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NAME
gcloud beta ai model-garden - interact with and manage resources in Vertex
Model Garden
SYNOPSIS
gcloud beta ai model-garden GROUP [GCLOUD_WIDE_FLAG ...]
DESCRIPTION
(BETA) Interact with and manage resources in Vertex Model Garden.
GCLOUD WIDE FLAGS
These flags are available to all commands: --help.
Run $ gcloud help for details.
GROUPS
GROUP is one of the following:
models
(BETA) List and use Model Garden models.
NOTES
This command is currently in beta and might change without notice. This
variant is also available:
$ gcloud alpha ai model-garden

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NAME
gcloud beta ai model-garden models deploy - deploy a model in Model Garden
to a Vertex AI endpoint
SYNOPSIS
gcloud beta ai model-garden models deploy --model=MODEL
[--accelerator-type=ACCELERATOR_TYPE] [--accept-eula] [--asynchronous]
[--container-args=[ARG,...]] [--container-command=[COMMAND,...]]
[--container-deployment-timeout-seconds=CONTAINER_DEPLOYMENT_TIMEOUT_SECONDS]
[--container-env-vars=[KEY=VALUE,...]]
[--container-grpc-ports=[PORT,...]]
[--container-health-probe-exec=[HEALTH_PROBE_EXEC,...]]
[--container-health-probe-period-seconds=CONTAINER_HEALTH_PROBE_PERIOD_SECONDS]
[--container-health-probe-timeout-seconds=CONTAINER_HEALTH_PROBE_TIMEOUT_SECONDS]
[--container-health-route=CONTAINER_HEALTH_ROUTE]
[--container-image-uri=CONTAINER_IMAGE_URI]
[--container-ports=[PORT,...]]
[--container-predict-route=CONTAINER_PREDICT_ROUTE]
[--container-shared-memory-size-mb=CONTAINER_SHARED_MEMORY_SIZE_MB]
[--container-startup-probe-exec=[STARTUP_PROBE_EXEC,...]]
[--container-startup-probe-period-seconds=CONTAINER_STARTUP_PROBE_PERIOD_SECONDS]
[--container-startup-probe-timeout-seconds=CONTAINER_STARTUP_PROBE_TIMEOUT_SECONDS]
[--enable-fast-tryout] [--endpoint-display-name=ENDPOINT_DISPLAY_NAME]
[--hugging-face-access-token=HUGGING_FACE_ACCESS_TOKEN]
[--machine-type=MACHINE_TYPE] [--region=REGION]
[--reservation-affinity=[key=KEY],
[reservation-affinity-type=RESERVATION-AFFINITY-TYPE],
[values=VALUES]] [--spot] [--use-dedicated-endpoint]
[GCLOUD_WIDE_FLAG ...]
EXAMPLES
To deploy a Model Garden model google/gemma2/gemma2-9b under project
example in region us-central1, run:
$ gcloud ai model-garden models deploy \
--model=google/gemma2@gemma-2-9b --project=example \
--region=us-central1
To deploy a Hugging Face model meta-llama/Meta-Llama-3-8B under project
example in region us-central1, run:
$ gcloud ai model-garden models deploy \
--model=meta-llama/Meta-Llama-3-8B \
--hugging-face-access-token={hf_token} --project=example \
--region=us-central1
REQUIRED FLAGS
--model=MODEL
The model to be deployed. If it is a Model Garden model, it should be
in the format of {publisher_name}/{model_name}@{model_version_name},
e.g. google/gemma2@gemma-2-2b. If it is a Hugging Face model, it should
be in the convention of Hugging Face models, e.g.
meta-llama/Meta-Llama-3-8B.
OPTIONAL FLAGS
--accelerator-type=ACCELERATOR_TYPE
The accelerator type to serve the model. It should be a supported
accelerator type from the verified deployment configurations of the
model. Use gcloud ai model-garden models list-deployment-config to
check the supported accelerator types.
--accept-eula
When set, the user accepts the End User License Agreement (EULA) of the
model.
--asynchronous
If set to true, the command will terminate immediately and not keep
polling the operation status.
--container-args=[ARG,...]
Comma-separated arguments passed to the command run by the container
image. If not specified and no --command is provided, the container
image's default command is used.
--container-command=[COMMAND,...]
Entrypoint for the container image. If not specified, the container
image's default entrypoint is run.
--container-deployment-timeout-seconds=CONTAINER_DEPLOYMENT_TIMEOUT_SECONDS
Deployment timeout in seconds.
