConfig feature flag set to true, AutoScalingGroup properties used to create a Launch Configuration will now be used to create a LaunchTemplate using a Launch Configuration to LaunchTemplate mapping. Specifically, the following AutoScalingGroup properties will be used to generate a LaunchTemplate:

machineImage
keyName (deprecated, prefer keyPair)
keyPair
instanceType
instanceMonitoring
securityGroup
role
userData
associatePublicIpAddress
spotPrice
blockDevices

After the Launch Configuration is replaced with a LaunchTemplate, any new instances launched by the AutoScalingGroup will use the new LaunchTemplate. Existing instances are not affected. To update an existing instance, you can allow the AutoScalingGroup to gradually replace existing instances with new instances based on the terminationPolicies for the AutoScalingGroup. Alternatively, you can terminate them yourself and force the AutoScalingGroup to launch new instances to maintain the desiredCapacity.

Support for creating an AutoScalingGroup from a LaunchTemplate was added in CDK version 2.21.0. Users on a CDK version earlier than version 2.21.0 that need to create an AutoScalingGroup with an account created after December 31, 2023 must update their CDK version to 2.21.0 or later. Users on CDK versions 2.21.0 up to, but not including 2.86.0, must use a manually created LaunchTemplate to create an AutoScalingGroup for accounts created after December 31, 2023. CDK version 2.86.0 or later will automatically generate a LaunchTemplate using the AutoScalingGroup properties mentioned above.

For additional migration information, please see: Migrating to a LaunchTemplate from a Launch Configuration

NOTE: AutoScalingGroup has a property called allowAllOutbound (allowing the instances to contact the internet) which is set to true by default. Be sure to set this to false if you don’t want your instances to be able to start arbitrary connections. Alternatively, you can specify an existing security group to attach to the instances that are launched, rather than have the group create a new one.

# vpc: ec2.Vpc


my_security_group = ec2.SecurityGroup(self, "SecurityGroup", vpc=vpc)
autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=ec2.InstanceType.of(ec2.InstanceClass.BURSTABLE2, ec2.InstanceSize.MICRO),
    machine_image=ec2.MachineImage.latest_amazon_linux2(),
    security_group=my_security_group
)

Alternatively, to enable more advanced features, you can create an AutoScalingGroup from a supplied LaunchTemplate:

# vpc: ec2.Vpc
# launch_template: ec2.LaunchTemplate


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    launch_template=launch_template
)

To launch a mixture of Spot and on-demand instances, and/or with multiple instance types, you can create an AutoScalingGroup from a MixedInstancesPolicy:

# vpc: ec2.Vpc
# launch_template1: ec2.LaunchTemplate
# launch_template2: ec2.LaunchTemplate


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    mixed_instances_policy=autoscaling.MixedInstancesPolicy(
        instances_distribution=autoscaling.InstancesDistribution(
            on_demand_percentage_above_base_capacity=50
        ),
        launch_template=launch_template1,
        launch_template_overrides=[autoscaling.LaunchTemplateOverrides(instance_type=ec2.InstanceType("t3.micro")), autoscaling.LaunchTemplateOverrides(instance_type=ec2.InstanceType("t3a.micro")), autoscaling.LaunchTemplateOverrides(instance_type=ec2.InstanceType("t4g.micro"), launch_template=launch_template2)]
    )
)

You can specify instances requirements with the instanceRequirements property:

# vpc: ec2.Vpc
# launch_template1: ec2.LaunchTemplate


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    mixed_instances_policy=autoscaling.MixedInstancesPolicy(
        launch_template=launch_template1,
        launch_template_overrides=[autoscaling.LaunchTemplateOverrides(
            instance_requirements=autoscaling.CfnAutoScalingGroup.InstanceRequirementsProperty(
                v_cpu_count=autoscaling.CfnAutoScalingGroup.VCpuCountRequestProperty(min=4, max=8),
                memory_mi_b=autoscaling.CfnAutoScalingGroup.MemoryMiBRequestProperty(min=16384),
                cpu_manufacturers=["intel"]
            )
        )
        ]
    )
)

Machine Images (AMIs)

AMIs control the OS that gets launched when you start your EC2 instance. The EC2 library contains constructs to select the AMI you want to use.

Depending on the type of AMI, you select it a different way.

