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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`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_v1beta1.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_v1beta1.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.v1beta1.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 |