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Speakeasy Lint Results: 0 errors, 1 warnings, 1368 hintsClick to expand lint outputLint completed at: Wed Oct 29 09:05:04 UTC 2025 |
Terraform Plan:Click to expand terraform planTerraform used the selected providers to generate the following execution
plan. Resource actions are indicated with the following symbols:
+ create
Terraform will perform the following actions:
# seqera_orgs.test_org will be created
+ resource "seqera_orgs" "test_org" {
+ description = "testing org for the terraform provider"
+ full_name = "seqera_test_shahbaz_tf_provider"
+ location = (known after apply)
+ member_id = (known after apply)
+ member_role = (known after apply)
+ name = "seqera_test_shahbaz_tf_provider"
+ org_id = (known after apply)
+ website = (known after apply)
}
# seqera_teams.my_teams will be created
+ resource "seqera_teams" "my_teams" {
+ avatar_url = (known after apply)
+ description = "Team created by Terraform"
+ members_count = (known after apply)
+ name = "terraform-test-team"
+ org_id = (known after apply)
+ team_id = (known after apply)
}
# seqera_workspace.my_workspace will be created
+ resource "seqera_workspace" "my_workspace" {
+ date_created = (known after apply)
+ description = "A test workspace created with Terraform"
+ full_name = "Test Workspace for Terraform Provider"
+ id = (known after apply)
+ last_updated = (known after apply)
+ name = "test-workspace-tf"
+ org_id = (known after apply)
+ visibility = "PRIVATE"
}
# module.aws_batch.seqera_action.my_action will be created
+ resource "seqera_action" "my_action" {
+ action_id = (known after apply)
+ config = (known after apply)
+ date_created = (known after apply)
+ event = (known after apply)
+ hook_id = (known after apply)
+ hook_url = (known after apply)
+ id = (known after apply)
+ labels = (known after apply)
+ last_seen = (known after apply)
+ last_updated = (known after apply)
+ launch = {
+ compute_env = (known after apply)
+ compute_env_id = (known after apply)
+ config_profiles = []
+ config_text = ""
+ date_created = (known after apply)
+ entry_name = (known after apply)
+ head_job_cpus = (known after apply)
+ head_job_memory_mb = (known after apply)
+ id = (known after apply)
+ last_updated = (known after apply)
+ launch_container = (known after apply)
+ main_script = (known after apply)
+ optimization_id = (known after apply)
+ optimization_targets = (known after apply)
+ params_text = (known after apply)
+ pipeline = "https://github.com/nf-core/sarek"
+ post_run_script = <<-EOT
#!/bin/bash
echo "Workflow execution completed!"
# Add any cleanup commands here
EOT
+ pre_run_script = <<-EOT
#!/bin/bash
echo "Starting workflow execution..."
# Add any setup commands here
EOT
+ pull_latest = true
+ resume = true
+ resume_launch_id = (known after apply)
+ revision = "master"
+ run_name = (known after apply)
+ schema_name = (known after apply)
+ session_id = (known after apply)
+ stub_run = (known after apply)
+ tower_config = (known after apply)
+ user_secrets = (known after apply)
+ work_dir = "s3://shahbaz-test"
+ workspace_secrets = (known after apply)
}
+ message = (known after apply)
+ name = "terraform-hello-world-action"
+ source = "tower"
+ status = (known after apply)
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_aws_compute_env.aws_batch_compute_env_dedicated will be created
+ resource "seqera_aws_compute_env" "aws_batch_compute_env_dedicated" {
+ compute_env_id = (known after apply)
+ config = {
+ cli_path = (known after apply)
+ compute_job_role = (known after apply)
+ compute_queue = (known after apply)
+ dragen_instance_type = (known after apply)
+ dragen_queue = (known after apply)
+ environment = (known after apply)
+ execution_role = (known after apply)
+ forge = {
+ alloc_strategy = "BEST_FIT_PROGRESSIVE"
+ allow_buckets = (known after apply)
+ arm64_enabled = false
+ bid_percentage = (known after apply)
+ dispose_on_deletion = true
+ dragen_ami_id = (known after apply)
+ dragen_enabled = (known after apply)
+ dragen_instance_type = (known after apply)
+ ebs_auto_scale = false
+ ebs_block_size = (known after apply)