The latest version of Amazon Linux and Microsoft Windows images are selectable by instantiating one of these classes:

# Pick a Windows edition to use
windows = ec2.WindowsImage(ec2.WindowsVersion.WINDOWS_SERVER_2019_ENGLISH_FULL_BASE)

# Pick the right Amazon Linux edition. All arguments shown are optional
# and will default to these values when omitted.
amzn_linux = ec2.AmazonLinuxImage(
    generation=ec2.AmazonLinuxGeneration.AMAZON_LINUX,
    edition=ec2.AmazonLinuxEdition.STANDARD,
    virtualization=ec2.AmazonLinuxVirt.HVM,
    storage=ec2.AmazonLinuxStorage.GENERAL_PURPOSE
)

# For other custom (Linux) images, instantiate a `GenericLinuxImage` with
# a map giving the AMI to in for each region:

linux = ec2.GenericLinuxImage({
    "us-east-1": "ami-97785bed",
    "eu-west-1": "ami-12345678"
})

NOTE: The Amazon Linux images selected will be cached in your cdk.json, so that your AutoScalingGroups don’t automatically change out from under you when you’re making unrelated changes. To update to the latest version of Amazon Linux, remove the cache entry from the context section of your cdk.json.

We will add command-line options to make this step easier in the future.

AutoScaling Instance Counts

AutoScalingGroups make it possible to raise and lower the number of instances in the group, in response to (or in advance of) changes in workload.

When you create your AutoScalingGroup, you specify a minCapacity and a maxCapacity. AutoScaling policies that respond to metrics will never go higher or lower than the indicated capacity (but scheduled scaling actions might, see below).

There are three ways to scale your capacity:

In response to a metric (also known as step scaling); for example, you might want to scale out if the CPU usage across your cluster starts to rise, and scale in when it drops again.
By trying to keep a certain metric around a given value (also known as target tracking scaling); you might want to automatically scale out and in to keep your CPU usage around 50%.
On a schedule; you might want to organize your scaling around traffic flows you expect, by scaling out in the morning and scaling in in the evening.

The general pattern of autoscaling will look like this:

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


auto_scaling_group = autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    min_capacity=5,
    max_capacity=100
)

Step Scaling

This type of scaling scales in and out in deterministics steps that you configure, in response to metric values. For example, your scaling strategy to scale in response to a metric that represents your average worker pool usage might look like this:

 Scaling        -1          (no change)          +1       +3
            │        │                       │        │        │
            ├────────┼───────────────────────┼────────┼────────┤
            │        │                       │        │        │
Worker use  0%      10%                     50%       70%     100%

(Note that this is not necessarily a recommended scaling strategy, but it’s a possible one. You will have to determine what thresholds are right for you).

Note that in order to set up this scaling strategy, you will have to emit a metric representing your worker utilization from your instances. After that, you would configure the scaling something like this:

# auto_scaling_group: autoscaling.AutoScalingGroup


worker_utilization_metric = cloudwatch.Metric(
    namespace="MyService",
    metric_name="WorkerUtilization"
)

auto_scaling_group.scale_on_metric("ScaleToCPU",
    metric=worker_utilization_metric,
    scaling_steps=[autoscaling.ScalingInterval(upper=10, change=-1), autoscaling.ScalingInterval(lower=50, change=+1), autoscaling.ScalingInterval(lower=70, change=+3)
    ],
    evaluation_periods=10,
    datapoints_to_alarm=5,

    # Change this to AdjustmentType.PERCENT_CHANGE_IN_CAPACITY to interpret the
    # 'change' numbers before as percentages instead of capacity counts.
    adjustment_type=autoscaling.AdjustmentType.CHANGE_IN_CAPACITY
)

The AutoScaling construct library will create the required CloudWatch alarms and AutoScaling policies for you.

Target Tracking Scaling

This type of scaling scales in and out in order to keep a metric around a value you prefer. There are four types of predefined metrics you can track, or you can choose to track a custom metric. If you do choose to track a custom metric, be aware that the metric has to represent instance utilization in some way (AutoScaling will scale out if the metric is higher than the target, and scale in if the metric is lower than the target).

If you configure multiple target tracking policies, AutoScaling will use the one that yields the highest capacity.

