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Terraform Cloud

This document explains how to use Magic Castle with Terraform Cloud.

What is Terraform Cloud?

Terraform Cloud is HashiCorp’s managed service that allows to provision infrastructure using a web browser or a REST API instead of the command-line. This also means that the provisioned infrastructure parameters can be modified by a team and the state is stored in the cloud instead of a local machine.

When provisioning in commercial cloud, Terraform Cloud can also provide a cost estimate of the resources.

Getting started with Terraform Cloud

  1. Create a Terraform Cloud account
  2. Create an organization, join one or choose one available to you

Managing a Magic Castle cluster with Terraform Cloud

Creating the workspace

  1. Create a git repository in GitHub, GitLab, or any of the version control system provider supported by Terraform Cloud
  2. In this git repository, add a copy of the Magic Castle example main.tf available for the cloud of your choice
  3. Log in Terraform Cloud account
  4. Create a new workspace
    1. Choose Type: "Version control workflow"
    2. Connect to VCS: choose the version control provider that hosts your repository
    3. Choose the repository that contains your main.tf
    4. Configure settings: tweak the name and description to your liking
    5. Click on "Create workspace"

You will be redirected automatically to your new workspace.

Providing cloud provider credentials to Terraform Cloud

Terraform Cloud will invoke Terraform command-line in a remote virtual environment. For the CLI to be able to communicate with your cloud provider API, we need to define environment variables that Terraform will use to authenticate. The next sections explain which environment variables to define for each cloud provider and how to retrieve the values of the variable from the provider.

If you plan on using these environment variables with multiple workspaces, it is recommended to create a credential variable set in Terraform Cloud.

AWS

You need to define these environment variables:

  • AWS_ACCESS_KEY_ID
  • AWS_SECRET_ACCESS_KEY (sensitive)

The value of these variables can either correspond to the value of access key created on the AWS Security Credentials - Access keys page, or you can add user dedicated to Terraform Cloud in AWS IAM Users, and use its access key.

Azure

You need to define these environment variables:

  • ARM_CLIENT_ID
  • ARM_CLIENT_SECRET (sensitive)
  • ARM_SUBSCRIPTION_ID
  • ARM_TENANT_ID

Refer to Terraform Azure Provider - Creating a Service Principal to know how to create a Service Principal and retrieve the values for these environment variables.

Google Cloud

You need to define this environment variable:

  • GOOGLE_CLOUD_KEYFILE_JSON (sensitive)

The value of the variable will be the content of a Google Cloud service account JSON key file expressed a single line string. Example:

{"type": "service_account","project_id": "project-id-1234","private_key_id": "abcd1234",...}

You can use jq to format the string from the JSON file provided by Google:

jq . -c project-name-123456-abcdefjg.json

OpenStack / OVH

You need to define these environment variables:

  • OS_AUTH_URL
  • OS_PROJECT_ID
  • OS_REGION_NAME
  • OS_INTERFACE
  • OS_IDENTITY_API_VERSION
  • OS_USER_DOMAIN_NAME
  • OS_USERNAME
  • OS_PASSWORD (sensitive)

Apart from OS_PASSWORD, the values for these variables are available in OpenStack RC file provided for your project.

If you prefer to use OpenStack application credentials, you need to define at least these variables:

  • OS_AUTH_TYPE 
  • OS_AUTH_URL
  • OS_APPLICATION_CREDENTIAL_ID
  • OS_APPLICATION_CREDENTIAL_SECRET

and potentially these too:

  • OS_IDENTITY_API_VERSION 
  • OS_REGION_NAME
  • OS_INTERFACE

The values for these variables are available in OpenStack RC file provided when creating the application credentials.

Providing DNS provider credentials to Terraform Cloud

Terraform Cloud will invoke Terraform command-line in a remote virtual environment. For the CLI to be able to communicate with your DNS provider API, we need to define environment variables that Terraform will use to authenticate. The next sections explain which environment variables to define for each DNS provider and how to retrieve the values of the variable from the provider.

