batch_create_compute_environment: Creates an AWS Batch compute environment

Description Usage Arguments Value Request syntax Examples

View source: R/batch_operations.R

Description

Creates an AWS Batch compute environment. You can create MANAGED or UNMANAGED compute environments. MANAGED compute environments can use Amazon EC2 or AWS Fargate resources. UNMANAGED compute environments can only use EC2 resources.

In a managed compute environment, AWS Batch manages the capacity and instance types of the compute resources within the environment. This is based on the compute resource specification that you define or the launch template that you specify when you create the compute environment. You can choose either to use EC2 On-Demand Instances and EC2 Spot Instances, or to use Fargate and Fargate Spot capacity in your managed compute environment. You can optionally set a maximum price so that Spot Instances only launch when the Spot Instance price is less than a specified percentage of the On-Demand price.

Multi-node parallel jobs are not supported on Spot Instances.

In an unmanaged compute environment, you can manage your own EC2 compute resources and have a lot of flexibility with how you configure your compute resources. For example, you can use custom AMI. However, you need to verify that your AMI meets the Amazon ECS container instance AMI specification. For more information, see container instance AMIs in the Amazon Elastic Container Service Developer Guide. After you have created your unmanaged compute environment, you can use the describe_compute_environments operation to find the Amazon ECS cluster that's associated with it. Then, manually launch your container instances into that Amazon ECS cluster. For more information, see Launching an Amazon ECS container instance in the Amazon Elastic Container Service Developer Guide.

AWS Batch doesn't upgrade the AMIs in a compute environment after it's created. For example, it doesn't update the AMIs when a newer version of the Amazon ECS-optimized AMI is available. Therefore, you're responsible for the management of the guest operating system (including updates and security patches) and any additional application software or utilities that you install on the compute resources. To use a new AMI for your AWS Batch jobs, complete these steps:

  1. Create a new compute environment with the new AMI.

  2. Add the compute environment to an existing job queue.

  3. Remove the earlier compute environment from your job queue.

  4. Delete the earlier compute environment.

Usage

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batch_create_compute_environment(computeEnvironmentName, type, state,
  computeResources, serviceRole, tags)

Arguments

computeEnvironmentName

[required] The name for your compute environment. Up to 128 letters (uppercase and lowercase), numbers, hyphens, and underscores are allowed.

type

[required] The type of the compute environment: MANAGED or UNMANAGED. For more information, see Compute Environments in the AWS Batch User Guide.

state

The state of the compute environment. If the state is ENABLED, then the compute environment accepts jobs from a queue and can scale out automatically based on queues.

If the state is ENABLED, then the AWS Batch scheduler can attempt to place jobs from an associated job queue on the compute resources within the environment. If the compute environment is managed, then it can scale its instances out or in automatically, based on the job queue demand.

If the state is DISABLED, then the AWS Batch scheduler doesn't attempt to place jobs within the environment. Jobs in a STARTING or RUNNING state continue to progress normally. Managed compute environments in the DISABLED state don't scale out. However, they scale in to minvCpus value after instances become idle.

computeResources

Details about the compute resources managed by the compute environment. This parameter is required for managed compute environments. For more information, see Compute Environments in the AWS Batch User Guide.

serviceRole

[required] The full Amazon Resource Name (ARN) of the IAM role that allows AWS Batch to make calls to other AWS services on your behalf. For more information, see AWS Batch service IAM role in the AWS Batch User Guide.

If your specified role has a path other than /, then you must either specify the full role ARN (this is recommended) or prefix the role name with the path.

Depending on how you created your AWS Batch service role, its ARN might contain the service-role path prefix. When you only specify the name of the service role, AWS Batch assumes that your ARN doesn't use the service-role path prefix. Because of this, we recommend that you specify the full ARN of your service role when you create compute environments.

tags

The tags that you apply to the compute environment to help you categorize and organize your resources. Each tag consists of a key and an optional value. For more information, see Tagging AWS Resources in AWS General Reference.

