Class LocalModel (1.31.0)

LocalModel(
    serving_container_spec: typing.Optional[
        google.cloud.aiplatform_v1.types.model.ModelContainerSpec
    ] = None,
    serving_container_image_uri: typing.Optional[str] = None,
    serving_container_predict_route: typing.Optional[str] = None,
    serving_container_health_route: typing.Optional[str] = None,
    serving_container_command: typing.Optional[typing.Sequence[str]] = None,
    serving_container_args: typing.Optional[typing.Sequence[str]] = None,
    serving_container_environment_variables: typing.Optional[
        typing.Dict[str, str]
    ] = None,
    serving_container_ports: typing.Optional[typing.Sequence[int]] = None,
)

Class that represents a local model.

Methods

LocalModel

LocalModel(
    serving_container_spec: typing.Optional[
        google.cloud.aiplatform_v1.types.model.ModelContainerSpec
    ] = None,
    serving_container_image_uri: typing.Optional[str] = None,
    serving_container_predict_route: typing.Optional[str] = None,
    serving_container_health_route: typing.Optional[str] = None,
    serving_container_command: typing.Optional[typing.Sequence[str]] = None,
    serving_container_args: typing.Optional[typing.Sequence[str]] = None,
    serving_container_environment_variables: typing.Optional[
        typing.Dict[str, str]
    ] = None,
    serving_container_ports: typing.Optional[typing.Sequence[int]] = None,
)

Creates a local model instance.

Parameters
Name	Description
`serving_container_spec`	`aiplatform.gapic.ModelContainerSpec` Optional. The container spec of the LocalModel instance.
`serving_container_image_uri`	`str` Optional. The URI of the Model serving container.
`serving_container_predict_route`	`str` Optional. An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI.
`serving_container_health_route`	`str` Optional. An HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by Vertex AI.
`serving_container_command`	`Sequence[str]` Optional. The command with which the container is run. Not executed within a shell. The Docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
`serving_container_environment_variables`	`Dict[str, str]` Optional. The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names.
`serving_container_ports`	`Sequence[int]` Optional. Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default "0.0.0.0" address inside a container will be accessible from the network.

Exceptions
Type	Description
`ValueError`	If `serving_container_spec` is specified but `serving_container_spec.image_uri` is `None`. Also if `serving_container_spec` is None but `serving_container_image_uri` is `None`.

build_cpr_model

build_cpr_model(src_dir: str, output_image_uri: str, predictor: typing.Optional[typing.Type[google.cloud.aiplatform.prediction.predictor.Predictor]] = None, handler: typing.Type[google.cloud.aiplatform.prediction.handler.Handler] = <class 'google.cloud.aiplatform.prediction.handler.PredictionHandler'>, base_image: str = 'python:3.7', requirements_path: typing.Optional[str] = None, extra_packages: typing.Optional[typing.List[str]] = None, no_cache: bool = False) -> google.cloud.aiplatform.prediction.local_model.LocalModel

Builds a local model from a custom predictor.

This method builds a docker image to include user-provided predictor, and handler.

Sample src_dir contents (e.g. ./user_src_dir):

user_src_dir/
|-- predictor.py
|-- requirements.txt
|-- user_code/
|   |-- utils.py
|   |-- custom_package.tar.gz
|   |-- ...
|-- ...

To build a custom container:

local_model = LocalModel.build_cpr_model(
    "./user_src_dir",
    "us-docker.pkg.dev/$PROJECT/$REPOSITORY/$IMAGE_NAME$",
    predictor=$CUSTOM_PREDICTOR_CLASS,
    requirements_path="./user_src_dir/requirements.txt",
    extra_packages=["./user_src_dir/user_code/custom_package.tar.gz"],
)

In the built image, user provided files will be copied as follows:

container_workdir/
|-- predictor.py
|-- requirements.txt
|-- user_code/
|   |-- utils.py
|   |-- custom_package.tar.gz
|   |-- ...
|-- ...

