> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mage.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Kubernetes executor

> Run Mage pipeline blocks in separate Kubernetes pods for scalability, isolation, and resource control.

export const ProOnly = ({button = 'Get started for free', description = 'Try our fully managed solution to access this advanced feature.', source = 'documentation', title = 'Only in Mage Pro.'}) => <div className="block my-4 px-5 py-4 overflow-hidden rounded-xl flex gap-3 border border-emerald-500/20 bg-emerald-50/50 dark:border-emerald-500/30 dark:bg-emerald-500/10">
    <div style={{
  display: 'flex',
  alignItems: 'center',
  width: '100%'
}}>
      <div className="text-sm prose min-w-0 text-emerald-900 dark:text-emerald-200" style={{
  flex: 1
}}>
        <span className="font-semibold">{title}</span>
        <p className="normal">{description}</p>
      </div>

      <div> </div>

      <div style={{
  height: 32,
  position: 'relative'
}}>
        <a target="_blank" rel="noopener noreferrer" className="group px-4 py-1.5 relative inline-flex items-center text-sm font-medium rounded-full" href={`https://cloud.mage.ai/sign-up?source=${source}`}>
          <span className="absolute inset-0 bg-primary-dark dark:bg-primary-light/10 border-primary-light/30 rounded-full dark:border group-hover:opacity-[0.9] dark:group-hover:border-primary-light/60">
          </span>

          <div className="mr-0.5 space-x-2.5 flex items-center">
            <span className="z-10 text-white dark:text-primary-light">
              {button}
            </span>

            <svg width="3" height="24" viewBox="0 -9 3 24" className="h-5 rotate-0 overflow-visible text-white/90 dark:text-primary-light">
              <path d="M0 0L3 3L0 6" fill="none" stroke="currentColor" strokeWidth="1.5" strokeLinecap="round"></path>
            </svg>
          </div>
        </a>
      </div>
    </div>
  </div>;

export const ProBanner = ({button = 'Try Mage Pro for free', description, source = 'documentation', title = 'Our fully managed solution for teams is now available!'}) => <a href={`https://cloud.mage.ai/sign-up?source=${source}`} className="block my-4 px-5 py-4 overflow-hidden rounded-xl flex gap-3 border border-emerald-500/20 bg-emerald-50/50 dark:border-emerald-500/30 dark:bg-emerald-500/10" target="_blank">
    <div style={{
  display: 'flex',
  alignItems: 'center',
  width: '100%'
}}>
      <div className="text-sm prose min-w-0 text-emerald-900 dark:text-emerald-200" style={{
  flex: 1
}}>
        {title}
        {description && <br />}
        {description && <p className="normal">{description}</p>}
      </div>

      <div> </div>

      <div>
        <ProButton label={button} href={`https://cloud.mage.ai/sign-up?source=${source}`} />
      </div>
    </div>
  </a>;

export const ProButton = ({href, label = 'Get started with Mage Pro for free', source = 'documentation'}) => <div style={{
  height: 32,
  position: 'relative'
}}>
    <a target="_blank" className="group px-4 py-1.5 relative inline-flex items-center text-sm font-medium rounded-full" href={href ?? `https://cloud.mage.ai/sign-up?source=${source}`}>
      <span className="absolute inset-0 bg-primary-dark dark:bg-primary-light/10 border-primary-light/30 rounded-full dark:border group-hover:opacity-[0.9] dark:group-hover:border-primary-light/60">
      </span>

      <div className="mr-0.5 space-x-2.5 flex items-center">
        <span class="z-10 text-white dark:text-primary-light">
          {label}
        </span>

        <svg width="3" height="24" viewBox="0 -9 3 24" class="h-5 rotate-0 overflow-visible text-white/90 dark:text-primary-light">
          <path d="M0 0L3 3L0 6" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"></path>
        </svg>
      </div>
    </a>
  </div>;

<ProBanner
  source="kubernetes-executor"
  title="Use Mage Pro if you need to run your blocks in separate pods or process while developing in the code editor."
  description={`
Running a block from the code editor, while developing a pipeline, won’t use this executor.
Instead, the block will be executed using the same process that the code editor is running on.
`}
/>

If your Mage app is deployed in a **Kubernetes cluster**, you can configure the **Kubernetes Executor** (`executor_type: k8s`) to run each block in its own Kubernetes pod.

