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Follow the instructions in this document to deploy Mage in a production environment. When running Mage in production, you can customize the compute resource for the following services:
  1. Front-end application
  2. Pipeline executor
  3. Block executor

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Front-end application

Customize the compute resource of the Mage web service. The Mage web service is responsible for running Mage web backend, scheduler service and local block executions. You can customize the CPU and memory of the Mage web service by updating the Terraform variables and then running terraform apply.

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Amazon Web Services (AWS)

Update the ecs_task_cpu and ecs_task_memory variables in the mage-ai-terraform-templates/aws/variables.tf file.

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Google Cloud Platform (GCP)

Update the container_cpu and container_memory variables in the mage-ai-terraform-templates/gcp/variables.tf file.

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Pipeline executor

Set the pipeline’s executor type to customize the compute resources for pipeline runs. Here are the available executor types:
  • local_python
  • k8s
  • ecs

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Default for all blocks in a pipeline

You can set the executor type for all the blocks in a pipeline by specifying the executor_type at pipeline level.

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Block executor

Mage provides multiple executors to execute blocks. Here are the available executor types:
  • local_python
  • k8s
  • ecs
  • azure_container_instance
  • gcp_cloud_run

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Default for all blocks

Mage uses local_python executor type by default. If you want to specify another executor_type as the default executor type for blocks, you can set the environment variable DEFAULT_EXECUTOR_TYPE to one executor type mentioned above.