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Module Structure

AWS SageMaker Terraform Module

terraform-aws-arc-sagemaker

Latest Release Last Updated Terraform GitHub Actions

Quality gate

Overview

The ARC Terraform module provides a robust and fully extensible solution for managing SageMaker Studio domains, user profiles, models, endpoint, endpoint configurations, and pipelines. It supports custom app settings, and VPC integration to meet enterprise-grade machine learning needs. With dynamic configurations and secure defaults, the module enables rapid, consistent, and scalable SageMaker infrastructure provisioning using best practices.

Prerequisites

Before using this module, ensure you have the following:

  • AWS credentials configured.
  • Terraform installed.
  • A working knowledge of Terraform.

Getting Started

  1. Define the Module

Initially, it's essential to define a Terraform module, which is organized as a distinct directory encompassing Terraform configuration files. Within this module directory, input variables and output values must be defined in the variables.tf and outputs.tf files, respectively. The following illustrates an example directory structure:

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sagemaker/
|-- main.tf
|-- variables.tf
|-- outputs.tf
  1. Define Input Variables

Inside the variables.tf or in *.tfvars file, you should define values for the variables that the module requires.

  1. Use the Module in Your Main Configuration In your main Terraform configuration file (e.g., main.tf), you can use the module. Specify the source of the module, and version, For Example
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module "sagemaker_model" {
  source                 = "sourcefuse/arc-sagemaker/aws"
  version                = "0.0.1"

  name                   = "terraform-arc"
  create_endpoint_config = true
  create_model           = true


  primary_container = {
    image          = "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3"
    model_data_url = "s3://your-sagemaker-model-bucket-21-05-25/model/model.tar.gz"
    environment    = {}
  }

  production_variants = [
    {
      variant_name           = "AllTraffic"
      initial_instance_count = 1
      instance_type          = "ml.m5.large"
      initial_variant_weight = 1.0
    }
  ]
  tags = module.tags.tags
}
  1. Output Values

Inside the outputs.tf file of the module, you can define output values that can be referenced in the main configuration. For example:

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output "model_name" {
  description = "Name of the SageMaker model"
  value       = module.sagemaker_model.model_name
}

output "model_arn" {
  description = "ARN of the SageMaker model"
  value       = module.sagemaker_model.model_arn
}

output "endpoint_config_arn" {
  description = "ARN of the SageMaker endpoint configuration"
  value       = module.sagemaker_model.endpoint_config_arn
}
  1. .tfvars

Inside the .tfvars file of the module, you can provide desired values that can be referenced in the main configuration.

First Time Usage

uncomment the backend block in main.tf 

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terraform init -backend-config=config.dev.hcl
If testing locally, terraform init should be fine

Create a dev workspace 

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terraform workspace new dev

Plan Terraform 

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terraform plan -var-file dev.tfvars

Apply Terraform 

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terraform apply -var-file dev.tfvars

Production Setup

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terraform init -backend-config=config.prod.hcl

Create a prod workspace 

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terraform workspace new prod

Plan Terraform 

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terraform plan -var-file prod.tfvars

Apply Terraform 

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terraform apply -var-file prod.tfvars

Cleanup

Destroy Terraform 

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terraform destroy -var-file dev.tfvars

Requirements

Name Version
terraform >= 1.5.0
aws ~> 5.0

Providers

Name Version
aws 5.100.0

Modules

Name Source Version
arc_security_group sourcefuse/arc-security-group/aws 0.0.1

Resources

Name Type
aws_iam_role.execution_role resource
aws_iam_role.pipeline_role resource
aws_iam_role.sagemaker_execution_role resource
aws_iam_role_policy.execution_role_custom resource
aws_iam_role_policy.pipeline_role_custom resource
aws_iam_role_policy_attachment.attach_policy resource
aws_iam_role_policy_attachment.execution_role_all_policies resource
aws_iam_role_policy_attachment.pipeline_role_sagemaker_full_access resource
aws_sagemaker_domain.this resource
aws_sagemaker_endpoint.this resource
aws_sagemaker_endpoint_configuration.this resource
aws_sagemaker_model.this resource
aws_sagemaker_pipeline.this resource
aws_sagemaker_user_profile.this resource

