AiEndpointWithModelGardenDeploymentModelConfigContainerSpec class
Constructors
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AiEndpointWithModelGardenDeploymentModelConfigContainerSpec({Input<List<String>?>? args, Input<List<String>?>? commands, Input<String?>? deploymentTimeout, Input<List<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecEnv>?>? envs, Input<List<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecGrpcPort>?>? grpcPorts, Input<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecHealthProbe?>? healthProbe, Input<String?>? healthRoute, required Input<String> imageUri, Input<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecLivenessProbe?>? livenessProbe, Input<List<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecPort>?>? ports, Input<String?>? predictRoute, Input<String?>? sharedMemorySizeMb, Input<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecStartupProbe?>? startupProbe})
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Creates a new AiEndpointWithModelGardenDeploymentModelConfigContainerSpec.
args Specifies arguments for the command that runs when the container starts.
commands Specifies the command that runs when the container starts. This overrides
deploymentTimeout Deployment timeout.
envs List of environment variables to set in the container. After the container
grpcPorts List of ports to expose from the container. Vertex AI sends gRPC
healthProbe Probe describes a health check to be performed against a container to
healthRoute HTTP path on the container to send health checks to. Vertex AI
imageUri URI of the Docker image to be used as the custom container for serving
livenessProbe Probe describes a health check to be performed against a container to
ports List of ports to expose from the container. Vertex AI sends any
predictRoute HTTP path on the container to send prediction requests to. Vertex AI
sharedMemorySizeMb The amount of the VM memory to reserve as the shared memory for the model
startupProbe Probe describes a health check to be performed against a container to
const
-
AiEndpointWithModelGardenDeploymentModelConfigContainerSpec.fromMap(Map<String, dynamic> map)
-
factory
Properties
-
args
→ Input<List<String>?>?
-
Specifies arguments for the command that runs when the container starts.
This overrides the container's
CMD. Specify
this field as an array of executable and arguments, similar to a Docker
CMD's "default parameters" form.
If you don't specify this field but do specify the
command field, then the command from the
command field runs without any additional arguments. See the
Kubernetes documentation about how the
command and args fields interact with a container's ENTRYPOINT and
CMD.
If you don't specify this field and don't specify the command field,
then the container's
ENTRYPOINT and
CMD determine what runs based on their default behavior. See the Docker
documentation about how CMD and ENTRYPOINT
interact.
In this field, you can reference environment variables
set by Vertex
AI
and environment variables set in the env field.
You cannot reference environment variables set in the Docker image. In
order for environment variables to be expanded, reference them by using the
following syntax:$(VARIABLE_NAME)
Note that this differs from Bash variable expansion, which does not use
parentheses. If a variable cannot be resolved, the reference in the input
string is used unchanged. To avoid variable expansion, you can escape this
syntax with $$; for example:$$(VARIABLE_NAME)
This field corresponds to the args field of the Kubernetes Containers
v1 core
API.
final
-
commands
→ Input<List<String>?>?
-
Specifies the command that runs when the container starts. This overrides
the container's
ENTRYPOINT.
Specify this field as an array of executable and arguments, similar to a
Docker
ENTRYPOINT's "exec" form, not its "shell" form.
If you do not specify this field, then the container's ENTRYPOINT runs,
in conjunction with the args field or the
container's CMD,
if either exists. If this field is not specified and the container does not
have an ENTRYPOINT, then refer to the Docker documentation about how
CMD and ENTRYPOINT
interact.
If you specify this field, then you can also specify the args field to
provide additional arguments for this command. However, if you specify this
field, then the container's CMD is ignored. See the
Kubernetes documentation about how the
command and args fields interact with a container's ENTRYPOINT and
CMD.
In this field, you can reference environment variables set by Vertex
AI
and environment variables set in the env field.