The following example scales to keep the CPU usage of your instances around 50% utilization:

# auto_scaling_group: autoscaling.AutoScalingGroup


auto_scaling_group.scale_on_cpu_utilization("KeepSpareCPU",
    target_utilization_percent=50
)

To scale on average network traffic in and out of your instances:

# auto_scaling_group: autoscaling.AutoScalingGroup


auto_scaling_group.scale_on_incoming_bytes("LimitIngressPerInstance",
    target_bytes_per_second=10 * 1024 * 1024
)
auto_scaling_group.scale_on_outgoing_bytes("LimitEgressPerInstance",
    target_bytes_per_second=10 * 1024 * 1024
)

To scale on the average request count per instance (only works for AutoScalingGroups that have been attached to Application Load Balancers):

# auto_scaling_group: autoscaling.AutoScalingGroup


auto_scaling_group.scale_on_request_count("LimitRPS",
    target_requests_per_second=1000
)

Scheduled Scaling

This type of scaling is used to change capacities based on time. It works by changing minCapacity, maxCapacity and desiredCapacity of the AutoScalingGroup, and so can be used for two purposes:

Scale in and out on a schedule by setting the minCapacity high or the maxCapacity low.
Still allow the regular scaling actions to do their job, but restrict the range they can scale over (by setting both minCapacity and maxCapacity but changing their range over time).

A schedule is expressed as a cron expression. The Schedule class has a cron method to help build cron expressions.

The following example scales the fleet out in the morning, going back to natural scaling (all the way down to 1 instance if necessary) at night:

# auto_scaling_group: autoscaling.AutoScalingGroup


auto_scaling_group.scale_on_schedule("PrescaleInTheMorning",
    schedule=autoscaling.Schedule.cron(hour="8", minute="0"),
    min_capacity=20
)

auto_scaling_group.scale_on_schedule("AllowDownscalingAtNight",
    schedule=autoscaling.Schedule.cron(hour="20", minute="0"),
    min_capacity=1
)

Instance Maintenance Policy

You can configure an instance maintenance policy for your Auto Scaling group to meet specific capacity requirements during events that cause instances to be replaced, such as an instance refresh or the health check process.

For example, suppose you have an Auto Scaling group that has a small number of instances. You want to avoid the potential disruptions from terminating and then replacing an instance when health checks indicate an impaired instance. With an instance maintenance policy, you can make sure that Amazon EC2 Auto Scaling first launches a new instance and then waits for it to be fully ready before terminating the unhealthy instance.

An instance maintenance policy also helps you minimize any potential disruptions in cases where multiple instances are replaced at the same time. You set the minHealthyPercentage and the maxHealthyPercentage for the policy, and your Auto Scaling group can only increase and decrease capacity within that minimum-maximum range when replacing instances. A larger range increases the number of instances that can be replaced at the same time.

# vpc: ec2.Vpc


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=ec2.InstanceType.of(ec2.InstanceClass.BURSTABLE2, ec2.InstanceSize.MICRO),
    machine_image=ec2.MachineImage.latest_amazon_linux2(),
    max_healthy_percentage=200,
    min_healthy_percentage=100
)

Visit Instance maintenance policies for more details.

Block Devices

This type specifies how block devices are exposed to the instance. You can specify virtual devices and EBS volumes.

GP3 Volumes

You can only specify the throughput on GP3 volumes.

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


auto_scaling_group = autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,
    block_devices=[autoscaling.BlockDevice(
        device_name="gp3-volume",
        volume=autoscaling.BlockDeviceVolume.ebs(15,
            volume_type=autoscaling.EbsDeviceVolumeType.GP3,
            throughput=125
        )
    )
    ]
)

Configuring Instances using CloudFormation Init

It is possible to use the CloudFormation Init mechanism to configure the instances in the AutoScalingGroup. You can write files to it, run commands, start services, etc. See the documentation of AWS::CloudFormation::Init and the documentation of CDK’s aws-ec2 library for more information.

When you specify a CloudFormation Init configuration for an AutoScalingGroup:

you must also specify signals to configure how long CloudFormation should wait for the instances to successfully configure themselves.
you should also specify an updatePolicy to configure how instances should be updated when the AutoScalingGroup is updated (for example, when the AMI is updated). If you don’t specify an update policy, a rolling update is chosen by default.

Here’s an example of using CloudFormation Init to write a file to the instance hosts on startup:

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    init=ec2.CloudFormationInit.from_elements(
        ec2.InitFile.from_string("/etc/my_instance", "This got written during instance startup")),
    signals=autoscaling.Signals.wait_for_all(
        timeout=Duration.minutes(10)
    )
)

Signals

In normal operation, CloudFormation will send a Create or Update command to an AutoScalingGroup and proceed with the rest of the deployment without waiting for the instances in the AutoScalingGroup.

Configure signals to tell CloudFormation to wait for a specific number of instances in the AutoScalingGroup to have been started (or failed to start) before moving on. An instance is supposed to execute the cfn-signal program as part of its startup to indicate whether it was started successfully or not.

