Class ModelContainerSpec (1.0.0)

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification.

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][google.cloud.aiplatform.v1.ModelContainerSpec.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][google.cloud.aiplatform.v1.ModelContainerSpec.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: <code>$(<var>VARIABLE_NAME</var>)</code> 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 $$; for example: <code>$$(<var>VARIABLE_NAME</var>)</code> 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][google.cloud.aiplatform.v1.ModelContainerSpec.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][google.cloud.aiplatform.v1.ModelContainerSpec.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: <code>$(<var>VARIABLE_NAME</var>)</code> 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 $$; for example: <code>$$(<var>VARIABLE_NAME</var>)</code> This field corresponds to the command field of the Kubernetes Containers v1 core API.

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][google.cloud.aiplatform.v1.ModelContainerSpec.command] and [args][google.cloud.aiplatform.v1.ModelContainerSpec.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:

[
{
"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. 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][google.cloud.aiplatform.v1.ModelContainerSpec.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]: <code>/v1/endpoints/<var>ENDPOINT</var>/deployedModels/<var>DEPLOYED_MODEL</var>:predict</code> The placeholders in this value are replaced as follows:

• <var>ENDPOINT</var>: 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.)

• <var>DEPLOYED_MODEL</var>: [DeployedModel.id][google.cloud.aiplatform.v1.DeployedModel.id] of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_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][google.cloud.aiplatform.v1.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:

[
{
"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][google.cloud.aiplatform.v1.PredictionService.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][google.cloud.aiplatform.v1.ModelContainerSpec.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]: <code>/v1/endpoints/<var>ENDPOINT</var>/deployedModels/<var>DEPLOYED_MODEL</var>:predict</code> The placeholders in this value are replaced as follows:

• <var>ENDPOINT</var>: 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.)

• <var>DEPLOYED_MODEL</var>: [DeployedModel.id][google.cloud.aiplatform.v1.DeployedModel.id] of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)