To exclude files and directories from being copied into the built container images, create a .dockerignore file in the src_dir. See https://docs.docker.com/engine/reference/builder/#dockerignore-file for more details about usage.

In order to save and restore class instances transparently with Pickle, the class definition must be importable and live in the same module as when the object was stored. If you want to use Pickle, you must save your objects right under the src_dir you provide.

The created CPR images default the number of model server workers to the number of cores. Depending on the characteristics of your model, you may need to adjust the number of workers. You can set the number of workers with the following environment variables:

VERTEX_CPR_WEB_CONCURRENCY:
    The number of the workers. This will overwrite the number calculated by the other
    variables, min(VERTEX_CPR_WORKERS_PER_CORE * number_of_cores, VERTEX_CPR_MAX_WORKERS).
VERTEX_CPR_WORKERS_PER_CORE:
    The number of the workers per core. The default is 1.
VERTEX_CPR_MAX_WORKERS:
    The maximum number of workers can be used given the value of VERTEX_CPR_WORKERS_PER_CORE
    and the number of cores.

If you hit the error showing "model server container out of memory" when you deploy models to endpoints, you should decrease the number of workers.

Parameters
Name	Description
`src_dir`	`str` Required. The path to the local directory including all needed files such as predictor. The whole directory will be copied to the image.
`output_image_uri`	`str` Required. The image uri of the built image.
`predictor`	`Type[Predictor]` Optional. The custom predictor class consumed by handler to do prediction.
`handler`	`Type[Handler]` Required. The handler class to handle requests in the model server.
`base_image`	`str` Required. The base image used to build the custom images. The base image must have python and pip installed where the two commands `python` and `pip` must be available.
`requirements_path`	`str` Optional. The path to the local requirements.txt file. This file will be copied to the image and the needed packages listed in it will be installed.
`extra_packages`	`List[str]` Optional. The list of user custom dependency packages to install.
`no_cache`	`bool` Required. Do not use cache when building the image. Using build cache usually reduces the image building time. See https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#leverage-build-cache for more details.

Exceptions
Type	Description
`ValueError`	If handler is `None` or if handler is `PredictionHandler` but predictor is `None`.

Returns
Type	Description
`local model`	Instantiated representation of the local model.

copy_image

copy_image(
    dst_image_uri: str,
) -> google.cloud.aiplatform.prediction.local_model.LocalModel

Copies the image to another image uri.

Parameter
Name	Description
`dst_image_uri`	`str` The destination image uri to copy the image to.

Exceptions
Type	Description
`DockerError`	If the command fails.

Returns
Type	Description
`local model`	Instantiated representation of the local model with the copied image.

deploy_to_local_endpoint

deploy_to_local_endpoint(
    artifact_uri: typing.Optional[str] = None,
    credential_path: typing.Optional[str] = None,
    host_port: typing.Optional[str] = None,
    gpu_count: typing.Optional[int] = None,
    gpu_device_ids: typing.Optional[typing.List[str]] = None,
    gpu_capabilities: typing.Optional[typing.List[typing.List[str]]] = None,
    container_ready_timeout: typing.Optional[int] = None,
    container_ready_check_interval: typing.Optional[int] = None,
) -> google.cloud.aiplatform.prediction.local_endpoint.LocalEndpoint

Deploys the local model instance to a local endpoint.

An environment variable, GOOGLE_CLOUD_PROJECT, will be set to the project in the global config. This is required if the credentials file does not have project specified and used to recognize the project by the Cloud Storage client.