If you use CloudFormation Init support (described in the previous section), the appropriate call to cfn-signal is automatically added to the AutoScalingGroup’s UserData. If you don’t use the signals directly, you are responsible for adding such a call yourself.

The following type of Signals are available:

Signals.waitForAll([options]): wait for all of desiredCapacity amount of instances to have started (recommended).
Signals.waitForMinCapacity([options]): wait for a minCapacity amount of instances to have started (use this if waiting for all instances takes too long and you are happy with a minimum count of healthy hosts).
Signals.waitForCount(count, [options]): wait for a specific amount of instances to have started.

There are two options you can configure:

timeout: maximum time a host startup is allowed to take. If a host does not report success within this time, it is considered a failure. Default is 5 minutes.
minSuccessPercentage: percentage of hosts that needs to be healthy in order for the update to succeed. If you set this value lower than 100, some percentage of hosts may report failure, while still considering the deployment a success. Default is 100%.

Update Policy

The update policy describes what should happen to running instances when the definition of the AutoScalingGroup is changed. For example, if you add a command to the UserData of an AutoScalingGroup, do the existing instances get replaced with new instances that have executed the new UserData? Or do the “old” instances just keep on running?

It is recommended to always use an update policy, otherwise the current state of your instances also depends the previous state of your instances, rather than just on your source code. This degrades the reproducibility of your deployments.

The following update policies are available:

UpdatePolicy.none(): leave existing instances alone (not recommended).
UpdatePolicy.rollingUpdate([options]): progressively replace the existing instances with new instances, in small batches. At any point in time, roughly the same amount of total instances will be running. If the deployment needs to be rolled back, the fresh instances will be replaced with the “old” configuration again.
UpdatePolicy.replacingUpdate([options]): build a completely fresh copy of the new AutoScalingGroup next to the old one. Once the AutoScalingGroup has been successfully created (and the instances started, if signals is configured on the AutoScalingGroup), the old AutoScalingGroup is deleted. If the deployment needs to be rolled back, the new AutoScalingGroup is deleted and the old one is left unchanged.

Allowing Connections

See the documentation of the aws-cdk-lib/aws-ec2 package for more information about allowing connections between resources backed by instances.

Max Instance Lifetime

To enable the max instance lifetime support, specify maxInstanceLifetime property for the AutoscalingGroup resource. The value must be between 7 and 365 days(inclusive). To clear a previously set value, leave this property undefined.

Instance Monitoring

To disable detailed instance monitoring, specify instanceMonitoring property for the AutoscalingGroup resource as Monitoring.BASIC. Otherwise detailed monitoring will be enabled.

Monitoring Group Metrics

Group metrics are used to monitor group level properties; they describe the group rather than any of its instances (e.g GroupMaxSize, the group maximum size). To enable group metrics monitoring, use the groupMetrics property. All group metrics are reported in a granularity of 1 minute at no additional charge.

See EC2 docs for a list of all available group metrics.

To enable group metrics monitoring using the groupMetrics property:

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


# Enable monitoring of all group metrics
autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    group_metrics=[autoscaling.GroupMetrics.all()]
)

# Enable monitoring for a subset of group metrics
autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    group_metrics=[autoscaling.GroupMetrics(autoscaling.GroupMetric.MIN_SIZE, autoscaling.GroupMetric.MAX_SIZE)]
)

Termination policies

Auto Scaling uses termination policies to determine which instances it terminates first during scale-in events. You can specify one or more termination policies with the terminationPolicies property:

Custom termination policy with lambda can be used to determine which instances to terminate based on custom logic. The custom termination policy can be specified using TerminationPolicy.CUSTOM_LAMBDA_FUNCTION. If this is specified, you must also supply a value of lambda arn in the terminationPolicyCustomLambdaFunctionArn property and attach necessary permission to invoke the lambda function.

If there are multiple termination policies specified, custom termination policy with lambda TerminationPolicy.CUSTOM_LAMBDA_FUNCTION must be specified first.

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage
# arn: str


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    termination_policies=[autoscaling.TerminationPolicy.CUSTOM_LAMBDA_FUNCTION, autoscaling.TerminationPolicy.OLDEST_INSTANCE, autoscaling.TerminationPolicy.DEFAULT
    ],

    # terminationPolicyCustomLambdaFunctionArn property must be specified if the TerminationPolicy.CUSTOM_LAMBDA_FUNCTION is used
    termination_policy_custom_lambda_function_arn=arn
)

Protecting new instances from being terminated on scale-in

By default, Auto Scaling can terminate an instance at any time after launch when scaling in an Auto Scaling Group, subject to the group’s termination policy.