+ ebs_boot_size = (known after apply)
+ ec2_key_pair = (known after apply)
+ ecs_config = (known after apply)
+ efs_create = (known after apply)
+ efs_id = (known after apply)
+ efs_mount = (known after apply)
+ fargate_head_enabled = (known after apply)
+ fsx_mount = (known after apply)
+ fsx_name = (known after apply)
+ fsx_size = (known after apply)
+ fusion_enabled = (known after apply)
+ gpu_enabled = false
+ image_id = (known after apply)
+ instance_types = [
+ "m5.large",
+ "m5.xlarge",
+ "m5.2xlarge",
]
+ max_cpus = 1000
+ min_cpus = 0
+ security_groups = (known after apply)
+ subnets = (known after apply)
+ type = "EC2"
+ vpc_id = (known after apply)
}
+ fusion2_enabled = true
+ fusion_snapshots = (known after apply)
+ head_job_cpus = 2
+ head_job_memory_mb = 4096
+ head_job_role = (known after apply)
+ head_queue = (known after apply)
+ log_group = (known after apply)
+ lustre_id = (known after apply)
+ nextflow_config = (known after apply)
+ nvnme_storage_enabled = (known after apply)
+ post_run_script = (known after apply)
+ pre_run_script = (known after apply)
+ region = "us-east-1"
+ storage_type = (known after apply)
+ volumes = (known after apply)
+ wave_enabled = true
+ work_dir = "s3://shahbaz-test"
}
+ credentials_id = (known after apply)
+ date_created = (known after apply)
+ deleted = (known after apply)
+ last_updated = (known after apply)
+ last_used = (known after apply)
+ name = "aws-batch-dedicated"
+ org_id = (known after apply)
+ platform = "aws-batch"
+ status = (known after apply)
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_aws_credential.aws_credential will be created
+ resource "seqera_aws_credential" "aws_credential" {
+ credentials_id = (known after apply)
+ date_created = (known after apply)
+ deleted = (known after apply)
+ description = "AWS credentials generated by terraform update test"
+ keys = {
+ access_key = (sensitive value)
+ assume_role_arn = "arn:aws:iam::128997144437:role/TowerDevelopmentRole"
+ secret_key = (sensitive value)
}
+ last_updated = (known after apply)
+ last_used = (known after apply)
+ name = "test_aws_credential"
+ provider_type = "aws"
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_compute_env.aws_batch_compute_env will be created
+ resource "seqera_compute_env" "aws_batch_compute_env" {
+ compute_env = {
+ compute_env_id = (known after apply)
+ config = {
+ altair_platform = (known after apply)
+ aws_batch = {
+ cli_path = (known after apply)
+ compute_job_role = (known after apply)
+ compute_queue = (known after apply)
+ dragen_instance_type = (known after apply)
+ dragen_queue = (known after apply)
+ environment = (known after apply)
+ execution_role = (known after apply)
+ forge = {
+ alloc_strategy = "BEST_FIT_PROGRESSIVE"
+ allow_buckets = (known after apply)
+ arm64_enabled = false
+ bid_percentage = (known after apply)
+ dispose_on_deletion = true
+ dragen_ami_id = (known after apply)
+ dragen_enabled = (known after apply)
+ dragen_instance_type = (known after apply)
+ ebs_auto_scale = false
+ ebs_block_size = (known after apply)
+ ebs_boot_size = (known after apply)
+ ec2_key_pair = (known after apply)
+ ecs_config = (known after apply)
+ efs_create = (known after apply)
+ efs_id = (known after apply)
+ efs_mount = (known after apply)
+ fargate_head_enabled = (known after apply)
+ fsx_mount = (known after apply)
+ fsx_name = (known after apply)
+ fsx_size = (known after apply)
+ fusion_enabled = (known after apply)
+ gpu_enabled = false
+ image_id = (known after apply)
+ instance_types = [
+ "m5.large",
+ "m5.xlarge",
+ "m5.2xlarge",
]
+ max_cpus = 1000
+ min_cpus = 0
+ security_groups = (known after apply)
+ subnets = (known after apply)
+ type = "EC2"
+ vpc_id = (known after apply)
}
+ fusion2_enabled = true
+ fusion_snapshots = (known after apply)
+ head_job_cpus = 2
+ head_job_memory_mb = 4096
+ head_job_role = (known after apply)
+ head_queue = (known after apply)
+ log_group = (known after apply)
+ lustre_id = (known after apply)
+ nextflow_config = (known after apply)
+ nvnme_storage_enabled = (known after apply)
+ post_run_script = <<-EOT
#!/bin/bash
echo "Workflow execution completed!"