**Defaults in Mage Pro:**

* Namespace: `default`
* **Job name format:** `mage-data-prep-block-{block_run_id}`

You can override these defaults at the **project level**, **pipeline level**, **block level**, or with a **full Kubernetes job template**.

## Basic Setup

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:

```yaml theme={"system"}
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.

```bash theme={"system"}
export KUBE_NAMESPACE=my-namespace
```

## Configuration Methods

You can configure Kubernetes Executor in several places. Mage builds the
effective config by deep-merging the less specific config first, then applying
more specific config on top:

1. Environment variable defaults and hardcoded defaults
2. Project-level `k8s_executor_config`
3. Pipeline-level `executor_config`
4. Block-level `executor_config`

For a normal block-level Kubernetes executor run, block settings win over
pipeline settings, pipeline settings win over project settings, and project
settings win over environment defaults. For `run_pipeline_in_one_process: true`,
Mage uses the pipeline-level Kubernetes executor and merges only project-level
and pipeline-level settings because the whole pipeline runs in one pod.

Most nested dictionaries are merged, so you can set broad defaults at the
project level and override only the keys that need to change at the pipeline or
block level. Resource requests and limits are applied to the job container after
the pod template is built, so `executor_config.resource_requests` and
`executor_config.resource_limits` take precedence over `container.resources`.

### 1. Environment Variable Defaults

Use environment variables to set default resource requests, resource limits, and
service account for all Kubernetes executor jobs. These values are only applied
when a more specific project, pipeline, or block config does not set the same
resource group.

```bash theme={"system"}
export K8S_EXECUTOR_DEFAULT_RESOURCE_REQUESTS_CPU=250m
export K8S_EXECUTOR_DEFAULT_RESOURCE_REQUESTS_MEMORY=512Mi
export K8S_EXECUTOR_DEFAULT_RESOURCE_LIMITS_CPU=500m
export K8S_EXECUTOR_DEFAULT_RESOURCE_LIMITS_MEMORY=1Gi
export K8S_EXECUTOR_DEFAULT_SERVICE_ACCOUNT_NAME=default
```

✅ **Use when:**
You want a cluster-wide fallback for jobs that do not define their own
Kubernetes resources.

### 2. Project-Level Configuration

Set `k8s_executor_config` in the project's `metadata.yaml` to apply defaults to
all blocks and pipeline runs that use the Kubernetes Executor in that project:

```yaml theme={"system"}
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
  # Node scheduling configurations
  pod:
    node_selector:
      node-type: gpu
      instance-type: c5.large
    scheduler_name: custom-scheduler
    tolerations:
      - key: "nvidia.com/gpu"
        operator: "Exists"
        effect: "NoSchedule"
    affinity:
      nodeAffinity:
        requiredDuringSchedulingIgnoredDuringExecution:
          nodeSelectorTerms:
            - matchExpressions:
                - key: "node-type"
                  operator: "In"
                  values: ["gpu", "compute"]
      podAntiAffinity:
        requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchLabels:
                app: mage-pipeline
            topologyKey: "kubernetes.io/hostname"
    volumes:
      - name: data-volume
        persistent_volume_claim:
          claim_name: mage-data-pvc
    image_pull_secrets: my-registry-secret
```

✅ **Use when:**
You want consistent Kubernetes settings across a project, such as default
CPU/RAM, namespace, service account, or node scheduling.

### 3. Pipeline-Level Configuration

Add `executor_type: k8s` and `executor_config` to the pipeline's
`metadata.yaml`. Pipeline-level config applies to the pipeline executor when
`run_pipeline_in_one_process: true`; it also acts as a shared default for block
runs in that pipeline before block-level overrides are applied.

