--- title: "PATCH /v1/projects/{projectsId}/locations/{locationsId}/deploymentResourcePools/{deploymentResourcePoolsId}" method: PATCH path: "/v1/projects/{projectsId}/locations/{locationsId}/deploymentResourcePools/{deploymentResourcePoolsId}" tags: ["projects"] --- # PATCH /v1/projects/{projectsId}/locations/{locationsId}/deploymentResourcePools/{deploymentResourcePoolsId} `PATCH /v1/projects/{projectsId}/locations/{locationsId}/deploymentResourcePools/{deploymentResourcePoolsId}` ## Request - Base URL: `https://aiplatform.googleapis.com` - URL: `https://aiplatform.googleapis.com/v1/projects/{projectsId}/locations/{locationsId}/deploymentResourcePools/{deploymentResourcePoolsId}` - Auth: OAuth 2 and OAuth 2 - Implicit: `https://accounts.google.com/o/oauth2/auth` - Scopes: `https://www.googleapis.com/auth/cloud-platform` - Authorization code: `https://accounts.google.com/o/oauth2/auth` - Token URL: `https://accounts.google.com/o/oauth2/token` scopes: `https://www.googleapis.com/auth/cloud-platform` Update a DeploymentResourcePool. ## Path parameters - `projectsId` string, required - `locationsId` string, required - `deploymentResourcePoolsId` string, required ## Query parameters - `updateMask` string, google-fieldmask ## Request body - GoogleCloudAiplatformV1DeploymentResourcePool: A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources. - `createTime` string, google-datetime: Output only. Timestamp when this DeploymentResourcePool was created. - `dedicatedResources` GoogleCloudAiplatformV1DedicatedResources: A description of resources that are dedicated to a DeployedModel or DeployedIndex, and that need a higher degree of manual configuration. - `autoscalingMetricSpecs` GoogleCloudAiplatformV1AutoscalingMetricSpec[]: Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`. - `metricName` string: Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization` * `aiplatform.googleapis.com/prediction/online/request_count` * `pubsub.googleapis.com/subscription/num_undelivered_messages` * `prometheus.googleapis.com/vertex_dcgm_fi_dev_gpu_util` * `prometheus.googleapis.com/vertex_vllm_gpu_cache_usage_perc` * `prometheus.googleapis.com/vertex_vllm_num_requests_waiting` - `target` integer: The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided. - `machineSpec` GoogleCloudAiplatformV1MachineSpec: Specification of a single machine. - `acceleratorCount` integer: The number of accelerators to attach to the machine. For [accelerator optimized machine types](https://cloud.google.com/compute/docs/accelerator-optimized-machines), One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled. - `acceleratorType` 'ACCELERATOR_TYPE_UNSPECIFIED' | 'NVIDIA_TESLA_K80' | 'NVIDIA_TESLA_P100' | 'NVIDIA_TESLA_V100' | 'NVIDIA_TESLA_P4' | 'NVIDIA_TESLA_T4' | 'NVIDIA_TESLA_A100' | 'NVIDIA_A100_80GB' | 'NVIDIA_L4' | 'NVIDIA_H100_80GB' | 'NVIDIA_H100_MEGA_80GB' | 'NVIDIA_H200_141GB' | 'NVIDIA_B200' | 'NVIDIA_GB200' | 'NVIDIA_RTX_PRO_6000' | 'TPU_V2' | 'TPU_V3' | 'TPU_V4_POD' | 'TPU_V5_LITEPOD': Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count. - `gpuPartitionSize` string: Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to [Nvidia GPU Partitioning](https://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi#multi-instance_gpu_partitions) for the available partition sizes. If set, the accelerator_count should be set to 1. - `machineType` string: Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/gemini-enterprise-agent-platform/machine-learning/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - `reservationAffinity` GoogleCloudAiplatformV1ReservationAffinity: A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity. - `key` string: Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use `compute.googleapis.com/reservation-name` as the key and specify the name of your reservation as its value. - `reservationAffinityType` 'TYPE_UNSPECIFIED' | 'NO_RESERVATION' | 'ANY_RESERVATION' | 'SPECIFIC_RESERVATION': Required. Specifies the reservation affinity type. - `values` string[]: Optional. Corresponds to the label values of a reservation resource. This must be the resource name of the reservation, reservation block, or reservation sub- block. - `tpuTopology` string: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1"). - `maxReplicaCount` integer: Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type). - `minReplicaCount` integer: Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. - `requiredReplicaCount` integer: Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count. - `spot` boolean: Optional. If true, schedule the deployment workload on [spot VMs](https://cloud.google.com/kubernetes-engine/docs/concepts/spot-vms). - `disableContainerLogging` boolean: If the DeploymentResourcePool is deployed with custom-trained Models or AutoML Tabular Models, the container(s) of the DeploymentResourcePool will send `stderr` and `stdout` streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to [Cloud Logging pricing](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true. - `encryptionSpec` GoogleCloudAiplatformV1EncryptionSpec: Represents a customer-managed encryption key specification that can be applied to a Vertex AI resource. - `kmsKeyName` string: Required. Resource name of the Cloud KMS key used to protect the resource. The Cloud KMS key must be in the same region as the resource. It must have the format `projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}`. - `name` string: Immutable. The resource name of the DeploymentResourcePool. Format: `projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}` - `satisfiesPzi` boolean: Output only. Reserved for future use. - `satisfiesPzs` boolean: Output only. Reserved for future use. - `serviceAccount` string: The service account that the DeploymentResourcePool's container(s) run as. Specify the email address of the service account. If this service account is not specified, the container(s) run as a service account that doesn't have access to the resource project. Users deploying the Models to this DeploymentResourcePool must have the `iam.serviceAccounts.actAs` permission on this service account. ## Response `200` Successful response - GoogleLongrunningOperation: This resource represents a long-running operation that is the result of a network API call. - `done` boolean: If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. - `error` GoogleRpcStatus: The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). - `code` integer: The status code, which should be an enum value of google.rpc.Code. - `details` object[]: A list of messages that carry the error details. There is a common set of message types for APIs to use. - `message` string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. - `metadata` object: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. - `name` string: The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. - `response` object: The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. --- [API](https://skmtc.dev/google/apis/aiplatform.md) - [All operations](https://skmtc.dev/google/apis/aiplatform/llms.txt) - [OpenAPI document](https://skmtc.dev/google/apis/aiplatform/revisions/3aca3b348f0c?raw)