Compute resources

Follow the instructions in this doc to deploy Mage tool to production environment. When running the Mage tool in production, you can customize the compute resource in the following ways:

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1. Customize the compute resource of the Mage web service

Mage web serivce is responsbile 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

• AWS: Update the ecs_task_cpu and ecs_task_memory variables in the mage-ai-terraform-templates/aws/variables.tf file.
• GCP: Update the container_cpu and container_memory variables in the mage-ai-terraform-templates/gcp/variables.tf file.

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2. Set executor type and customize the compute resource of the Mage executor

Mage provides multiple executors to execute blocks. Here are the available executor types:

• Block executor
  • local_python
  • ecs
  • gcp_cloud_run
  • azure_container_instance
  • k8s
• Pipeline executor
  • local_python
  • ecs
  • k8s

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.

If you want to use local_python executor when DEFAULT_EXECUTOR_TYPE is set to another executor type, you can set the executor_type to local_python_force.

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Local python executor

Local python exeuctors are running within the same container of Mage scheduler service. You can customize the compute resource with the same way mentioned in the Customize the compute resource of the Mage web service section.

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Kubernetes executor

If your Mage app is running in a Kubernetes cluster, you can execute the blocks in separate Kubernetes pods with Kubernetes executor.

To configure a pipeline block to use Kubernetes executor, you simply just need to update the executor_type of the block to k8s in pipeline’s metadata.yaml:

blocks:
- uuid: example_data_loader
  type: data_loader
  upstream_blocks: []
  downstream_blocks: []
  executor_type: k8s
  ...

By default, Mage uses default as the Kubernetes namespace. You can customize the namespace by setting the KUBE_NAMESPACE environment variable.

There’re three ways to customize the Kubernetes executor config:

1. Add the executor_config at block level in pipeline’s metadata.yaml file. Example config:

   blocks:
   - uuid: example_data_loader
     type: data_loader
     upstream_blocks: []
     downstream_blocks: []
     executor_type: k8s
     executor_config:
       namespace: default
       resource_limits:
         cpu: 1000m
         memory: 2048Mi
       resource_requests:
         cpu: 500m
         memory: 1024Mi
   

2. Add the k8s_executor_config to project’s metadata.yaml. This k8s_executor_config will apply to all the blocks that use k8s executor in this project. Example config:

   k8s_executor_config:
     job_name_prefix: data-prep
     namespace: default
     resource_limits:
       cpu: 1000m
       memory: 2048Mi
     resource_requests:
       cpu: 500m
       memory: 1024Mi
     service_account_name: default
   

• The kubernetes job name is in this format: mage-{job_name_prefix}-block-{block_run_id}. The default job_name_prefix is data-prep. You can customize it in the k8s executor config.
• If you want to use GPU resource in your k8s executor, you can configure the GPU resource in the k8s_executor_config like

  k8s_executor_config:
    resource_limits:
      gpu-vendor.example/example-gpu: 1 # requesting 1 GPU
  

  Please make sure the GPU driver is installed and run on your nodes to use the GPUs.
• To futher customize the container config of the kubernetes executor, you can sepcify the container_config in the k8s executor config. Here is the example:

  k8s_executor_config:
    container_config:
      image: mageai/mageai:0.9.7
      env:
      - name: USER_CODE_PATH
        value: /home/src/k8s_project
  

3. You can configure the job template by using the K8S_CONFIG_FILE environment variable, which should point to the configuration file. Here is the format for the Kubernetes configuration template:

# Kubernetes Configuration Template
metadata:
  annotations:
    application: "mage"
    composant: "executor"
  labels:
    application: "mage"
    type: "spark"
  namespace: "default"
pod:
  service_account_name: ""
  image_pull_secrets: "secret"
  volumes:
  - name: data-pvc
    persistent_volume_claim:
      claim_name: pvc-name
container:
  name: "mage-data"
  env:
    - name: "KUBE_NAMESPACE"
      value: "default"
    - name: "secret_key"
      value: "somesecret"
  image: "mageai/mageai:latest"
  image_pull_policy: "IfNotPresent"
  resources:
    limits:
      cpu: "1"
      memory: "1Gi"
    requests:
      cpu: "0.1"
      memory: "0.5Gi"
  volume_mounts:
    - mount_path: "/tmp/data"
      name: "data-pvc"