```yaml theme={"system"}
executor_type: k8s
run_pipeline_in_one_process: true
executor_config:
  namespace: default
  resource_limits:
    cpu: 2000m
    memory: 4096Mi
  resource_requests:
    cpu: 1000m
    memory: 2048Mi
```

✅ **Use when:**
You want every block run in one pipeline to inherit the same Kubernetes config,
or you run the entire pipeline in one Kubernetes pod with
`run_pipeline_in_one_process: true`.

### 4. Block-Level Configuration

Add `executor_type: k8s` and `executor_config` to a block in the pipeline's
`metadata.yaml`:

```yaml theme={"system"}
blocks:
- uuid: example_data_loader
  type: data_loader
  executor_type: k8s
  executor_config:
    namespace: default
    resource_limits:
      cpu: 1000m
      memory: 2048Mi
    resource_requests:
      cpu: 500m
      memory: 1024Mi
```

✅ **Use when:**
You only want to:

* Run certain blocks in the Kubernetes executor

* Override the Kubernetes executor config for specific blocks

* 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`.
  You can interpolate the trigger name in the `job_name_prefix` field with the format `{trigger_name}`.

- **GPU Support:**
  ```yaml theme={"system"}
  k8s_executor_config:
    resource_limits:
      nvidia.com/gpu: 1  # request 1 GPU
    pod:
      node_selector:
        accelerator: nvidia-tesla-v100
      tolerations:
        - key: "nvidia.com/gpu"
          operator: "Exists"
          effect: "NoSchedule"
  ```
  Make sure [GPU device plugins](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/#using-device-plugins) are installed.

- **Custom container & job spec:**
  ```yaml theme={"system"}
  k8s_executor_config:
    container_config:
      image: mageai/mageai:0.9.7
      env:
      - name: USER_CODE_PATH
        value: /home/src/k8s_project
    job:
      active_deadline_seconds: 120
      backoff_limit: 3
      ttl_seconds_after_finished: 86400
  ```

✅ **Use when:**
You need specialized resources or scheduling for one expensive block without
changing the rest of the pipeline.

### 5. Full Kubernetes Job Template (maximum control)

Set the `K8S_CONFIG_FILE` environment variable to the path of a YAML
configuration file. This is the raw job template for Kubernetes executor jobs.
Use `K8S_DEFAULT_CONFIG_PATH` instead when you want a reusable policy that
merges with user config as described in [Default config file](#default-config-file).

**Example template:**

```yaml theme={"system"}
metadata:
  annotations:
    application: "mage"
    component: "executor"
  labels:
    application: "mage"
    type: "spark"
  namespace: "default"

pod:
  service_account_name: "mage-service-account"
  image_pull_secrets: "my-registry-secret"
  node_selector:
    node-type: "compute"
    instance-type: "c5.xlarge"
    zone: "us-west-1a"
  scheduler_name: "custom-scheduler"
  tolerations:
    - key: "nvidia.com/gpu"
      operator: "Exists"
      effect: "NoSchedule"
    - key: "spot-instance"
      operator: "Equal"
      value: "true"
      effect: "NoSchedule"
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
          - matchExpressions:
              - key: "node-type"
                operator: "In"
                values: ["compute", "gpu"]
              - key: "instance-type"
                operator: "In"
                values: ["c5.xlarge", "c5.2xlarge"]
      preferredDuringSchedulingIgnoredDuringExecution:
        - weight: 100
          preference:
            matchExpressions:
              - key: "zone"
                operator: "In"
                values: ["us-west-1a", "us-west-1b"]
    podAntiAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        - labelSelector:
            matchLabels:
              app: "mage-pipeline"
          topologyKey: "kubernetes.io/hostname"
  volumes:
  - name: data-pvc
    persistent_volume_claim:
      claim_name: mage-data-pvc
  - name: config-volume
    config_map:
      name: mage-config
  - name: secret-volume
    secret:
      secret_name: mage-secrets
  - name: empty-dir-volume
    empty_dir: {}

