Runtime settings¶

A running Workload's configuration is mutable, but the mutations split across two endpoints with different semantics. Identity fields—name, description, importance—update in place via PATCH /workloads/{id}. Runtime and resource changes—replica count, autoscaling, CPU, memory, GPU, and bundle selection—go through PATCH /workloads/{id}/settings and queue a rolling replacement so the Workload stays available during the swap. The sections that follow cover the fields exposed at each layer and the PATCH body shapes.

Importance and metadata¶

Use PATCH /workloads/{id} to update name, description, and importance. The importance field is mutable on a running Workload:

curl -X PATCH "${DATAROBOT_ENDPOINT}/workloads/${WORKLOAD_ID}" \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{"importance": "critical"}' 

Runtime, autoscaling, and replicas¶

Update a Workload's runtime configuration (replica count, autoscaling policies, per-container resource allocation, resource bundles) with PATCH /workloads/{workload_id}/settings. The PATCH returns a Replacement (HTTP 202) and queues a rolling replacement onto the new runtime; see Replace and roll out.

All runtime fields are nested under runtime.containerGroups[], with each entry matched to the artifact's container group by name:

# Fixed scaling
curl -X PATCH "${DATAROBOT_ENDPOINT}/workloads/${WORKLOAD_ID}/settings" \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "runtime": {
      "containerGroups": [{
        "name": "default",
        "replicaCount": 5
      }]
    }
  }'

# Dynamic scaling
curl -X PATCH "${DATAROBOT_ENDPOINT}/workloads/${WORKLOAD_ID}/settings" \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "runtime": {
      "containerGroups": [{
        "name": "default",
        "autoscaling": {
          "enabled": true,
          "minReplicaCount": 2,
          "maxReplicaCount": 10,
          "policies": [{
            "scalingMetric": "httpRequestsConcurrency",
            "target": 20
          }]
        }
      }]
    }
  }' 

You can also set autoscaling policies at Workload creation time inside the initial POST /workloads/ payload—see Tutorial: Deploy a production-ready container.

Scaling metrics¶

autoscalingオブジェクトは、policiesと同じレベルでminReplicaCountおよびmaxReplicaCountを受け付けます。これらは、定義されているポリシーの数に関係なく、ワークロードのレプリカの上限と下限を設定します。 各AutoscalingPolicyは、scalingMetricとtargetのみを設定します。 maxReplicaCountは1以上である必要があります。また、minReplicaCountはmaxReplicaCount以下である必要があります。 カスタムメトリクス名（NIM 2.0のみ）は、Prometheus/OpenMetricsの命名規則に従います。最大63文字で、[a-zA-Z_:][a-zA-Z0-9_:]*というパターンに一致する必要があります。

ゼロスケーリングの例については、同じ呼び出しでオートスケーリングを設定するを参照してください。 The Console maps the same values in Configure autoscaling.

Resource allocation and bundles¶

All resource configuration is runtime-side—the artifact describes container topology only and carries no CPU, memory, or GPU fields. The runtime declares resources at two layers:

ワークロードで使用可能なリソースバンドルIDを一覧表示するには、useCases=workloadを指定してGET /mlops/compute/bundles/にクエリーを実行します。

curl -s "${DATAROBOT_ENDPOINT}/mlops/compute/bundles/?useCases=workload" \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" 

このクエリーによって返されるバンドルのみが、runtime.containerGroups[].resourceBundlesで有効な値となります。

memory accepts either a human-readable string with one of B, KB, MB, GB (1000-based—for example, "4GB", "512MB") or a raw byte integer. Kubernetes形式のバイナリサフィックス（Mi、Gi）はサポートされておらず、検証エラーが返されます。 cpu has a minimum of 0.1. The gpu field controls GPU count only; GPU model and VRAM are determined entirely by the selected resource bundle—there is no per-container GPU type or VRAM field. Each override's name follows DNS-label syntax (lowercase letters, digits, and hyphens; must start with a letter and end with a letter or digit; up to 63 characters) and must match a container declared in the artifact group.

Set both layers on the Workload's runtime. Changing them on a running Workload via PATCH /workloads/{workload_id}/settings queues a rolling replacement using the strategies in Replace and roll out.

curl -X PATCH "${DATAROBOT_ENDPOINT}/workloads/${WORKLOAD_ID}/settings" \
  -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "runtime": {
      "containerGroups": [{
        "name": "default",
        "resourceBundles": ["cpu.medium"],
        "containers": [{
          "name": "agent",
          "resourceAllocation": {"cpu": 2, "memory": "4GB"}
        }]
      }]
    }
  }'