GoogleCloudAiplatformV1ModelContainerSpec
import type { GoogleCloudAiplatformV1ModelContainerSpec } from "https://googleapis.deno.dev/v1/aiplatform:v1.ts";
Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification.
§Properties
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
.
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.
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.
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.
Immutable. Deployment timeout. Limit for deployment timeout is 2 hours.
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
: 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.
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.
Immutable. Specification for Kubernetes readiness probe.
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.
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: * 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.)
- DEPLOYED_MODEL: DeployedModel.id of the
DeployedModel
. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_ID
environment variable.)
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, 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.
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
to this port. If you do not specify this field, it defaults to following
value: json [ { "containerPort": 8080 } ]
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.
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:
/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.)
- DEPLOYED_MODEL: DeployedModel.id of the
DeployedModel
. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_ID
environment variable.)
Immutable. Specification for Kubernetes startup probe.