# Add any cleanup commands here
EOT
+ pre_run_script = <<-EOT
#!/bin/bash
echo "Starting workflow execution..."
# Add any setup commands here
EOT
+ region = "us-east-1"
+ storage_type = (known after apply)
+ volumes = (known after apply)
+ wave_enabled = true
+ work_dir = "s3://shahbaz-test"
}
+ aws_cloud = (known after apply)
+ azure_batch = (known after apply)
+ eks_platform = (known after apply)
+ gke_platform = (known after apply)
+ google_batch = (known after apply)
+ google_lifesciences = (known after apply)
+ k8s_platform = (known after apply)
+ lsf_platform = (known after apply)
+ moab_platform = (known after apply)
+ seqeracompute_platform = (known after apply)
+ slurm_platform = (known after apply)
+ uge_platform = (known after apply)
}
+ credentials_id = (known after apply)
+ date_created = (known after apply)
+ deleted = (known after apply)
+ description = "AWS Batch compute environment for bioinformatics workflows"
+ labels = (known after apply)
+ last_updated = (known after apply)
+ last_used = (known after apply)
+ managed_identity_id = (known after apply)
+ message = (known after apply)
+ name = "aws-batch-compute-env"
+ org_id = (known after apply)
+ platform = "aws-batch"
+ primary = (known after apply)
+ status = (known after apply)
+ workspace_id = (known after apply)
}
+ compute_env_id = (known after apply)
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_data_link.my_datalink will be created
+ resource "seqera_data_link" "my_datalink" {
+ credentials = (known after apply)
+ credentials_id = (known after apply)
+ data_link_id = (known after apply)
+ description = "data link created by Terraform"
+ hidden = (known after apply)
+ message = (known after apply)
+ name = "terraform-datalink"
+ provider_type = "aws"
+ public_accessible = false
+ region = (known after apply)
+ resource_ref = "s3://shahbaz-test"
+ status = (known after apply)
+ type = "bucket"
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_datasets.my_datasets will be created
+ resource "seqera_datasets" "my_datasets" {
+ date_created = (known after apply)
+ deleted = (known after apply)
+ description = "Terraform created dataset"
+ id = (known after apply)
+ last_updated = (known after apply)
+ media_type = (known after apply)
+ name = "terraform-dataset"
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_labels.my_labels will be created
+ resource "seqera_labels" "my_labels" {
+ is_default = false
+ label_id = (known after apply)
+ name = "terraform-test-label"
+ resource = true
+ value = "terraform-label-value"
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_pipeline.hello_world_minimal will be created
+ resource "seqera_pipeline" "hello_world_minimal" {
+ compute_env = (known after apply)
+ deleted = (known after apply)
+ description = "Hello world pipeline generated by terraform"
+ icon = (known after apply)
+ labels = (known after apply)
+ last_updated = (known after apply)
+ launch = {
+ compute_env_id = (known after apply)
+ config_profiles = []
+ head_job_cpus = 6
+ pipeline = "https://github.com/nextflow-io/hello"
+ pull_latest = true
+ resume = false
+ revision = "master"
+ work_dir = "s3://shahbaz-test"
}
+ name = "terraform-hello-world"
+ optimization_id = (known after apply)
+ optimization_status = (known after apply)
+ optimization_targets = (known after apply)
+ org_id = (known after apply)
+ org_name = (known after apply)
+ pipeline_id = (known after apply)
+ repository = (known after apply)
+ user_first_name = (known after apply)
+ user_id = (known after apply)
+ user_last_name = (known after apply)
+ user_name = (known after apply)
+ visibility = (known after apply)
+ workspace_id = (known after apply)
+ workspace_name = (known after apply)
}