NB: When deploying Mage in a multi-container pod, you need to specify the environment variable MAGE_CONTAINER_NAME. If this variable is not set, Mage will default to using the first container in the pod. To specify the Mage container, you can use:

    env:
      - name: MAGE_CONTAINER_NAME
        valueFrom:
          fieldRef:
            fieldPath: metadata.name

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AWS ECS executor

You can choose to launch separate AWS ECS tasks to executor blocks by specifying block executor_type to be ecs in pipeline’s metadata.yaml file.

There’re 2 ways to customize the compute resource of ECS executor,

1. Update cpu and memory the ecs_config in project’s metadata.yaml file. Example config:

   ecs_config:
     cpu: 1024
     memory: 2048
   

2. Add the executor_config at block level in pipeline’s metadata.yaml file. Example config:

   blocks:
   - uuid: example_data_loader
     type: data_loader
     upstream_blocks: []
     downstream_blocks: []
     executor_type: ecs
     executor_config:
       cpu: 1024
       memory: 2048
   

To run the whole pipeline in one ECS executor, you can set the executor_type at pipeline level and set run_pipeline_in_one_process to true. executor_config can also be set at pipeline level. Here is the example pipeline metadata.yaml:

blocks:
- ...
- ...
executor_type: ecs
run_pipeline_in_one_process: true
name: example_pipeline
...

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Extra fields

Example config

ecs_config:
  cpu: 1024
  memory: 2048
  assign_public_ip: false
  enable_execute_command: true
  wait_timeout: 1200

Required IAM permissions for using ECS executor:

[
  "ec2:DescribeNetworkInterfaces",
  "ecs:DescribeTasks",
  "ecs:ListTasks",
  "ecs:RunTask"
]

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GCP Cloud Run executor

If your Mage app is deployed on GCP Cloud Run, you can choose to launch separate GCP Cloud Run jobs to execute blocks.

How to configure pipeline to use GCP cloud run executor:

1. Update Project’s metadata.yaml

gcp_cloud_run_config:
  path_to_credentials_json_file: "/path/to/credentials_json_file"
  project_id: project_id
  timeout_seconds: 600

2. Update the executor_type of block to gcp_cloud_run in pipeline’s metadata.yaml:

blocks:
- uuid: example_data_loader
  type: data_loader
  upstream_blocks: []
  downstream_blocks: []
  executor_type: gcp_cloud_run
  ...

Customizing compute resource for GCP Cloud Run executor is coming soon.

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Azure Container Instance executor

If your Mage app is deployed on Microsoft Azure with Mage’s terraform scripts, you can choose to launch separate Azure containce instances to execute blocks.

How to configure pipeline to use Azure Container Instance executor:

1. Update Project’s metadata.yaml

azure_container_instance_config:
  cpu: 1
  memory: 2

2. Update the executor_type of the block to azure_container_instance in pipeline’s metadata.yaml and specify executor_config optionally. The block level executor_config will override the global executor_config.

blocks:
- uuid: example_data_loader
  type: data_loader
  upstream_blocks: []
  downstream_blocks: []
  executor_type: azure_container_instance
  executor_config:
    cpu: 1
    memory: 2
  ...

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PySpark executor

If the pipeline type is “pyspark”, we use PySpark exeuctors for pipeline and block executions. You can customize the compute resource of PySpark exeuctor by updating the instance types of emr_config in project’s metadata.yaml file.

Example config:

emr_config:
  ec2_key_name: "xxxxx"
  master_instance_type: "r5.2xlarge"
  slave_instance_type: "r5.2xlarge"