Inputs

Name Description Type Default Required
additional_iam_policies List of additional IAM policy ARNs to attach to the execution role list(string) [] no
additional_security_group_ids List of additional security group IDs to attach to the domain list(string) [] no
app_network_access_type Specifies the VPC used for non-EFS traffic. Valid values are PublicInternetOnly and VpcOnly string "PublicInternetOnly" no
app_security_group_management The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Valid values are Service and Customer string "Service" no
async_inference_config (Optional) Configuration for asynchronous inference. 
object({
    output_config = object({
      s3_output_path  = string
      s3_failure_path = optional(string)
      kms_key_id      = optional(string)
      notification_config = optional(object({
        include_inference_response_in = optional(string)
        error_topic                   = optional(string)
        success_topic                 = optional(string)
      }))
    })
    client_config = optional(object({
      max_concurrent_invocations_per_instance = optional(number)
    }))
  })
 null no
auth_mode The mode of authentication that members use to access the domain. Valid values are IAM and SSO string "IAM" no
container List of containers for inference pipeline (alternative to primary_container) list(any) null no
create_domain Whether to create the SageMaker domain bool false no
create_endpoint Whether to create the SageMaker endpoint bool false no
create_endpoint_config Whether to create the SageMaker endpoint configuration bool false no
create_execution_role Whether to create an execution role for SageMaker bool false no
create_model Whether to create the SageMaker model bool false no
create_pipeline Whether to create the SageMaker pipeline bool false no
create_pipeline_role Whether to create a separate role for pipelines bool false no
create_sagemaker_execution_role Whether to create an SageMaker execution role bool false no
create_security_groups Whether to create security groups for SageMaker Studio bool false no
create_user_profile Whether to create the SageMaker user profile bool false no
data_capture_config (Optional) Configuration for capturing input/output data. 
object({
    initial_sampling_percentage = number
    destination_s3_uri          = string
    kms_key_id                  = optional(string)
    enable_capture              = optional(bool)
    capture_options = list(object({
      capture_mode = string
    }))
    capture_content_type_header = optional(object({
      csv_content_types  = optional(list(string))
      json_content_types = optional(list(string))
    }))
  })
 null no
default_space_settings The default space settings for the domain 
object({
    execution_role_arn = string
    security_groups    = optional(list(string))

    jupyter_server_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))
    }))

    kernel_gateway_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))
    }))

    jupyter_lab_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))
    }))

    space_storage_settings = optional(object({
      default_ebs_storage_settings = optional(object({
        default_ebs_volume_size_in_gb = number
        maximum_ebs_volume_size_in_gb = number
      }))
    }))

    custom_file_system_config = optional(object({
      efs_file_system_config = optional(object({
        file_system_id   = string
        file_system_path = string
      }))
    }))

    custom_posix_user_config = optional(object({
      gid = optional(number)
      uid = optional(number)
    }))
  })
 null no
default_user_settings The default user settings for the domain 
object({
    execution_role_arn  = string
    auto_mount_home_efs = optional(string, "Disabled")
    default_landing_uri = optional(string)
    studio_web_portal   = optional(string, "ENABLED")

    jupyter_lab_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))

      app_lifecycle_management = optional(object({
        idle_settings = optional(object({
          lifecycle_management        = optional(string, "ENABLED")
          idle_timeout_in_minutes     = optional(number, 60)
          max_idle_timeout_in_minutes = optional(number, 480)
          min_idle_timeout_in_minutes = optional(number, 60)
        }))
      }))

      custom_images = optional(list(object({
        app_image_config_name = string
        image_name            = string
        image_version_number  = optional(number)
      })))

      code_repositories = optional(list(object({
        repository_url = string
      })))

      lifecycle_config_arns         = optional(list(string))
      built_in_lifecycle_config_arn = optional(string)

      emr_settings = optional(object({
        assumable_role_arns = optional(list(string))
        execution_role_arns = optional(list(string))
      }))
    }))

    code_editor_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))

      app_lifecycle_management = optional(object({
        idle_settings = optional(object({
          lifecycle_management        = optional(string, "ENABLED")
          idle_timeout_in_minutes     = optional(number, 60)
          max_idle_timeout_in_minutes = optional(number, 480)
          min_idle_timeout_in_minutes = optional(number, 60)
        }))
      }))

      custom_images = optional(list(object({
        app_image_config_name = string
        image_name            = string
        image_version_number  = optional(number)
      })))