# module.aws_batch.seqera_pipeline_secret.my_pipelinesecret will be created
+ resource "seqera_pipeline_secret" "my_pipelinesecret" {
+ date_created = (known after apply)
+ id = (known after apply)
+ last_updated = (known after apply)
+ last_used = (known after apply)
+ name = "test_terraform_secret"
+ secret_id = (known after apply)
+ value = (sensitive value)
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_primary_compute_env.my_primarycomputeenv will be created
+ resource "seqera_primary_compute_env" "my_primarycomputeenv" {
+ compute_env_id = (known after apply)
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_studios.my_datastudios will be created
+ resource "seqera_studios" "my_datastudios" {
+ active_connections = (known after apply)
+ base_image = (known after apply)
+ compute_env = (known after apply)
+ compute_env_id = (known after apply)
+ configuration = {
+ conda_environment = (known after apply)
+ cpu = (known after apply)
+ gpu = (known after apply)
+ lifespan_hours = (known after apply)
+ memory = (known after apply)
+ mount_data = (known after apply)
}
+ custom_image = (known after apply)
+ data_studio_tool_url = "public.cr.seqera.io/platform/data-studio-jupyter:4.2.5-0.8"
+ date_created = (known after apply)
+ description = "Data studio"
+ effective_lifespan_hours = (known after apply)
+ is_private = (known after apply)
+ labels = (known after apply)
+ last_started = (known after apply)
+ last_updated = (known after apply)
+ mounted_data_links = (known after apply)
+ name = "Terraform-Data-Studio"
+ parent_checkpoint = (known after apply)
+ progress = (known after apply)
+ session_id = (known after apply)
+ status_info = (known after apply)
+ studio_url = (known after apply)
+ template = (known after apply)
+ user = (known after apply)
+ wave_build_url = (known after apply)
+ workspace_id = (known after apply)
}
# module.aws_batch.seqera_workflows.my_workflows will be created
+ resource "seqera_workflows" "my_workflows" {
+ compute_env_id = (known after apply)
+ job_info = (known after apply)
+ labels = (known after apply)
+ messages = (known after apply)
+ optimized = (known after apply)
+ org_id = (known after apply)
+ org_name = (known after apply)
+ pipeline = "nextflow-io/hello"
+ platform = (known after apply)
+ progress = (known after apply)
+ work_dir = "s3://shahbaz-test"
+ workflow = (known after apply)
+ workflow_id = (known after apply)
+ workspace_id = (known after apply)
+ workspace_name = (known after apply)
}
# module.azure_batch.seqera_azure_credential.azure_credential will be created
+ resource "seqera_azure_credential" "azure_credential" {
+ credentials_id = (known after apply)
+ date_created = (known after apply)
+ deleted = (known after apply)
+ description = "azure credentials generated by terraform test"
+ keys = {
+ batch_name = (known after apply)
+ storage_name = (known after apply)
}
+ last_updated = (known after apply)
+ last_used = (known after apply)
+ name = "azure_credential"
+ provider_type = "azure"
+ workspace_id = (known after apply)
}
# module.azure_batch.seqera_compute_env.azure_batch_compute_env will be created
+ resource "seqera_compute_env" "azure_batch_compute_env" {
+ compute_env = {
+ compute_env_id = (known after apply)
+ config = {
+ altair_platform = (known after apply)
+ aws_batch = (known after apply)
+ aws_cloud = (known after apply)
+ azure_batch = {
+ auto_pool_mode = (known after apply)
+ delete_jobs_on_completion = (known after apply)
+ delete_pools_on_completion = (known after apply)
+ environment = (known after apply)
+ forge = {
+ auto_scale = (known after apply)
+ container_reg_ids = (known after apply)
+ dispose_on_deletion = (known after apply)
+ vm_count = 1
+ vm_type = (known after apply)
}