container:
  name: "mage-data"
  image: "mageai/mageai:latest"
  image_pull_policy: "IfNotPresent"
  env:
    - name: "KUBE_NAMESPACE"
      value: "default"
    - name: "secret_key"
      value: "somesecret"
  resources:
    limits:
      cpu: "1"
      memory: "1Gi"
      nvidia.com/gpu: 1
    requests:
      cpu: "0.1"
      memory: "0.5Gi"
      nvidia.com/gpu: 1
  volume_mounts:
    - mount_path: "/tmp/data"
      name: "data-pvc"
    - mount_path: "/etc/config"
      name: "config-volume"
    - mount_path: "/etc/secrets"
      name: "secret-volume"
      read_only: true
```

## Node Scheduling Configuration

### Node Selector

Use `node_selector` to schedule pods on specific nodes based on labels:

```yaml theme={"system"}
k8s_executor_config:
  pod:
    node_selector:
      node-type: "gpu"           # Schedule on GPU nodes
      instance-type: "c5.large"  # Schedule on specific instance types
      zone: "us-west-1a"         # Schedule in specific availability zones
```

### Tolerations

Use `tolerations` to allow pods to be scheduled on tainted nodes:

```yaml theme={"system"}
k8s_executor_config:
  pod:
    tolerations:
      - key: "nvidia.com/gpu"
        operator: "Exists"
        effect: "NoSchedule"
      - key: "spot-instance"
        operator: "Equal"
        value: "true"
        effect: "NoSchedule"
```

### Affinity Rules

Use `affinity` for advanced scheduling rules:

```yaml theme={"system"}
k8s_executor_config:
  pod:
    affinity:
      # Node affinity - prefer specific node types
      nodeAffinity:
        requiredDuringSchedulingIgnoredDuringExecution:
          nodeSelectorTerms:
            - matchExpressions:
                - key: "node-type"
                  operator: "In"
                  values: ["compute", "gpu"]
        preferredDuringSchedulingIgnoredDuringExecution:
          - weight: 100
            preference:
              matchExpressions:
                - key: "zone"
                  operator: "In"
                  values: ["us-west-1a", "us-west-1b"]

      # Pod anti-affinity - avoid co-locating similar pods
      podAntiAffinity:
        requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchLabels:
                app: "mage-pipeline"
            topologyKey: "kubernetes.io/hostname"
```

### Custom Scheduler

Use a custom scheduler for advanced scheduling logic:

```yaml theme={"system"}
k8s_executor_config:
  pod:
    scheduler_name: "custom-scheduler"
```

✅ **Use when:**
You need full control over pod specs, annotations, volume mounts, and other Kubernetes settings.

## Multi-Container Pods

If your Mage deployment runs in a **multi-container pod**, set the `MAGE_CONTAINER_NAME` environment variable to specify which container runs Mage:

```yaml theme={"system"}
env:
  - name: MAGE_CONTAINER_NAME
    valueFrom:
      fieldRef:
        fieldPath: metadata.name
```

If not set, Mage defaults to the **first container** in the pod.

## Default config file

<ProOnly source="kubernetes-executor" />

You can provide a **default K8s executor config file** that Mage reads from the path in `K8S_DEFAULT_CONFIG_PATH` environment variable. The file is loaded as a policy and merged with block-level and project-level config. Use this to enforce cluster-wide defaults (e.g. resource limits, service account) without editing every project.

**Helm chart:** When deploying **Mage Pro** with the [Mage Helm chart](https://github.com/mage-ai/helm-charts), configure the default via **`k8sExecutorConfig`** in `values.yaml`. The chart creates a ConfigMap from your config, mounts it into the control-plane (and workspace pods), and sets `K8S_DEFAULT_CONFIG_PATH` automatically. Do not set the env var manually when using the chart. See [Using Helm](/production/deploying-to-cloud/using-helm#default-workspace-and-k8s-executor-config) for details. For non-Helm deployments, set the env var and ensure the file exists at that path (e.g. via your own ConfigMap or volume mount).

**File format:**

```yaml theme={"system"}
enabled: true   # Set to false to disable this policy
enforce: true   # If true, policy overrides user config. If false, user config overrides.