CloudFlare

Refer to DNS - CloudFlare section of Magic Castle main documentation to determine which environment variables needs to be set.

Google Cloud DNS

Refer to DNS - Google Cloud section of Magic Castle main documentation to determine which environment variables needs to be set.

Managing Magic Castle variables with Terraform Cloud UI

It is possible to use Terraform Cloud web interface to define variable values in your main.tf. For example, you could want to define a guest password without writing it directly in main.tf to avoid displaying publicly.

To manage a variable with Terraform Cloud:

  1. edit your main.tf to define the variables you want to manage. In the following example, we want to manage the number of nodes and the guest password.

    Add the variables at the beginning of the main.tf:

    variable "nb_nodes" {}
variable "password" {}

    Then replace the static value by the variable in our main.tf,

    compute node count

    node = { type = "p2-3gb", tags = ["node"], count = var.nb_nodes }

    guest password

    guest_passwd = var.password

  2. Commit and push this changes to your git repository.

  3. In Terraform Cloud workspace associated with that repository, go in "Variables.

  4. Under "Terraform Variables", click the "Add variable" button and create a variable for each one defined previously in the main.tf. Check "Sensitive" if the variable content should not never be shown in the UI or the API.

You may edit the variables at any point of your cluster lifetime.

Applying changes

To create your cluster, apply changes made to your main.tf or the variables, you will need to queue a plan. When you push to the default branch of the linked git repository, a plan will be automatically created. You can also create a plan manually. To do so, click on the "Queue plan manually" button inside your workspace, then "Queue plan".

Once the plan has been successfully created, you can apply it using the "Runs" section. Click on the latest queued plan, then on the "Apply plan" button at the bottom of the plan page.

Auto apply

It is possible to apply automatically a successful plan. Go in the "Settings" section, and under "Apply method" select "Auto apply". Any following successful plan will then be automatically applied.

Magic Castle, Terraform Cloud and the CLI

Terraform cloud only allows to apply or destroy the plan as stated in the main.tf, but sometimes it can be useful to run some other terraform commands that are only available through the command-line interface, for example terraform taint.

It is possible to import the terraform state of a cluster on your local computer and then use the CLI on it.

  1. Log in Terraform cloud:
terraform login
  1. Create a folder where the terraform state will be stored:
mkdir my-cluster-1
  1. Create a file named cloud.tf with the following content in your cluster folder:
terraform {
  cloud {
    organization = "REPLACE-BY-YOUR-TF-CLOUD-ORG"
    workspaces {
      name = "REPLACE-BY-THE-NAME-OF-YOUR-WORKSPACE"
    }
  }
}

replace the values of organization and name with the appropriate value for your cluster.

  1. Initialize the folder and retrieve the state:
terraform init

To confirm the workspace has been properly imported locally, you can list the resources using:

terraform state list

Enable Magic Castle Autoscaling

Magic Castle in combination with Terraform Cloud (TFE) can be configured to give Slurm the ability to create and destroy instances based on the job queue content.

To enable this feature:

  1. Create a TFE API Token and save it somewhere safe.

    1.1. If you subscribe to Terraform Cloud Team & Governance plan, you can generate a Team API Token. The team associated with this token requires no access to organization and can be secret. It does not have to include any member. Team API token is preferable as its permissions can be restricted to the minimum required for autoscale purpose.