However, you may wish to protect newly-launched instances from being scaled in if they are going to run critical applications that should not be prematurely terminated. EC2 Capacity Providers for Amazon ECS requires this attribute be set to true.

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    new_instances_protected_from_scale_in=True
)

Configuring Capacity Rebalancing

Indicates whether Capacity Rebalancing is enabled. Otherwise, Capacity Rebalancing is disabled. When you turn on Capacity Rebalancing, Amazon EC2 Auto Scaling attempts to launch a Spot Instance whenever Amazon EC2 notifies that a Spot Instance is at an elevated risk of interruption. After launching a new instance, it then terminates an old instance. For more information, see Use Capacity Rebalancing to handle Amazon EC2 Spot Interruptions in the in the Amazon EC2 Auto Scaling User Guide.

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    capacity_rebalance=True
)

Connecting to your instances using SSM Session Manager

SSM Session Manager makes it possible to connect to your instances from the AWS Console, without preparing SSH keys.

To do so, you need to:

Use an image with SSM agent installed and configured. Many images come with SSM Agent preinstalled, otherwise you may need to manually put instructions to install SSM Agent into your instance’s UserData or use EC2 Init).
Create the AutoScalingGroup with ssmSessionPermissions: true.

If these conditions are met, you can connect to the instance from the EC2 Console. Example:

# vpc: ec2.Vpc


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=ec2.InstanceType.of(ec2.InstanceClass.T3, ec2.InstanceSize.MICRO),

    # Amazon Linux 2 comes with SSM Agent by default
    machine_image=ec2.MachineImage.latest_amazon_linux2(),

    # Turn on SSM
    ssm_session_permissions=True
)

Configuring Instance Metadata Service (IMDS)

Toggling IMDSv1

You can configure EC2 Instance Metadata Service options to either allow both IMDSv1 and IMDSv2 or enforce IMDSv2 when interacting with the IMDS.

To do this for a single AutoScalingGroup, you can use set the requireImdsv2 property. The example below demonstrates IMDSv2 being required on a single AutoScalingGroup:

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    require_imdsv2=True
)

You can also use AutoScalingGroupRequireImdsv2Aspect to apply the operation to multiple AutoScalingGroups. The example below demonstrates the AutoScalingGroupRequireImdsv2Aspect being used to require IMDSv2 for all AutoScalingGroups in a stack:

aspect = autoscaling.AutoScalingGroupRequireImdsv2Aspect()

Aspects.of(self).add(aspect)

Warm Pool

Auto Scaling offers a warm pool which gives an ability to decrease latency for applications that have exceptionally long boot times. You can create a warm pool with default parameters as below:

# auto_scaling_group: autoscaling.AutoScalingGroup

auto_scaling_group.add_warm_pool()

You can also customize a warm pool by configuring parameters:

# auto_scaling_group: autoscaling.AutoScalingGroup


auto_scaling_group.add_warm_pool(
    min_size=1,
    reuse_on_scale_in=True
)

Default Instance Warming

You can use the default instance warmup feature to improve the Amazon CloudWatch metrics used for dynamic scaling. When default instance warmup is not enabled, each instance starts contributing usage data to the aggregated metrics as soon as the instance reaches the InService state. However, if you enable default instance warmup, this lets your instances finish warming up before they contribute the usage data.

To optimize the performance of scaling policies that scale continuously, such as target tracking and step scaling policies, we strongly recommend that you enable the default instance warmup, even if its value is set to 0 seconds.

To set up Default Instance Warming for an autoscaling group, simply pass it in as a prop

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    default_instance_warmup=Duration.seconds(5)
)

Configuring KeyPair for instances

You can use a keyPair to build your asg when you decide not to use a ready-made LanchTemplate.

To configure KeyPair for an autoscaling group, pass the keyPair as a prop:

# vpc: ec2.Vpc
# instance_type: ec2.InstanceType
# machine_image: ec2.IMachineImage


my_key_pair = ec2.KeyPair(self, "MyKeyPair")

autoscaling.AutoScalingGroup(self, "ASG",
    vpc=vpc,
    instance_type=instance_type,
    machine_image=machine_image,

    # ...

    key_pair=my_key_pair
)

Future work

[ ] CloudWatch Events (impossible to add currently as the AutoScalingGroup ARN is necessary to make this rule and this cannot be accessed from CloudFormation).