resource_limits:
  cpu: "500m"
  memory: "1Gi"
resource_requests:
  cpu: "250m"
  memory: "512Mi"
service_account_name: default

pod:
  node_selector:
    node-type: compute
  tolerations: []
  volumes: []

container:
  env: []
  volume_mounts: []
```

**Policy fields:**

| Field          | Description                                                                                                                                                                                           |
| -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `enabled`      | If `false`, the policy is not applied.                                                                                                                                                                |
| `enforce`      | Controls how the default file is merged with **user config** (block + pipeline + project). See [Priority when merging](#priority-when-merging-default-file-with-user-config) below.                   |
| *(other keys)* | Same structure as [project-level k8s\_executor\_config](/production/executors/k8#2-project-level-configuration) (e.g. `resource_limits`, `resource_requests`, `pod`, `container`, `metadata`, `job`). |

The file must exist at the given path on the Mage control-plane. Use a ConfigMap or volume mount to provide it. When **workspaces** are created, only the K8s default config file is injected (volume + volume mount + env var) so workspace pods can read it; see [Workspaces](/guides/developer-ux/workspaces#default-workspace-config-file).

**Priority when merging default file with user config**

The default file is **merged** with the combined user config (block + pipeline + project). The **effective priority order** (who wins for a given key) depends on `enforce`:

| `enforce`   | Effective priority (highest → lowest)                                                                           | Result                                                                                                                                                                                                                                                                                                                     |
| ----------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **`true`**  | **1. Default config file** → 2. Block → 3. Pipeline → 4. Project → 5. Env vars → 6. Hardcoded                   | **Policy is highest priority.** Values from the default file override block, pipeline, and project for the same keys. Use this to enforce cluster-wide defaults (e.g. resource limits, service account) that users cannot override. Conflicting values can cause an error on update when strict conflict checking is used. |
| **`false`** | **1. Block** → 2. Pipeline → 3. Project → 4. Default config file (fills gaps only) → 5. Env vars → 6. Hardcoded | **User wins.** Block, pipeline, and project override the policy for the same keys. The policy only fills in keys that user config did not set. Use this to provide optional defaults while allowing per-block, per-pipeline, or per-project overrides.                                                                     |

So when **`enforce: true`**, the policy has the **highest** priority; when **`enforce: false`**, block, pipeline, and project config are higher and the policy only supplies missing values.

## Environment Variables Reference

### General Configuration

| Variable              | Purpose                                       | Default |
| --------------------- | --------------------------------------------- | ------- |
| `KUBE_NAMESPACE`      | Namespace for job execution                   | default |
| `K8S_CONFIG_FILE`     | Path to full Kubernetes job config file       | —       |
| `MAGE_CONTAINER_NAME` | Container to run Mage in multi-container pods | —       |

### Resource Usage Metrics

<ProOnly source="kubernetes-resource-usage" />

In Mage Pro, completed Kubernetes executor runs can store CPU and memory usage
summaries for the run detail **Resources** tab and the pipeline monitoring
dashboard. Mage uses Prometheus first when
`K8S_RESOURCE_USAGE_PROMETHEUS_URL` is configured; otherwise it uses the
Kubernetes Metrics API fallback when available.

Pipeline-level recommendations are available in Mage Pro after completed K8s
executor runs have stored CPU or memory samples. Mage calculates the suggested
`resource_requests` and `resource_limits` from observed usage, then shows the
values in the pipeline monitoring page so you can copy them into
`executor_config` or `k8s_executor_config`.

**How recommendations are calculated:**

| Resource | Basis                                                      | Request                  | Limit                           |
