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Sep 2, 2025
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chore: Update discovery artifacts #2650

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0992ab1
chore: update docs/dyn/index.md
yoshi-automation Sep 2, 2025
505fe57
feat(aiplatform): update the api
yoshi-automation Sep 2, 2025
35758c6
feat(androidmanagement): update the api
yoshi-automation Sep 2, 2025
7af3687
feat(backupdr): update the api
yoshi-automation Sep 2, 2025
c9bb6ea
feat(bigquery): update the api
yoshi-automation Sep 2, 2025
8866a0a
feat(contactcenteraiplatform): update the api
yoshi-automation Sep 2, 2025
5ba402c
feat(contactcenterinsights): update the api
yoshi-automation Sep 2, 2025
bbaa573
feat(container): update the api
yoshi-automation Sep 2, 2025
a6bcada
feat(dataplex): update the api
yoshi-automation Sep 2, 2025
4550300
feat(deploymentmanager): update the api
yoshi-automation Sep 2, 2025
3a419d6
feat(dfareporting): update the api
yoshi-automation Sep 2, 2025
643b0de
feat(discoveryengine): update the api
yoshi-automation Sep 2, 2025
aa058d0
fix(dlp): update the api
yoshi-automation Sep 2, 2025
a39534d
feat(file): update the api
yoshi-automation Sep 2, 2025
922fbb5
feat(firebaseappdistribution): update the api
yoshi-automation Sep 2, 2025
4b3e905
feat(logging): update the api
yoshi-automation Sep 2, 2025
b918b97
feat(looker): update the api
yoshi-automation Sep 2, 2025
9dd4525
feat(memcache): update the api
yoshi-automation Sep 2, 2025
c646b13
feat(merchantapi): update the api
yoshi-automation Sep 2, 2025
e9b345d
feat(networkconnectivity): update the api
yoshi-automation Sep 2, 2025
692c1d4
feat(networksecurity): update the api
yoshi-automation Sep 2, 2025
0496df5
feat(observability): update the api
yoshi-automation Sep 2, 2025
b49ae19
feat(playintegrity): update the api
yoshi-automation Sep 2, 2025
d114f55
feat(redis): update the api
yoshi-automation Sep 2, 2025
047a2e6
feat(sqladmin): update the api
yoshi-automation Sep 2, 2025
1c03523
feat(vmmigration): update the api
yoshi-automation Sep 2, 2025
a0d3161
feat(workloadmanager): update the api
yoshi-automation Sep 2, 2025
76da8fc
chore(docs): Add new discovery artifacts and artifacts with minor upd…
yoshi-automation Sep 2, 2025
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405 changes: 404 additions & 1 deletion docs/dyn/aiplatform_v1.projects.locations.html
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4 changes: 4 additions & 0 deletions docs/dyn/aiplatform_v1beta1.batchPredictionJobs.html
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Expand Up @@ -118,6 +118,7 @@ <h3>Method Details</h3>
&quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
&quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
&quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
&quot;gpuPartitionSize&quot;: &quot;A String&quot;, # 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=&quot;1g.10gb&quot;, 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.
&quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/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.
&quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
&quot;reservationAffinity&quot;: { # 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. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
Expand Down Expand Up @@ -612,6 +613,7 @@ <h3>Method Details</h3>
&quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
&quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
&quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
&quot;gpuPartitionSize&quot;: &quot;A String&quot;, # 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=&quot;1g.10gb&quot;, 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.
&quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/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.
&quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
&quot;reservationAffinity&quot;: { # 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. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
Expand Down Expand Up @@ -1112,6 +1114,7 @@ <h3>Method Details</h3>
&quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
&quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
&quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
&quot;gpuPartitionSize&quot;: &quot;A String&quot;, # 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=&quot;1g.10gb&quot;, 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.
&quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/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.
&quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
&quot;reservationAffinity&quot;: { # 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. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
Expand Down Expand Up @@ -1618,6 +1621,7 @@ <h3>Method Details</h3>
&quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
&quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
&quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
&quot;gpuPartitionSize&quot;: &quot;A String&quot;, # 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=&quot;1g.10gb&quot;, 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.
&quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/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.
&quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
&quot;reservationAffinity&quot;: { # 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. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
Expand Down
4 changes: 4 additions & 0 deletions docs/dyn/aiplatform_v1beta1.projects.locations.batchPredictionJobs.html
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Expand Up @@ -149,6 +149,7 @@ <h3>Method Details</h3>
&quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
&quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
&quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
&quot;gpuPartitionSize&quot;: &quot;A String&quot;, # 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=&quot;1g.10gb&quot;, 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.
&quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/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.
&quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
&quot;reservationAffinity&quot;: { # 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. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
Expand Down Expand Up @@ -642,6 +643,7 @@ <h3>Method Details</h3>
&quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
&quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
&quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
&quot;gpuPartitionSize&quot;: &quot;A String&quot;, # 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=&quot;1g.10gb&quot;, 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.
&quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/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.
&quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
&quot;reservationAffinity&quot;: { # 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. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
Expand Down Expand Up @@ -1177,6 +1179,7 @@ <h3>Method Details</h3>
&quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
&quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
&quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
&quot;gpuPartitionSize&quot;: &quot;A String&quot;, # 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=&quot;1g.10gb&quot;, 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.
&quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/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.
&quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
&quot;reservationAffinity&quot;: { # 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. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
Expand Down Expand Up @@ -1683,6 +1686,7 @@ <h3>Method Details</h3>
&quot;machineSpec&quot;: { # Specification of a single machine. # Required. Immutable. The specification of a single machine.
&quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
&quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
&quot;gpuPartitionSize&quot;: &quot;A String&quot;, # 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=&quot;1g.10gb&quot;, 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.
&quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/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.
&quot;multihostGpuNodeCount&quot;: 42, # Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
&quot;reservationAffinity&quot;: { # 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. # Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
Expand Down
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