public sealed class ModelContainerSpec : IMessage<ModelContainerSpec>, IEquatable<ModelContainerSpec>, IDeepCloneable<ModelContainerSpec>, IBufferMessage, IMessageReference documentation and code samples for the Cloud AI Platform v1beta1 API class ModelContainerSpec.
Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification.
Implements
IMessageModelContainerSpec, IEquatableModelContainerSpec, IDeepCloneableModelContainerSpec, IBufferMessage, IMessageNamespace
Google.Cloud.AIPlatform.V1Beta1Assembly
Google.Cloud.AIPlatform.V1Beta1.dll
Constructors
ModelContainerSpec()
public ModelContainerSpec()ModelContainerSpec(ModelContainerSpec)
public ModelContainerSpec(ModelContainerSpec other)| Parameter | |
|---|---|
| Name | Description |
other |
ModelContainerSpec |
Properties
Args
public RepeatedField<string> Args { get; }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.v1beta1.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.v1beta1.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.
| Property Value | |
|---|---|
| Type | Description |
RepeatedFieldstring |
|
Command
public RepeatedField<string> Command { get; }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.v1beta1.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.v1beta1.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.
| Property Value | |
|---|---|
| Type | Description |
RepeatedFieldstring |
|
DeploymentTimeout
public Duration DeploymentTimeout { get; set; }Immutable. Deployment timeout. Limit for deployment timeout is 2 hours.
| Property Value | |
|---|---|
| Type | Description |
Duration |
|
Env
public RepeatedField<EnvVar> Env { get; }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.v1beta1.ModelContainerSpec.command] and
[args][google.cloud.aiplatform.v1beta1.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.
| Property Value | |
|---|---|
| Type | Description |
RepeatedFieldEnvVar |
|
GrpcPorts
public RepeatedField<Port> GrpcPorts { get; }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.
| Property Value | |
|---|---|
| Type | Description |
RepeatedFieldPort |
|
HealthProbe
public Probe HealthProbe { get; set; }Immutable. Specification for Kubernetes readiness probe.
| Property Value | |
|---|---|
| Type | Description |
Probe |
|
HealthRoute
public string HealthRoute { get; set; }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.v1beta1.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.v1beta1.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 theAIP_ENDPOINT_IDenvironment variable.)<var>DEPLOYED_MODEL</var>: [DeployedModel.id][google.cloud.aiplatform.v1beta1.DeployedModel.id] of the
DeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.)
| Property Value | |
|---|---|
| Type | Description |
string |
|
ImageUri
public string ImageUri { get; set; }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.v1beta1.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.
| Property Value | |
|---|---|
| Type | Description |
string |
|
Ports
public RepeatedField<Port> Ports { get; }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.
| Property Value | |
|---|---|
| Type | Description |
RepeatedFieldPort |
|
PredictRoute
public string PredictRoute { get; set; }Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using [projects.locations.endpoints.predict][google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.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 theAIP_ENDPOINT_IDenvironment variable.)<var>DEPLOYED_MODEL</var>: [DeployedModel.id][google.cloud.aiplatform.v1beta1.DeployedModel.id] of the
DeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.)
| Property Value | |
|---|---|
| Type | Description |
string |
|
SharedMemorySizeMb
public long SharedMemorySizeMb { get; set; }Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
| Property Value | |
|---|---|
| Type | Description |
long |
|
StartupProbe
public Probe StartupProbe { get; set; }Immutable. Specification for Kubernetes startup probe.
| Property Value | |
|---|---|
| Type | Description |
Probe |
|