  2. Create a workspace in TFE

    2.1. Make sure the repo is private as it will contain the API token.

    2.2. If you generated a Team API Token in 1, provide access to the workspace to the team:

    1. Workspace Settings -> Team Access -> Add team and permissions
    2. Select the team
    3. Click on "Customize permissions for this team"
    4. Under "Runs" select "Apply"
    5. Under "Variables" select "Read and write"
    6. Leave the rest as is and click on "Assign custom permissions"

    2.3 In Configure settings, under Advanced options, for Apply method, select Auto apply.

  3. Create the environment variables of the cloud provider credentials in TFE

  4. Create a variable named pool in TFE. Set value to [] and check HCL.

  5. Add a file named data.yaml in your git repo with the following content:

    ---
profile::slurm::controller::tfe_token: <TFE API token>
profile::slurm::controller::tfe_workspace: <TFE workspace id>

    Complete the file by replacing <TFE API TOKEN> with the token generated at step 1 and <TFE workspace id> (i.e.: ws-...) by the id of the workspace created at step 2. It is recommended to encrypt the TFE API token before committing data.yaml in git. Refer to section 4.15 of README.md to know how to encrypt the token.

  6. Add data.yaml in git and push.

  7. Modify main.tf:

    1. If not already present, add the following definition of the pool variable at the beginning of your main.tf.
    variable "pool" { description = "Slurm pool of compute nodes" }
    1. Add instances to instances with the tags pool and node. These are the nodes that Slurm will able to create and destroy.
    2. If not already present, add the following line after the instances definition to pass the list of compute nodes from Terraform cloud workspace variable to the provider module:
    pool = var.pool
    1. On the right-hand-side of public_keys = , replace [file("~/.ssh/id_rsa.pub")] by a list of SSH public keys that will have admin access to the cluster.
    2. After the line public_keys = ..., add hieradata = file("data.yaml").
    3. Stage changes, commit and push to git repo.

  8. Go to your workspace in TFE, click on Actions -> Start a new run -> Plan and apply -> Start run. Then, click on "Confirm & Apply" and "Confirm Plan".

  9. Compute nodes defined in step 8 can be modified at any point in the cluster lifetime and more pool compute nodes can be added or removed if needed.

Considerations for autoscaling

To reduce the time required for compute nodes to become available in Slurm, consider creating a compute node image.

JupyterHub will time out by default after 300 seconds if a node is not spawned yet. Since it may take longer than this to spawn a node, even with an image created, consider increasing the timeout by adding the following to your YAML configuration file:

jupyterhub::jupyterhub_config_hash:
  SlurmFormSpawner:
    start_timeout: 900

Slurm 23 adds the possibility for sinfo to report nodes that are not yet spawned. This is useful if you want JupyterHub to be aware of those nodes, for example if you want to allow to use GPU nodes without keeping them online at all time. To use that version of Slurm, add the following to your YAML configuration file:

profile::slurm::base::slurm_version: '23.02'

Troubleshoot autoscaling with Terraform Cloud

If after enabling autoscaling with Terraform Cloud for your Magic Castle cluster, the number of nodes does not increase when submitting jobs, verify the following points:

  1. Go to the Terraform Cloud workspace webpage, and look for errors in the runs. If the runs were only triggered by changes to the git repo, it means scaling signals from the cluster do not reach the Terraform cloud workspace or no signals were sent at all.
  2. Make sure the Terraform Cloud workspace id matches with the value of profile::slurm::controller::tfe_workspace in data.yaml.
  3. Execute squeue on the cluster, and verify the reasons why jobs are still in the queue. If under the column (Reason), there is the keyword ReqNodeNotAvail, it implies Slurm tried to boot the listed nodes, but they would not show up before the timeout, therefore Slurm marked them as down. It can happen if your cloud provider is slow to build the instances, or following a configuration problem like in 2. When Slurm marks a node as down, a trace is left in slurmctld's log - using zgrep on the slurm controller node (typically mgmt1): 
    sudo zgrep "marking down" /var/log/slurm/slurmctld.log*
    To tell Slurm these nodes are available again, enter the following command: 
    sudo /opt/software/slurm/bin/scontrol update nodename=node[Y-Z] state=IDLE
    Replace node[Y-Z] by the hostname range listed next to ReqNodeNotAvail in squeue.
  4. Under mgmt1:/var/log/slurm, look for errors in the file slurm_resume.log.