+ fusion2_enabled = (known after apply)
+ head_pool = (known after apply)
+ managed_identity_client_id = (known after apply)
+ nextflow_config = (known after apply)
+ post_run_script = (known after apply)
+ pre_run_script = (known after apply)
+ region = "eastus"
+ token_duration = (known after apply)
+ wave_enabled = (known after apply)
+ work_dir = "az://terraform-provider"
}
+ eks_platform = (known after apply)
+ gke_platform = (known after apply)
+ google_batch = (known after apply)
+ google_lifesciences = (known after apply)
+ k8s_platform = (known after apply)
+ lsf_platform = (known after apply)
+ moab_platform = (known after apply)
+ seqeracompute_platform = (known after apply)
+ slurm_platform = (known after apply)
+ uge_platform = (known after apply)
}
+ credentials_id = (known after apply)
+ date_created = (known after apply)
+ deleted = (known after apply)
+ description = "azure Batch compute environment"
+ labels = (known after apply)
+ last_updated = (known after apply)
+ last_used = (known after apply)
+ managed_identity_id = (known after apply)
+ message = (known after apply)
+ name = "azure-batch-compute-env-terraform"
+ org_id = (known after apply)
+ platform = "azure-batch"
+ primary = (known after apply)
+ status = (known after apply)
+ workspace_id = (known after apply)
}
+ compute_env_id = (known after apply)
+ workspace_id = (known after apply)
}
# module.azure_batch.seqera_pipeline.hello_world_minimal will be created
+ resource "seqera_pipeline" "hello_world_minimal" {
+ compute_env = (known after apply)
+ deleted = (known after apply)
+ description = "Hello world pipeline generated by terraform"
+ icon = (known after apply)
+ labels = (known after apply)
+ last_updated = (known after apply)
+ launch = {
+ compute_env_id = (known after apply)
+ config_profiles = []
+ head_job_cpus = 6
+ pipeline = "https://github.com/nextflow-io/hello"
+ pull_latest = true
+ resume = false
+ revision = "master"
+ work_dir = "az://terraform-provider"
}
+ name = "terraform-hello-world-azure"
+ optimization_id = (known after apply)
+ optimization_status = (known after apply)
+ optimization_targets = (known after apply)
+ org_id = (known after apply)
+ org_name = (known after apply)
+ pipeline_id = (known after apply)
+ repository = (known after apply)
+ user_first_name = (known after apply)
+ user_id = (known after apply)
+ user_last_name = (known after apply)
+ user_name = (known after apply)
+ visibility = (known after apply)
+ workspace_id = (known after apply)
+ workspace_name = (known after apply)
}
# module.azure_batch.seqera_workflows.my_workflows will be created
+ resource "seqera_workflows" "my_workflows" {
+ compute_env_id = (known after apply)
+ job_info = (known after apply)
+ labels = (known after apply)
+ messages = (known after apply)
+ optimized = (known after apply)
+ org_id = (known after apply)
+ org_name = (known after apply)
+ pipeline = "nextflow-io/hello"
+ platform = (known after apply)
+ progress = (known after apply)
+ work_dir = "az://terraform-provider"
+ workflow = (known after apply)
+ workflow_id = (known after apply)
+ workspace_id = (known after apply)
+ workspace_name = (known after apply)
}
Plan: 19 to add, 0 to change, 0 to destroy.Plan generated at: Wed Oct 29 09:05:15 UTC 2025 |
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Important
This Pull Request is unlikely ever to be merged.
The following is an example of adding a custom Nextflow datasource for the purposes of managing Nextflow configuration in HCL
This was intended to address some of the issues with parsing nextflow config in heredoc to give users of the terraform provider defining compute environments the ability to use HCL for linting and validation purposes.
Related context