These tags can be updated or removed using the tag_resource and untag_resource API operations. These tags don't propagate to the underlying compute resources.

Value

A list with the following syntax:

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list(
  computeEnvironmentName = "string",
  computeEnvironmentArn = "string"
)

Request syntax

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svc$create_compute_environment(
  computeEnvironmentName = "string",
  type = "MANAGED"|"UNMANAGED",
  state = "ENABLED"|"DISABLED",
  computeResources = list(
    type = "EC2"|"SPOT"|"FARGATE"|"FARGATE_SPOT",
    allocationStrategy = "BEST_FIT"|"BEST_FIT_PROGRESSIVE"|"SPOT_CAPACITY_OPTIMIZED",
    minvCpus = 123,
    maxvCpus = 123,
    desiredvCpus = 123,
    instanceTypes = list(
      "string"
    ),
    imageId = "string",
    subnets = list(
      "string"
    ),
    securityGroupIds = list(
      "string"
    ),
    ec2KeyPair = "string",
    instanceRole = "string",
    tags = list(
      "string"
    ),
    placementGroup = "string",
    bidPercentage = 123,
    spotIamFleetRole = "string",
    launchTemplate = list(
      launchTemplateId = "string",
      launchTemplateName = "string",
      version = "string"
    ),
    ec2Configuration = list(
      list(
        imageType = "string",
        imageIdOverride = "string"
      )
    )
  ),
  serviceRole = "string",
  tags = list(
    "string"
  )
)

Examples

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## Not run: 
# This example creates a managed compute environment with specific C4
# instance types that are launched on demand. The compute environment is
# called C4OnDemand.
svc$create_compute_environment(
  type = "MANAGED",
  computeEnvironmentName = "C4OnDemand",
  computeResources = list(
    type = "EC2",
    desiredvCpus = 48L,
    ec2KeyPair = "id_rsa",
    instanceRole = "ecsInstanceRole",
    instanceTypes = list(
      "c4.large",
      "c4.xlarge",
      "c4.2xlarge",
      "c4.4xlarge",
      "c4.8xlarge"
    ),
    maxvCpus = 128L,
    minvCpus = 0L,
    securityGroupIds = list(
      "sg-cf5093b2"
    ),
    subnets = list(
      "subnet-220c0e0a",
      "subnet-1a95556d",
      "subnet-978f6dce"
    ),
    tags = list(
      Name = "Batch Instance - C4OnDemand"
    )
  ),
  serviceRole = "arn:aws:iam::012345678910:role/AWSBatchServiceRole",
  state = "ENABLED"
)

# This example creates a managed compute environment with the M4 instance
# type that is launched when the Spot bid price is at or below 20% of the
# On-Demand price for the instance type. The compute environment is called
# M4Spot.
svc$create_compute_environment(
  type = "MANAGED",
  computeEnvironmentName = "M4Spot",
  computeResources = list(
    type = "SPOT",
    bidPercentage = 20L,
    desiredvCpus = 4L,
    ec2KeyPair = "id_rsa",
    instanceRole = "ecsInstanceRole",
    instanceTypes = list(
      "m4"
    ),
    maxvCpus = 128L,
    minvCpus = 0L,
    securityGroupIds = list(
      "sg-cf5093b2"
    ),
    spotIamFleetRole = "arn:aws:iam::012345678910:role/aws-ec2-spot-fleet-role",
    subnets = list(
      "subnet-220c0e0a",
      "subnet-1a95556d",
      "subnet-978f6dce"
    ),
    tags = list(
      Name = "Batch Instance - M4Spot"
    )
  ),
  serviceRole = "arn:aws:iam::012345678910:role/AWSBatchServiceRole",
  state = "ENABLED"
)

## End(Not run)

paws.compute documentation built on March 7, 2021, 5:10 p.m.