Class ModelContainerSpec (1.38.1) 

ModelContainerSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification <https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core>__.

Attributes
Name Description
image_uri str
Required. Immutable. 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
command MutableSequence[str]
Immutable. 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
args MutableSequence[str]
Immutable. 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
ENTRYPOINT` __ and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD` and ENTRYPOINT interact
env MutableSequence[google.cloud.aiplatform_v1.types.EnvVar]
Immutable. 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: .. code:: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers `v1 core API
ports MutableSequence[google.cloud.aiplatform_v1.types.Port]
Immutable. 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
predict_route str
Immutable. 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][google.cloud.aiplatform.v1.EndpointService.DeployModel]: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: - ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the 
AIP_ENDPOINT_ID`` environment
      variable
AIP_DEPLOYED_MODEL_ID`` environment variable
health_route str
Immutable. 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
grpc_ports MutableSequence[google.cloud.aiplatform_v1.types.Port]
Immutable. 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.
deployment_timeout google.protobuf.duration_pb2.Duration
Immutable. Deployment timeout. Limit for deployment timeout is 2 hours.
shared_memory_size_mb int
Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
startup_probe google.cloud.aiplatform_v1.types.Probe
Immutable. Specification for Kubernetes startup probe.
health_probe google.cloud.aiplatform_v1.types.Probe
Immutable. Specification for Kubernetes readiness probe.

Methods

ModelContainerSpec

ModelContainerSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification <https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core>__.