      lifecycle_config_arns         = optional(list(string))
      built_in_lifecycle_config_arn = optional(string)
    }))

    jupyter_server_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))

      code_repositories = optional(list(object({
        repository_url = string
      })))

      lifecycle_config_arns = optional(list(string))
    }))

    kernel_gateway_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))

      custom_images = optional(list(object({
        app_image_config_name = string
        image_name            = string
        image_version_number  = optional(number)
      })))

      lifecycle_config_arns = optional(list(string))
    }))

    canvas_app_settings = optional(object({
      time_series_forecasting_settings = optional(object({
        status                   = optional(string, "DISABLED")
        amazon_forecast_role_arn = optional(string)
      }))

      model_register_settings = optional(object({
        status                                = optional(string, "DISABLED")
        cross_account_model_register_role_arn = optional(string)
      }))

      workspace_settings = optional(object({
        s3_artifact_path = optional(string)
        s3_kms_key_id    = optional(string)
      }))

      direct_deploy_settings = optional(object({
        status = optional(string, "DISABLED")
      }))

      kendra_settings = optional(object({
        status = optional(string, "DISABLED")
      }))

      identity_provider_oauth_settings = optional(list(object({
        data_source_name = optional(string)
        secret_arn       = optional(string)
        status           = optional(string, "DISABLED")
      })))

      emr_serverless_settings = optional(object({
        execution_role_arn = optional(string)
        status             = optional(string, "DISABLED")
      }))
    }))

    tensor_board_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))
    }))

    r_session_app_settings = optional(object({
      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))

      custom_images = optional(list(object({
        app_image_config_name = string
        image_name            = string
        image_version_number  = optional(number)
      })))
    }))

    r_studio_server_pro_app_settings = optional(object({
      access_status = optional(string, "DISABLED")
      user_group    = optional(string, "R_STUDIO_USER")
    }))

    sharing_settings = optional(object({
      notebook_output_option = optional(string, "Disabled")
      s3_kms_key_id          = optional(string)
      s3_output_path         = optional(string)
    }))

    space_storage_settings = optional(object({
      default_ebs_storage_settings = optional(object({
        default_ebs_volume_size_in_gb = number
        maximum_ebs_volume_size_in_gb = number
      }))
    }))

    custom_file_system_config = optional(object({
      efs_file_system_config = optional(object({
        file_system_id   = string
        file_system_path = string
      }))
    }))

    custom_posix_user_config = optional(object({
      gid = optional(number)
      uid = optional(number)
    }))

    studio_web_portal_settings = optional(object({
      hidden_app_types      = optional(list(string))
      hidden_instance_types = optional(list(string))
      hidden_ml_tools       = optional(list(string))
    }))
  })
 null no
deployment_config Deployment configuration block 
object({
    blue_green_update_policy = optional(object({
      traffic_routing_configuration = object({
        type                     = string
        wait_interval_in_seconds = number
        canary_size = optional(object({
          type  = string
          value = number
        }))
        linear_step_size = optional(object({
          type  = string
          value = number
        }))
      })
      maximum_execution_timeout_in_seconds = optional(number)
      termination_wait_in_seconds          = optional(number)
    }))
    auto_rollback_configuration = optional(object({
      alarms = list(object({
        alarm_name = string
      }))
    }))
    rolling_update_policy = optional(object({
      wait_interval_in_seconds             = number
      maximum_execution_timeout_in_seconds = optional(number)
      maximum_batch_size = object({
        type  = string
        value = number
      })
      rollback_maximum_batch_size = optional(object({
        type  = string
        value = number
      }))
    }))
  })
 null no
domain_name The name of the SageMaker domain string "arc-sagemaker-domain" no
domain_settings The domain settings 
object({
    execution_role_identity_config = optional(string, "USER_PROFILE_NAME")
    security_group_ids             = optional(list(string))

    docker_settings = optional(object({
      enable_docker_access      = optional(string, "ENABLED")
      vpc_only_trusted_accounts = optional(list(string))
    }))

    r_studio_server_pro_domain_settings = optional(object({
      domain_execution_role_arn    = string
      r_studio_connect_url         = optional(string)
      r_studio_package_manager_url = optional(string)