| -------- | ---------------------------------------------------------- | ------------------------ | ------------------------------- |
| CPU      | `p95_cores`, then `max_cores`, then latest `current_cores` | `max(basis * 0.75, 50m)` | `max(basis * 1.5, request * 2)` |
| Memory   | `p95_bytes`, then `max_bytes`, then latest `current_bytes` | `basis * 1.1`            | `basis * 1.3`                   |

Mage marks the recommendation as `low` confidence when the run has fewer than
three samples or an OOM warning, `medium` when enough samples exist from the
Kubernetes Metrics API fallback, and `high` when Prometheus provides at least
six samples. Prometheus samples are queried with
`K8S_RESOURCE_USAGE_PROMETHEUS_STEP_SECONDS` and Mage can wait once after a run
completes, controlled by
`K8S_RESOURCE_USAGE_PROMETHEUS_COMPLETION_DELAY_SECONDS`, before retrying sparse
completed runs. When unset, the completion delay defaults to
`K8S_RESOURCE_USAGE_PROMETHEUS_STEP_SECONDS`.

| Variable                                                 | Purpose                                                                  | Default                                      |
| -------------------------------------------------------- | ------------------------------------------------------------------------ | -------------------------------------------- |
| `K8S_RESOURCE_USAGE_PROMETHEUS_URL`                      | Prometheus base URL for K8s resource usage summaries                     | —                                            |
| `K8S_RESOURCE_USAGE_PROMETHEUS_BEARER_TOKEN`             | Bearer token for Prometheus requests                                     | —                                            |
| `K8S_RESOURCE_USAGE_PROMETHEUS_USERNAME`                 | Username for Prometheus basic authentication                             | —                                            |
| `K8S_RESOURCE_USAGE_PROMETHEUS_PASSWORD`                 | Password for Prometheus basic authentication                             | —                                            |
| `K8S_RESOURCE_USAGE_PROMETHEUS_TIMEOUT_SECONDS`          | Timeout for each Prometheus query                                        | `5`                                          |
| `K8S_RESOURCE_USAGE_PROMETHEUS_STEP_SECONDS`             | Query range step for CPU and memory samples                              | `15`                                         |
| `K8S_RESOURCE_USAGE_PROMETHEUS_COMPLETION_DELAY_SECONDS` | Delay before retrying Prometheus for completed runs with too few samples | `K8S_RESOURCE_USAGE_PROMETHEUS_STEP_SECONDS` |
| `K8S_RESOURCE_USAGE_PROMETHEUS_VERIFY_TLS`               | Set to `0`, `false`, `no`, or `off` to skip TLS certificate verification | `true`                                       |

See [Monitoring with Prometheus](/observability/external/prometheus#kubernetes-resource-usage-in-mage-pro) for Prometheus query behavior and authentication details.

### Default Resource Configuration

<ProOnly source="kubernetes-executor" />

These environment variables set default resource limits and requests for all K8s executor jobs.
They are applied when a project-level, pipeline-level, or block-level configuration does not
specify resource requests or limits.

| Variable                                        | Purpose                                   | Example Value |
| ----------------------------------------------- | ----------------------------------------- | ------------- |
| `K8S_EXECUTOR_DEFAULT_RESOURCE_REQUESTS_CPU`    | Default CPU request for executor jobs     | `250m`        |
| `K8S_EXECUTOR_DEFAULT_RESOURCE_REQUESTS_MEMORY` | Default memory request for executor jobs  | `512Mi`       |
| `K8S_EXECUTOR_DEFAULT_RESOURCE_LIMITS_CPU`      | Default CPU limit for executor jobs       | `500m`        |
| `K8S_EXECUTOR_DEFAULT_RESOURCE_LIMITS_MEMORY`   | Default memory limit for executor jobs    | `1Gi`         |
| `K8S_EXECUTOR_DEFAULT_SERVICE_ACCOUNT_NAME`     | Default service account for executor jobs | `default`     |

**Priority order without a default config file** (highest to lowest):

1. Block-level `executor_config` in the pipeline's `metadata.yaml`
2. Pipeline-level `executor_config` in the pipeline's `metadata.yaml`
3. Project-level `k8s_executor_config` in the project's `metadata.yaml`
4. Environment variable defaults (listed above)
5. Hardcoded defaults

When Mage Pro resource usage recommendations suggest new Kubernetes
`resource_requests` or `resource_limits`, apply the values at the narrowest
level you want to tune: block-level for one block, pipeline-level for all runs
of one pipeline, or project-level for all Kubernetes executor jobs in the
project.