--container-env-vars=[KEY=VALUE,...]
List of key-value pairs to set as environment variables.
--container-grpc-ports=[PORT,...]
Container ports to receive grpc requests at. Must be a number between 1
and 65535, inclusive.
--container-health-probe-exec=[HEALTH_PROBE_EXEC,...]
Exec specifies the action to take. Used by health probe. An example of
this argument would be ["cat", "/tmp/healthy"].
--container-health-probe-period-seconds=CONTAINER_HEALTH_PROBE_PERIOD_SECONDS
How often (in seconds) to perform the health probe. Default to 10
seconds. Minimum value is 1.
--container-health-probe-timeout-seconds=CONTAINER_HEALTH_PROBE_TIMEOUT_SECONDS
Number of seconds after which the health probe times out. Defaults to 1
second. Minimum value is 1.
--container-health-route=CONTAINER_HEALTH_ROUTE
HTTP path to send health checks to inside the container.
--container-image-uri=CONTAINER_IMAGE_URI
URI of the Model serving container file in the Container Registry (e.g.
gcr.io/myproject/server:latest).
--container-ports=[PORT,...]
Container ports to receive http requests at. Must be a number between 1
and 65535, inclusive.
--container-predict-route=CONTAINER_PREDICT_ROUTE
HTTP path to send prediction requests to inside the container.
--container-shared-memory-size-mb=CONTAINER_SHARED_MEMORY_SIZE_MB
The amount of the VM memory to reserve as the shared memory for the
model in megabytes.
--container-startup-probe-exec=[STARTUP_PROBE_EXEC,...]
Exec specifies the action to take. Used by startup probe. An example of
this argument would be ["cat", "/tmp/healthy"].
--container-startup-probe-period-seconds=CONTAINER_STARTUP_PROBE_PERIOD_SECONDS
How often (in seconds) to perform the startup probe. Default to 10
seconds. Minimum value is 1.
--container-startup-probe-timeout-seconds=CONTAINER_STARTUP_PROBE_TIMEOUT_SECONDS
Number of seconds after which the startup probe times out. Defaults to
1 second. Minimum value is 1.
--enable-fast-tryout
If True, model will be deployed using faster deployment path. Useful
for quick experiments. Not for production workloads. Only available for
most popular models with certain machine types.
--endpoint-display-name=ENDPOINT_DISPLAY_NAME
Display name of the endpoint with the deployed model.
--hugging-face-access-token=HUGGING_FACE_ACCESS_TOKEN
The access token from Hugging Face needed to read the model artifacts
of gated models. It is only needed when the Hugging Face model to
deploy is gated.
--machine-type=MACHINE_TYPE
The machine type to deploy the model to. It should be a supported
machine type from the deployment configurations of the model. Use
gcloud ai model-garden models list-deployment-config to check the
supported machine types.
Region resource - Cloud region to deploy the model. This represents a
Cloud 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 --region on the command line with a fully
specified name;
◆ set the property ai/region with a fully specified name;
◆ choose one from the prompted list of available regions with a fully
specified name;
◆ provide the argument --project on the command line;
◆ set the property core/project.
--region=REGION
ID of the region or fully qualified identifier for the region.
To set the region attribute:
▸ provide the argument --region on the command line;
▸ set the property ai/region;
▸ choose one from the prompted list of available regions.
--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.
--spot
If true, schedule the deployment workload on Spot VM.
--use-dedicated-endpoint
If true, the endpoint will be exposed through a dedicated DNS. Your
request to the dedicated DNS will be isolated from other users' traffic
and will have better performance and reliability.
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
This command is currently in beta and might change without notice. This
variant is also available:
$ gcloud alpha ai model-garden models deploy

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NAME
gcloud beta ai model-garden models - list and use Model Garden models
SYNOPSIS
gcloud beta ai model-garden models COMMAND [GCLOUD_WIDE_FLAG ...]
DESCRIPTION
(BETA) List and use Model Garden models.
GCLOUD WIDE FLAGS
These flags are available to all commands: --help.
Run $ gcloud help for details.
COMMANDS
COMMAND is one of the following:
deploy
(BETA) Deploy a model in Model Garden to a Vertex AI endpoint.
list
(BETA) List the publisher models in Model Garden.
list-deployment-config
(BETA) List the machine specifications supported by and verified for a
model in Model Garden.
