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Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#args
def args() -> ::Array<::String>
-
(::Array<::String>) — 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 DockerCMD
'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 thecommand
andargs
fields interact with a container'sENTRYPOINT
andCMD
.If you don't specify this field and don't specify the
command
field, then the container'sENTRYPOINT
andCMD
determine what runs based on their default behavior. See the Docker documentation about howCMD
andENTRYPOINT
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 theargs
field of the Kubernetes Containers v1 core API.
#args=
def args=(value) -> ::Array<::String>
-
value (::Array<::String>) — 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 DockerCMD
'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 thecommand
andargs
fields interact with a container'sENTRYPOINT
andCMD
.If you don't specify this field and don't specify the
command
field, then the container'sENTRYPOINT
andCMD
determine what runs based on their default behavior. See the Docker documentation about howCMD
andENTRYPOINT
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 theargs
field of the Kubernetes Containers v1 core API.
-
(::Array<::String>) — 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 DockerCMD
'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 thecommand
andargs
fields interact with a container'sENTRYPOINT
andCMD
.If you don't specify this field and don't specify the
command
field, then the container'sENTRYPOINT
andCMD
determine what runs based on their default behavior. See the Docker documentation about howCMD
andENTRYPOINT
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 theargs
field of the Kubernetes Containers v1 core API.
#command
def command() -> ::Array<::String>
-
(::Array<::String>) — 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'sCMD
, if either exists. If this field is not specified and the container does not have anENTRYPOINT
, then refer to the Docker documentation about howCMD
andENTRYPOINT
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'sCMD
is ignored. See the Kubernetes documentation about how thecommand
andargs
fields interact with a container'sENTRYPOINT
andCMD
.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 thecommand
field of the Kubernetes Containers v1 core API.
#command=
def command=(value) -> ::Array<::String>
-
value (::Array<::String>) — 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'sCMD
, if either exists. If this field is not specified and the container does not have anENTRYPOINT
, then refer to the Docker documentation about howCMD
andENTRYPOINT
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'sCMD
is ignored. See the Kubernetes documentation about how thecommand
andargs
fields interact with a container'sENTRYPOINT
andCMD
.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 thecommand
field of the Kubernetes Containers v1 core API.
-
(::Array<::String>) — 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'sCMD
, if either exists. If this field is not specified and the container does not have anENTRYPOINT
, then refer to the Docker documentation about howCMD
andENTRYPOINT
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'sCMD
is ignored. See the Kubernetes documentation about how thecommand
andargs
fields interact with a container'sENTRYPOINT
andCMD
.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 thecommand
field of the Kubernetes Containers v1 core API.
#env
def env() -> ::Array<::Google::Cloud::AIPlatform::V1::EnvVar>
-
(::Array<::Google::Cloud::AIPlatform::V1::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 valuefoo bar
:json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ]
If you switch the order of the variables in the example, then the expansion does not occur.
This field corresponds to the
env
field of the Kubernetes Containers v1 core API.
#env=
def env=(value) -> ::Array<::Google::Cloud::AIPlatform::V1::EnvVar>
-
value (::Array<::Google::Cloud::AIPlatform::V1::EnvVar>) — Immutable. List of environment variables to set in the container. After the container
starts running, code running in the container can read these environment
variables.
Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable
VAR_2
to have the valuefoo bar
:json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(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.
-
(::Array<::Google::Cloud::AIPlatform::V1::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 valuefoo 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.
#health_route
def health_route() -> ::String
-
(::String) —
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 thisModelContainerSpec
'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 theAIP_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.)
#health_route=
def health_route=(value) -> ::String
-
value (::String) —
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 thisModelContainerSpec
'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 theAIP_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.)
-
(::String) —
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 thisModelContainerSpec
'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 theAIP_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.)
#image_uri
def image_uri() -> ::String
-
(::String) — 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.
#image_uri=
def image_uri=(value) -> ::String
-
value (::String) — 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.
-
(::String) — 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.
#ports
def ports() -> ::Array<::Google::Cloud::AIPlatform::V1::Port>
-
(::Array<::Google::Cloud::AIPlatform::V1::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
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.
#ports=
def ports=(value) -> ::Array<::Google::Cloud::AIPlatform::V1::Port>
-
value (::Array<::Google::Cloud::AIPlatform::V1::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
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.
-
(::Array<::Google::Cloud::AIPlatform::V1::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
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.
#predict_route
def predict_route() -> ::String
-
(::String) —
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 thisModelContainerSpec
'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 theAIP_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.)
#predict_route=
def predict_route=(value) -> ::String
-
value (::String) —
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 thisModelContainerSpec
'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 theAIP_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.)
-
(::String) —
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 thisModelContainerSpec
'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 theAIP_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.)