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Deploy Amazon SageMaker Initiatives with Terraform Cloud

admin by admin
May 30, 2025
in Artificial Intelligence
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Deploy Amazon SageMaker Initiatives with Terraform Cloud
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Amazon SageMaker Initiatives empower information scientists to self-serve Amazon Internet Companies (AWS) tooling and infrastructure to arrange all entities of the machine studying (ML) lifecycle, and additional allow organizations to standardize and constrain the assets accessible to their information science groups in pre-packaged templates.

For AWS prospects utilizing Terraform to outline and handle their infrastructure-as-code (IaC), the present greatest apply for enabling Amazon SageMaker Initiatives carries a dependency on AWS CloudFormation to facilitate integration between AWS Service Catalog and Terraform. This blocks enterprise prospects whose IT governance prohibit use of vendor-specific IaC equivalent to CloudFormation from utilizing Terraform Cloud.

This submit outlines how one can allow SageMaker Initiatives with Terraform Cloud, eradicating the CloudFormation dependency.

AWS Service Catalog engine for Terraform Cloud

SageMaker Initiatives are instantly mapped to AWS Service Catalog merchandise. To obviate using CloudFormation, these merchandise should be designated as Terraform merchandise that use the AWS Service Catalog Engine (SCE) for Terraform Cloud. This module, actively maintained by Hashicorp, comprises AWS-native infrastructure for integrating Service Catalog with Terraform Cloud in order that your Service Catalog merchandise are deployed utilizing the Terraform Cloud platform.

By following the steps on this submit, you need to use the Service Catalog engine to deploy SageMaker Initiatives instantly from Terraform Cloud.

Conditions

To efficiently deploy the instance, you should have the next:

  1. An AWS account with the mandatory permissions to create and handle SageMaker Initiatives and Service Catalog merchandise. See the Service Catalog documentation for extra info on Service Catalog permissions.
  2. An current Amazon SageMaker Studio area with an related Amazon SageMaker person profile. The SageMaker Studio area should have SageMaker Initiatives enabled. See Use fast setup for Amazon SageMaker AI.
  3. A Unix terminal with the AWS Command Line Interface (AWS CLI) and Terraform put in. See the Putting in or updating to the newest model of the AWS CLIand the Set up Terraform for extra details about set up.
  4. An current Terraform Cloud account with the mandatory permissions to create and handle workspaces. See the next tutorials to rapidly create your personal account:
    1. HCP Terraform – intro and signal Up
    2. Log In to HCP Terraform from the CLI

See Terraform groups and organizations documentation for extra details about Terraform Cloud permissions.

Deployment steps

  1. Clone the sagemaker-custom-project-templates repository from the AWS Samples GitHub to your native machine, replace the submodules, and navigate to the mlops-terraform-cloud listing.
    $ git clone https://github.com/aws-samples/sagemaker-custom-project-templates.git
    $ cd sagemaker-custom-project_templates
    $ git submodule replace --init --recursive
    $ cd mlops-terraform-cloud

The previous code base above creates a Service Catalog portfolio, provides the SageMaker Venture template as a Service Catalog product to the portfolio, permits the SageMaker Studio position to entry the Service Catalog product, and provides the mandatory tags to make the product seen in SageMaker Studio. See Create Customized Venture Templates within the SageMaker Initiatives Documentation for extra details about this course of.

  1. Login to your Terraform Cloud account

This prompts your browser to signal into your HCP account and generates a safety token. Copy this safety token and paste it again into your terminal.

  1. Navigate to your AWS account and retrieve the SageMaker person position Amazon Useful resource Title (ARN) for the SageMaker person profile related together with your SageMaker Studio area. This position is used to grant SageMaker Studio customers permissions to create and handle SageMaker Initiatives.
    • Within the AWS Administration Console for Amazon SageMaker, select Domains from the navigation pane
      Amazon SageMaker home screen highlighting machine learning workflow options and quick-start configurations for users and organizations
    • Choose your studio area
      Amazon SageMaker Domains management screen with one InService domain, emphasizing shared environment for team collaboration
    • Below Person Profiles, choose your person profile
      Amazon SageMaker Domain management interface showing user profiles tab with configuration options and launch controls
    • Within the Person Particulars, copy the ARN
      SageMaker lead-data-scientist profile configuration with IAM role and creation details
  2. Create a tfvars file with the mandatory variables for the Terraform Cloud workspace
    $ cp terraform.tfvars.instance terraform.tfvars

  3. Set the suitable values within the newly created tfvars file. The next variables are required:
    tfc_organization = "my-tfc-organization"
    tfc_team = "aws-service-catalog"
    token_rotation_interval_in_days = 30
    sagemaker_user_role_arns = ["arn:aws:iam::XXXXXXXXXXX:role/service-role/AmazonSageMaker-ExecutionRole"]

Make it possible for your required Terraform Cloud (TFC) group has the right entitlements and that your tfc_team is exclusive for this deployment. See the Terraform Organizations Overview for extra info on creating organizations.

  1. Initialize the Terraform Cloud workspace
  2. Apply the Terraform Cloud workspace
  3. Return to the SageMaker console utilizing the person profile related to the SageMaker person position ARN that you just copied beforehand and select Open Studio software
    SageMaker Studio welcome screen highlighting integrated ML development environment with login options
  4. Within the navigation pane, select Deployments after which select Initiatives
    SageMaker Studio home interface highlighting ML workflow options, including JupyterLab and Code Editor, with Projects section emphasized for model deployment
  5. Select Create challenge, choose the mlops-tf-cloud-example product after which select Subsequent
    SageMaker Studio project creation workflow showing template selection step with Organization templates tab and MLOps workflow automation option
  6. In Venture particulars, enter a novel identify for the template and (choice) enter a challenge description. Select Create
    SageMaker project setup interface on Project details step, showcasing naming conventions, description field, and tagging options for MLOps workflow
  7. In a separate tab or window, return to your Terraform Cloud account’s Workspaces and also you’ll see a workspace being provisioned instantly out of your SageMaker Venture deployment. The naming conference of the Workspace will likely be –
    Terraform workspaces dashboard showing status counts and one workspace with Applied status

Additional customization

This instance will be modified to incorporate {custom} Terraform in your SageMaker Venture template. To take action, outline your Terraform within the mlops-product/product listing. When able to deploy, you’ll want to archive and compress this Terraform utilizing the next command:

$ cd mlops-product
$ tar -czf product.tar.gz product

Cleanup

To take away the assets deployed by this instance, run the next from the challenge listing:

Conclusion

On this submit you outlined, deployed, and provisioned a SageMaker Venture {custom} template purely in Terraform. With no dependencies on different IaC instruments, now you can allow SageMaker Initiatives strictly inside your Terraform Enterprise infrastructure.


Concerning the writer

Max Copeland is a Machine Studying Engineer for AWS, main buyer engagements spanning ML-Ops, information science, information engineering, and generative AI.

Tags: AmazoncloudDeployProjectsSageMakerTerraform
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