NOTES
This command is currently in beta and might change without notice. This
variant is also available:
$ gcloud alpha ai model-garden models

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NAME
gcloud beta ai model-garden models list - list the publisher models in
Model Garden
SYNOPSIS
gcloud beta ai model-garden models list
[--list-supported-hugging-face-models] [--model-filter=MODEL_FILTER]
[--filter=EXPRESSION] [--limit=LIMIT; default=1000]
[--page-size=PAGE_SIZE] [--sort-by=[FIELD,...]] [GCLOUD_WIDE_FLAG ...]
DESCRIPTION
(BETA) This command lists either all models in Model Garden or all Hugging
Face models supported by Model Garden.
Note: Since the number of Hugging Face models is large, the default limit
is set to 500 with a page size of 100 when listing supported Hugging Face
models. To override the limit or page size, specify the --limit or
--page-size flags, respectively. To list all models in Model Garden, use
--limit=unlimited.
FLAGS
--list-supported-hugging-face-models
Whether to only list supported Hugging Face models.
--model-filter=MODEL_FILTER
Filter to apply to the model names or the display names of the list of
models.
LIST COMMAND FLAGS
--filter=EXPRESSION
Apply a Boolean filter EXPRESSION to each resource item to be listed.
If the expression evaluates True, then that item is listed. For more
details and examples of filter expressions, run $ gcloud topic filters.
This flag interacts with other flags that are applied in this order:
--flatten, --sort-by, --filter, --limit.
--limit=LIMIT; default=1000
Maximum number of resources to list. The default is 1000. This flag
interacts with other flags that are applied in this order: --flatten,
--sort-by, --filter, --limit.
--page-size=PAGE_SIZE
Some services group resource list output into pages. This flag
specifies the maximum number of resources per page. The default is
determined by the service if it supports paging, otherwise it is
unlimited (no paging). Paging may be applied before or after --filter
and --limit depending on the service.
--sort-by=[FIELD,...]
Comma-separated list of resource field key names to sort by. The
default order is ascending. Prefix a field with ``~'' for descending
order on that field. This flag interacts with other flags that are
applied in this order: --flatten, --sort-by, --filter, --limit.
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
This command is currently in beta and might change without notice. This
variant is also available:
$ gcloud alpha ai model-garden models list

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NAME
gcloud beta ai model-garden models list-deployment-config - list the
machine specifications supported by and verified for a model in Model
Garden
SYNOPSIS
gcloud beta ai model-garden models list-deployment-config --model=MODEL
[--hugging-face-access-token=HUGGING_FACE_ACCESS_TOKEN]
[--filter=EXPRESSION] [--sort-by=[FIELD,...]] [GCLOUD_WIDE_FLAG ...]
EXAMPLES
To list the supported machine specifications for google/gemma2@gemma-2-9b,
run:
$ gcloud ai model-garden models list-deployment-config \
--model=google/gemma2@gemma-2-9b
To list the supported machine specifications for a Hugging Face model
meta-llama/Meta-Llama-3-8B, run:
$ gcloud ai model-garden models list-deployment-config \
--model=meta-llama/Meta-Llama-3-8B
REQUIRED FLAGS
--model=MODEL
The model to be deployed. If it is a Model Garden model, it should be
in the format of {publisher_name}/{model_name}@{model_version_name},
e.g. google/gemma2@gemma-2-2b. If it is a Hugging Face model, it should
be in the convention of Hugging Face models, e.g.
meta-llama/Meta-Llama-3-8B.
FLAGS
--hugging-face-access-token=HUGGING_FACE_ACCESS_TOKEN
The access token from Hugging Face needed to read the model artifacts
of gated models in order to generate the deployment configs. It is only
needed when the Hugging Face model to deploy is gated and not verified
by Model Garden. You can use the gcloud ai alpha/beta model-garden
models list command to find out which ones are verified by Model
Garden.
LIST COMMAND FLAGS
--filter=EXPRESSION
Apply a Boolean filter EXPRESSION to each resource item to be listed.
If the expression evaluates True, then that item is listed. For more
details and examples of filter expressions, run $ gcloud topic filters.
This flag interacts with other flags that are applied in this order:
--flatten, --sort-by, --filter, --limit.
--sort-by=[FIELD,...]
Comma-separated list of resource field key names to sort by. The
default order is ascending. Prefix a field with ``~'' for descending
order on that field. This flag interacts with other flags that are
applied in this order: --flatten, --sort-by, --filter, --limit.
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
This command is currently in beta and might change without notice. This
variant is also available:
$ gcloud alpha ai model-garden models list-deployment-config