      default_resource_spec = optional(object({
        instance_type                 = optional(string, "ml.t3.medium")
        lifecycle_config_arn          = optional(string)
        sagemaker_image_arn           = optional(string)
        sagemaker_image_version_arn   = optional(string)
        sagemaker_image_version_alias = optional(string)
      }))
    }))
  })
 null no
enable_network_isolation Isolate the model container from external network bool null no
execution_role_name Name of the execution role to create string "SageMakerStudioExecutionRole" no
execution_role_path Path for the execution role string "/" no
inference_execution_config Multi-container execution configuration 
object({
    mode = string
  })
 null no
kms_key_arn (Optional) ARN of the KMS key to encrypt storage volume data. string null no
kms_key_id The AWS KMS customer managed CMK used to encrypt the EFS volume attached to the domain string null no
name The name of the SageMaker model. string "terraform-sg" no
name_prefix (Optional) Prefix for the endpoint configuration name. Conflicts with name. string null no
pipeline_role_name Name of the pipeline role to create string "SageMakerPipelineExecutionRole" no
pipeline_role_path Path for the pipeline role string "/" no
pipelines List of SageMaker pipelines to create 
list(object({
    name         = string
    display_name = string
    description  = optional(string)
    definition   = optional(string)
    role_arn     = optional(string)
    tags         = optional(map(string))

    pipeline_definition_s3_location = optional(object({
      bucket     = string
      object_key = string
      version_id = optional(string)
    }))

    parallelism_configuration = optional(object({
      max_parallel_execution_steps = number
    }))
  }))
 [] no
primary_container Primary container block 
object({
    image              = string
    mode               = optional(string)
    model_data_url     = optional(string)
    model_package_name = optional(string)
    container_hostname = optional(string)
    environment        = optional(map(string))
    image_config = optional(object({
      repository_access_mode = string
      repository_auth_config = optional(object({
        repository_credentials_provider_arn = string
      }))
    }))
    inference_specification_name = optional(string)
    multi_model_config = optional(object({
      model_cache_setting = string
    }))
    model_data_source = optional(object({
      s3_data_source = object({
        compression_type = string
        s3_data_type     = string
        s3_uri           = string
        model_access_config = optional(object({
          accept_eula = bool
        }))
      })
    }))
  })
 
{
  "environment": {},
  "image": "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3",
  "model_data_url": "s3://your-sagemaker-model-bucket-21-05-25/model/model.tar.gz"
}
 no
production_variants (Required) List of production variant configurations. 
list(object({
    variant_name                                      = optional(string)
    model_name                                        = optional(string)
    initial_instance_count                            = optional(number)
    instance_type                                     = optional(string)
    accelerator_type                                  = optional(string)
    container_startup_health_check_timeout_in_seconds = optional(number)
    core_dump_config = optional(object({
      destination_s3_uri = string
      kms_key_id         = string
    }))
    enable_ssm_access                      = optional(bool)
    inference_ami_version                  = optional(string)
    initial_variant_weight                 = optional(number)
    model_data_download_timeout_in_seconds = optional(number)
    routing_config = optional(object({
      routing_strategy = string
    }))
    serverless_config = optional(object({
      max_concurrency         = number
      memory_size_in_mb       = number
      provisioned_concurrency = optional(number)
    }))
    managed_instance_scaling = optional(object({
      status             = optional(string)
      min_instance_count = optional(number)
      max_instance_count = optional(number)
    }))
    volume_size_in_gb = optional(number)
  }))
 
[
  {
    "initial_instance_count": 1,
    "initial_variant_weight": 1,
    "instance_type": "ml.m5.large",
    "variant_name": "AllTraffic"
  }
]
 no
retention_policy The retention policy for the domain 
object({
    home_efs_file_system = optional(string, "Retain")
  })
 null no
security_group_data (optional) Security Group data 
object({
    security_group_ids_to_attach = optional(list(string), [])
    create                       = optional(bool, true)
    description                  = optional(string, null)
    ingress_rules = optional(list(object({
      description              = optional(string, null)
      cidr_block               = optional(string, null)
      source_security_group_id = optional(string, null)
      from_port                = number
      ip_protocol              = string
      to_port                  = string
      self                     = optional(bool, false)
    })), [])
    egress_rules = optional(list(object({
      description                   = optional(string, null)
      cidr_block                    = optional(string, null)
      destination_security_group_id = optional(string, null)
      from_port                     = number
      ip_protocol                   = string
      to_port                       = string
      prefix_list_id                = optional(string, null)
    })), [])
  })
 