You cannot reference environment variables set in the Docker image. In
order for environment variables to be expanded, reference them by using the
following syntax:$(VARIABLE_NAME)
Note that this differs from Bash variable expansion, which does not use
parentheses. If a variable cannot be resolved, the reference in the input
string is used unchanged. To avoid variable expansion, you can escape this
syntax with $$; for example:$$(VARIABLE_NAME)
This field corresponds to the command field of the Kubernetes Containers
v1 core
API.
final
-
deploymentTimeout
→ Input<String?>?
-
Deployment timeout.
Limit for deployment timeout is 2 hours.
final
-
envs
→ Input<List<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecEnv>?>?
-
List of environment variables to set in the container. After the container
starts running, code running in the container can read these environment
variables.
Additionally, the command and
args fields can reference these variables. Later
entries in this list can also reference earlier entries. For example, the
following example sets the variable
VAR_2 to have the value foo bar:
final
-
grpcPorts
→ Input<List<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecGrpcPort>?>?
-
List of ports to expose from the container. Vertex AI sends gRPC
prediction requests that it receives to the first port on this list. Vertex
AI also sends liveness and health checks to this port.
If you do not specify this field, gRPC requests to the container will be
disabled.
Vertex AI does not use ports other than the first one listed. This field
corresponds to the
ports field of the Kubernetes Containers v1 core API.
Structure is documented below.
final
-
hashCode
→ int
-
The hash code for this object.
no setterinherited
-
healthProbe
→ Input<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecHealthProbe?>?
-
Probe describes a health check to be performed against a container to
determine whether it is alive or ready to receive traffic.
Structure is documented below.
final
-
healthRoute
→ Input<String?>?
-
HTTP path on the container to send health checks to. Vertex AI
intermittently sends GET requests to this path on the container's IP
address and port to check that the container is healthy. Read more about
health
checks.
For example, if you set this field to
/bar, then Vertex AI
intermittently sends a GET request to the /bar path on the port of your
container specified by the first value of this ModelContainerSpec's
ports field.
If you don't specify this field, it defaults to the following value when
you deploy this Model to an Endpoint:/v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict
The placeholders in this value are replaced as follows:
final
-
imageUri
→ Input<String>
-
URI of the Docker image to be used as the custom container for serving
predictions. This URI must identify an image in Artifact Registry or
Container Registry. Learn more about the container publishing
requirements,
including permissions requirements for the Vertex AI Service Agent.
The container image is ingested upon ModelService.UploadModel, stored
internally, and this original path is afterwards not used.
To learn about the requirements for the Docker image itself, see
Custom container
requirements.
You can use the URI to one of Vertex AI's pre-built container images for
prediction
in this field.
final
-
livenessProbe
→ Input<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecLivenessProbe?>?
-
Probe describes a health check to be performed against a container to
determine whether it is alive or ready to receive traffic.
Structure is documented below.
final
-
ports
→ Input<List<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecPort>?>?
-
List of ports to expose from the container. Vertex AI sends any
prediction requests that it receives to the first port on this list. Vertex
AI also sends
liveness and health
checks
to this port.
If you do not specify this field, it defaults to following value:
final
-
predictRoute
→ Input<String?>?
-
HTTP path on the container to send prediction requests to. Vertex AI
forwards requests sent using
projects.locations.endpoints.predict to this
path on the container's IP address and port. Vertex AI then returns the
container's response in the API response.
For example, if you set this field to
/foo, then when Vertex AI
receives a prediction request, it forwards the request body in a POST
request to the /foo path on the port of your container specified by the
first value of this ModelContainerSpec's
ports field.
If you don't specify this field, it defaults to the following value when
you deploy this Model to an Endpoint:/v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict
The placeholders in this value are replaced as follows:
final
-
runtimeType
→ Type
-
A representation of the runtime type of the object.
no setterinherited
-
sharedMemorySizeMb
→ Input<String?>?
-
The amount of the VM memory to reserve as the shared memory for the model
in megabytes.
final
-
startupProbe
→ Input<AiEndpointWithModelGardenDeploymentModelConfigContainerSpecStartupProbe?>?
-
Probe describes a health check to be performed against a container to
determine whether it is alive or ready to receive traffic.
Structure is documented below.
final
