gcloud ai endpoints deploy-model

NAME: gcloud ai endpoints deploy-model - deploy a model to an existing Vertex AI endpoint
SYNOPSIS: gcloud ai endpoints deploy-model (ENDPOINT : --region=REGION) --display-name=DISPLAY_NAME --model=MODEL [--accelerator=[count=COUNT],[type=TYPE]] [--autoscaling-metric-specs=[METRIC-NAME=TARGET,…]] [--deployed-model-id=DEPLOYED_MODEL_ID] [--disable-container-logging] [--enable-access-logging] [--machine-type=MACHINE_TYPE] [--max-replica-count=MAX_REPLICA_COUNT] [--min-replica-count=MIN_REPLICA_COUNT] [--reservation-affinity=[key=KEY],[reservation-affinity-type=RESERVATION-AFFINITY-TYPE],[values=VALUES]] [--service-account=SERVICE_ACCOUNT] [--spot] [--traffic-split=[DEPLOYED_MODEL_ID=VALUE,…]] [GCLOUD_WIDE_FLAG …]
EXAMPLES: To deploy a model 456 to an endpoint 123 under project example in region us-central1, run:
gcloud ai endpoints deploy-model 123 --project=example --region=us-central1 --model=456 --display-name=my_deployed_model
POSITIONAL ARGUMENTS: Endpoint resource - The endpoint to deploy a model to. The arguments in this group can be used to specify the attributes of this resource. (NOTE) Some attributes are not given arguments in this group but can be set in other ways.
To set the project attribute:

provide the argument endpoint on the command line with a fully specified name;

provide the argument --project on the command line;

set the property core/project.

This must be specified.

ENDPOINT

ID of the endpoint or fully qualified identifier for the endpoint.
To set the name attribute:

provide the argument endpoint on the command line.

This positional argument must be specified if any of the other arguments in this group are specified.

--region=REGION

Cloud region for the endpoint.
To set the region attribute:

provide the argument endpoint on the command line with a fully specified name;

provide the argument --region on the command line;

set the property ai/region;

choose one from the prompted list of available regions.
REQUIRED FLAGS: --display-name=DISPLAY_NAME

Display name of the deployed model.

--model=MODEL

Id of the uploaded model.
OPTIONAL FLAGS: --accelerator=[count=COUNT],[type=TYPE]

Manage the accelerator config for GPU serving. When deploying a model with Compute Engine Machine Types, a GPU accelerator may also be selected.

type

The type of the accelerator. Choices are 'nvidia-a100-80gb', 'nvidia-h100-80gb', 'nvidia-l4', 'nvidia-tesla-a100', 'nvidia-tesla-k80', 'nvidia-tesla-p100', 'nvidia-tesla-p4', 'nvidia-tesla-t4', 'nvidia-tesla-v100'.

count

The number of accelerators to attach to each machine running the job. This is usually 1. If not specified, the default value is 1.
For example: --accelerator=type=nvidia-tesla-k80,count=1

--autoscaling-metric-specs=[METRIC-NAME=TARGET,…]

Metric specifications that overrides a resource utilization metric's target value. At most one entry is allowed per metric.

METRIC-NAME

Resource metric name. Choices are 'cpu-usage', 'gpu-duty-cycle'.

TARGET

Target resource utilization in percentage (1% - 100%) for the given metric. If the value is set to 60, the target resource utilization is 60%.
For example: --autoscaling-metric-specs=cpu-usage=70

--deployed-model-id=DEPLOYED_MODEL_ID

User-specified ID of the deployed-model.

--disable-container-logging

For custom-trained Models and AutoML Tabular Models, the container of the deployed model instances 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.
User can disable container logging by setting this flag to true.

--enable-access-logging

If true, online prediction access logs are sent to Cloud Logging.
These logs are standard server access logs, containing information like timestamp and latency for each prediction request.

--machine-type=MACHINE_TYPE

The machine resources to be used for each node of this deployment. For available machine types, see https://cloud.google.com/ai-platform-unified/docs/predictions/machine-types.

--max-replica-count=MAX_REPLICA_COUNT

Maximum number of machine replicas for the deployment resources the model will be deployed on.

--min-replica-count=MIN_REPLICA_COUNT

Minimum number of machine replicas for the deployment resources the model will be deployed on. If specified, the value must be equal to or larger than 1.
If not specified and the uploaded models use dedicated resources, the default value is 1.

--reservation-affinity=[key=KEY],[reservation-affinity-type=RESERVATION-AFFINITY-TYPE],[values=VALUES]

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.

--service-account=SERVICE_ACCOUNT

Service account that the deployed model's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project.

--spot

If true, online prediction access logs are sent to Cloud Logging.
These logs are standard server access logs, containing information like timestamp and latency for each prediction request.

--traffic-split=[DEPLOYED_MODEL_ID=VALUE,…]

List of pairs of deployed model id and value to set as traffic split.
GCLOUD WIDE FLAGS: These flags are available to all commands: --access-token-file, --account, --billing-project, --configuration, --flags-file, --flatten, --format, --help, --impersonate-service-account, --log-http, --project, --quiet, --trace-token, --user-output-enabled, --verbosity.
Run $ gcloud help for details.
NOTES: These variants are also available:
gcloud alpha ai endpoints deploy-model

gcloud beta ai endpoints deploy-model