{
  "create": false
}
 no
security_group_name sagemaker security group name string "sagemaker-sg" no
shadow_production_variants (Optional) List of shadow production variant configurations. 
list(object({
    variant_name           = optional(string)
    model_name             = string
    initial_instance_count = optional(number)
    instance_type          = optional(string)
    initial_variant_weight = optional(number)
  }))
 [] no
subnet_ids The VPC subnets that Studio uses for communication list(string) [] no
tag_propagation Indicates whether custom tag propagation is supported for the domain. Valid values are ENABLED and DISABLED string "DISABLED" no
tags Tags to assign to the SageMaker model map(string) {} no
user_profiles List of user profiles to create 
list(object({
    name                           = string
    single_sign_on_user_identifier = optional(string)
    single_sign_on_user_value      = optional(string)
    tags                           = optional(map(string))

    user_settings = optional(object({
      execution_role_arn  = optional(string)
      auto_mount_home_efs = optional(string)
      default_landing_uri = optional(string)
      studio_web_portal   = optional(string)
      security_groups     = optional(list(string))

      jupyter_lab_app_settings = optional(object({
        default_resource_spec = optional(object({
          instance_type                 = optional(string)
          lifecycle_config_arn          = optional(string)
          sagemaker_image_arn           = optional(string)
          sagemaker_image_version_arn   = optional(string)
          sagemaker_image_version_alias = optional(string)
        }))

        app_lifecycle_management = optional(object({
          idle_settings = optional(object({
            lifecycle_management        = optional(string)
            idle_timeout_in_minutes     = optional(number)
            max_idle_timeout_in_minutes = optional(number)
            min_idle_timeout_in_minutes = optional(number)
          }))
        }))

        custom_images = optional(list(object({
          app_image_config_name = string
          image_name            = string
          image_version_number  = optional(number)
        })))

        code_repositories = optional(list(object({
          repository_url = string
        })))

        lifecycle_config_arns         = optional(list(string))
        built_in_lifecycle_config_arn = optional(string)

        emr_settings = optional(object({
          assumable_role_arns = optional(list(string))
          execution_role_arns = optional(list(string))
        }))
      }))

      code_editor_app_settings = optional(object({
        default_resource_spec = optional(object({
          instance_type                 = optional(string)
          lifecycle_config_arn          = optional(string)
          sagemaker_image_arn           = optional(string)
          sagemaker_image_version_arn   = optional(string)
          sagemaker_image_version_alias = optional(string)
        }))

        app_lifecycle_management = optional(object({
          idle_settings = optional(object({
            lifecycle_management        = optional(string)
            idle_timeout_in_minutes     = optional(number)
            max_idle_timeout_in_minutes = optional(number)
            min_idle_timeout_in_minutes = optional(number)
          }))
        }))

        custom_images = optional(list(object({
          app_image_config_name = string
          image_name            = string
          image_version_number  = optional(number)
        })))

        lifecycle_config_arns         = optional(list(string))
        built_in_lifecycle_config_arn = optional(string)
      }))

      jupyter_server_app_settings = optional(object({
        default_resource_spec = optional(object({
          instance_type                 = optional(string)
          lifecycle_config_arn          = optional(string)
          sagemaker_image_arn           = optional(string)
          sagemaker_image_version_arn   = optional(string)
          sagemaker_image_version_alias = optional(string)
        }))

        code_repositories = optional(list(object({
          repository_url = string
        })))

        lifecycle_config_arns = optional(list(string))
      }))

      kernel_gateway_app_settings = optional(object({
        default_resource_spec = optional(object({
          instance_type                 = optional(string)
          lifecycle_config_arn          = optional(string)
          sagemaker_image_arn           = optional(string)
          sagemaker_image_version_arn   = optional(string)
          sagemaker_image_version_alias = optional(string)
        }))

        custom_images = optional(list(object({
          app_image_config_name = string
          image_name            = string
          image_version_number  = optional(number)
        })))

        lifecycle_config_arns = optional(list(string))
      }))

      canvas_app_settings = optional(object({
        time_series_forecasting_settings = optional(object({
          status                   = optional(string)
          amazon_forecast_role_arn = optional(string)
        }))