Example 1:

with local_model.deploy_to_local_endpoint(
    artifact_uri="gs://path/to/your/model",
    credential_path="local/path/to/your/credentials",
) as local_endpoint:
    health_check_response = local_endpoint.run_health_check()
    print(health_check_response, health_check_response.content)

    predict_response = local_endpoint.predict(
        request='{"instances": [[1, 2, 3, 4]]}',
        headers={"header-key": "header-value"},
    )
    print(predict_response, predict_response.content)

    local_endpoint.print_container_logs()

Example 2:

local_endpoint = local_model.deploy_to_local_endpoint(
    artifact_uri="gs://path/to/your/model",
    credential_path="local/path/to/your/credentials",
)
local_endpoint.serve()

health_check_response = local_endpoint.run_health_check()
print(health_check_response, health_check_response.content)

predict_response = local_endpoint.predict(
    request='{"instances": [[1, 2, 3, 4]]}',
    headers={"header-key": "header-value"},
)
print(predict_response, predict_response.content)

local_endpoint.print_container_logs()
local_endpoint.stop()

Parameters
Name	Description
`artifact_uri`	`str` Optional. The path to the directory containing the Model artifact and any of its supporting files. The path is either a GCS uri or the path to a local directory. If this parameter is set to a GCS uri: (1) `credential_path` must be specified for local prediction. (2) The GCS uri will be passed directly to `Predictor.load`. If this parameter is a local directory: (1) The directory will be mounted to a default temporary model path. (2) The mounted path will be passed to `Predictor.load`.
`credential_path`	`str` Optional. The path to the credential key that will be mounted to the container. If it's unset, the environment variable, `GOOGLE_APPLICATION_CREDENTIALS`, will be used if set.
`host_port`	`str` Optional. The port on the host that the port, `AIP_HTTP_PORT`, inside the container will be exposed as. If it's unset, a random host port will be assigned.
`gpu_count`	`int` Optional. Number of devices to request. Set to -1 to request all available devices. To use GPU, set either `gpu_count` or `gpu_device_ids`. The default value is -1 if `gpu_capabilities` is set but both `gpu_count` and `gpu_device_ids` are not set.
`gpu_device_ids`	`List[str]` Optional. This parameter corresponds to `NVIDIA_VISIBLE_DEVICES` in the NVIDIA Runtime. To use GPU, set either `gpu_count` or `gpu_device_ids`.
`gpu_capabilities`	`List[List[str]]` Optional. This parameter corresponds to `NVIDIA_DRIVER_CAPABILITIES` in the NVIDIA Runtime. The outer list acts like an OR, and each sub-list acts like an AND. The driver will try to satisfy one of the sub-lists. Available capabilities for the NVIDIA driver can be found in https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html#driver-capabilities. The default value is `[["utility", "compute"]]` if `gpu_count` or `gpu_device_ids` is set.
`container_ready_timeout`	`int` Optional. The timeout in second used for starting the container or succeeding the first health check.
`container_ready_check_interval`	`int` Optional. The time interval in second to check if the container is ready or the first health check succeeds.

get_serving_container_spec

get_serving_container_spec() -> (
    google.cloud.aiplatform_v1.types.model.ModelContainerSpec
)

Returns the container spec for the image.

pull_image_if_not_exists

pull_image_if_not_exists()

Pulls the image if the image does not exist locally.

Exceptions
Type	Description
`DockerError`	If the command fails.

push_image

push_image() -> None

Pushes the image to a registry.

If you hit permission errors while calling this function, please refer to https://cloud.google.com/artifact-registry/docs/docker/authentication to set up the authentication.

For Artifact Registry, the repository must be created before you are able to push images to it. Otherwise, you will hit the error, "Repository {REPOSITORY} not found". To create Artifact Registry repositories, use UI or call the following gcloud command.

gcloud artifacts repositories create {REPOSITORY}                 --project {PROJECT}                 --location {REGION}                 --repository-format docker

See https://cloud.google.com/artifact-registry/docs/manage-repos#create for more details.

If you hit a "Permission artifactregistry.repositories.uploadArtifacts denied" error, set up authentication for Docker.

gcloud auth configure-docker {REPOSITORY}

See https://cloud.google.com/artifact-registry/docs/docker/authentication for mode details.

Exceptions
Type	Description
`ValueError`	If the image uri is not a container registry or artifact registry uri.
`DockerError`	If the command fails.