        model_register_settings = optional(object({
          status                                = optional(string)
          cross_account_model_register_role_arn = optional(string)
        }))

        workspace_settings = optional(object({
          s3_artifact_path = optional(string)
          s3_kms_key_id    = optional(string)
        }))

        direct_deploy_settings = optional(object({
          status = optional(string)
        }))

        kendra_settings = optional(object({
          status = optional(string)
        }))

        identity_provider_oauth_settings = optional(list(object({
          data_source_name = optional(string)
          secret_arn       = optional(string)
          status           = optional(string)
        })))

        emr_serverless_settings = optional(object({
          execution_role_arn = optional(string)
          status             = optional(string)
        }))
      }))

      tensor_board_app_settings = optional(object({
        default_resource_spec = optional(object({
          instance_type                 = optional(string)
          lifecycle_config_arn          = optional(string)
          sagemaker_image_arn           = optional(string)
          sagemaker_image_version_arn   = optional(string)
          sagemaker_image_version_alias = optional(string)
        }))
      }))

      r_session_app_settings = optional(object({
        default_resource_spec = optional(object({
          instance_type                 = optional(string)
          lifecycle_config_arn          = optional(string)
          sagemaker_image_arn           = optional(string)
          sagemaker_image_version_arn   = optional(string)
          sagemaker_image_version_alias = optional(string)
        }))

        custom_images = optional(list(object({
          app_image_config_name = string
          image_name            = string
          image_version_number  = optional(number)
        })))
      }))

      r_studio_server_pro_app_settings = optional(object({
        access_status = optional(string)
        user_group    = optional(string)
      }))

      sharing_settings = optional(object({
        notebook_output_option = optional(string)
        s3_kms_key_id          = optional(string)
        s3_output_path         = optional(string)
      }))

      space_storage_settings = optional(object({
        default_ebs_storage_settings = optional(object({
          default_ebs_volume_size_in_gb = number
          maximum_ebs_volume_size_in_gb = number
        }))
      }))

      custom_file_system_config = optional(object({
        efs_file_system_config = optional(object({
          file_system_id   = string
          file_system_path = string
        }))
      }))

      custom_posix_user_config = optional(object({
        gid = optional(number)
        uid = optional(number)
      }))

      studio_web_portal_settings = optional(object({
        hidden_app_types      = optional(list(string))
        hidden_instance_types = optional(list(string))
        hidden_ml_tools       = optional(list(string))
      }))
    }))
  }))
 [] no
vpc_config VPC configuration for the model 
object({
    security_group_ids = list(string)
    subnets            = list(string)
  })
 null no
vpc_id The ID of the VPC that Studio uses for communication string null no

Outputs

Name Description
domain_arn ARN of the SageMaker domain
domain_id ID of the SageMaker domain
domain_url URL to access the SageMaker Studio
endpoint_arn ARN of the SageMaker endpoint
endpoint_config_arn ARN of the SageMaker endpoint configuration
endpoint_config_name Name of the SageMaker endpoint configuration
endpoint_name Name of the SageMaker endpoint
model_arn ARN of the SageMaker model
model_name Name of the SageMaker model
pipeline_arns List of SageMaker pipeline ARNs
pipeline_names List of SageMaker pipeline names
user_profile_names List of SageMaker user profile names
user_profiles Map of SageMaker user profiles with their ARNs

Versioning

This project uses a .version file at the root of the repo which the pipeline reads from and does a git tag.

When you intend to commit to main, you will need to increment this version. Once the project is merged, the pipeline will kick off and tag the latest git commit.

Development

Prerequisites

Configurations

  • Configure pre-commit hooks 
    1
    pre-commit install

Versioning

while Contributing or doing git commit please specify the breaking change in your commit message whether its major,minor or patch

For Example

1
git commit -m "your commit message #major"
By specifying this , it will bump the version and if you don't specify this in your commit message then by default it will consider patch and will bump that accordingly

Tests

  • Tests are available in test directory
  • Configure the dependencies 
    1
2
3
    cd test/
go mod init github.com/sourcefuse/terraform-aws-refarch-<module_name>
go get github.com/gruntwork-io/terratest/modules/terraform
  • Now execute the test
    1
    go test -timeout  30m

Authors

This project is authored by: - SourceFuse ARC Team