3.0.0 Migration Guide

The 2.0 release of the google-cloud-automl client is a significant upgrade based on a next-gen code generator, and includes substantial interface changes. Existing code written for earlier versions of this library will likely require updates to use this version. This document describes the changes that have been made, and what you need to do to update your usage.

If you experience issues or have questions, please file an issue.

Supported Python Versions

WARNING: Breaking change

The 2.0.0 release requires Python 3.6+.

Method Calls

WARNING: Breaking change

Methods expect request objects. We provide a script that will convert most common use cases.

  • Install the library with libcst.
python3 -m pip install google-cloud-automl[libcst]
  • The script fixup_automl_{version}_keywords.py is shipped with the library. It expects an input directory (with the code to convert) and an empty destination directory.
$ fixup_automl_v1_keywords.py --input-directory .samples/ --output-directory samples/

Before:

from google.cloud import automl

project_id = "YOUR_PROJECT_ID"
model_id = "YOUR_MODEL_ID"

client = automl.AutoMlClient()
# Get the full path of the model.
model_full_id = client.model_path(project_id, "us-central1", model_id)
response = client.deploy_model(model_full_id)

After:

from google.cloud import automl

project_id = "YOUR_PROJECT_ID"
model_id = "YOUR_MODEL_ID"

client = automl.AutoMlClient()
# Get the full path of the model.
model_full_id = client.model_path(project_id, "us-central1", model_id)
response = client.deploy_model(name=model_full_id)

More Details

In google-cloud-automl<2.0.0, parameters required by the API were positional parameters and optional parameters were keyword parameters.

Before:

    def batch_predict(
        self,
        name,
        input_config,
        output_config,
        params=None,
        retry=google.api_core.gapic_v1.method.DEFAULT,
        timeout=google.api_core.gapic_v1.method.DEFAULT,
        metadata=None,
    ):

In the 2.0.0 release, all methods have a single positional parameter request. Method docstrings indicate whether a parameter is required or optional.

Some methods have additional keyword only parameters. The available parameters depend on the google.api.method_signature annotation specified by the API producer.

After:

def batch_predict(
        self,
        request: prediction_service.BatchPredictRequest = None,
        *,
        name: str = None,
        input_config: io.BatchPredictInputConfig = None,
        output_config: io.BatchPredictOutputConfig = None,
        params: Sequence[prediction_service.BatchPredictRequest.ParamsEntry] = None,
        retry: retries.Retry = gapic_v1.method.DEFAULT,
        timeout: float = None,
        metadata: Sequence[Tuple[str, str]] = (),
    ) -> operation.Operation:

NOTE: The request parameter and flattened keyword parameters for the API are mutually exclusive. Passing both will result in an error.

Both of these calls are valid:

response = client.batch_predict(
    request={
        "name": name,
        "input_config": input_config,
        "output_config": output_config,
        "params": params,
    }
)

response = client.batch_predict(
    name=name,
    input_config=input_config,
    output_config=output_config,
    params=params,
)

This call is invalid because it mixes request with a keyword argument params. Executing this code will result in an error.

response = client.batch_predict(
    request={
        "name": name,
        "input_config": input_config,
        "output_config": output_config,
    },
    params=params,
)

The method list_datasets takes an argument filter instead of filter_.

Before

from google.cloud import automl

project_id = "PROJECT_ID"

client = automl.AutoMlClient()
project_location = client.location_path(project_id, "us-central1")

# List all the datasets available in the region.
response = client.list_datasets(project_location, filter_="")

After

from google.cloud import automl

project_id = "PROJECT_ID"
client = automl.AutoMlClient()
# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"

# List all the datasets available in the region.
response = client.list_datasets(parent=project_location, filter="")

Changes to v1beta1 Tables Client

Optional arguments are now keyword-only arguments and must be passed by name. See PEP 3102.

*Before

    def predict(
        self,
        inputs,
        model=None,
        model_name=None,
        model_display_name=None,
        feature_importance=False,
        project=None,
        region=None,
        **kwargs
    ):

After

    def predict(
        self,
        inputs,
        *,
        model=None,
        model_name=None,
        model_display_name=None,
        feature_importance=False,
        project=None,
        region=None,
        **kwargs,
    ):

**kwargs passed to methods must be either (1) kwargs on the underlying method (retry, timeout, or metadata) or (2) attributes of the request object.

The following call is valid because filter is an attribute of automl_v1beta1.ListDatasetsRequest.

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# List all the datasets available in the region by applying filter.
response = client.list_datasets(filter=filter)

Enums and types

WARNING: Breaking change

The submodule enums and types have been removed.

Before:


from google.cloud import automl

gcs_source = automl.types.GcsSource(input_uris=["gs://YOUR_BUCKET_ID/path/to/your/input/csv_or_jsonl"])
deployment_state = automl.enums.Model.DeploymentState.DEPLOYED

After:

from google.cloud import automl

gcs_source = automl.GcsSource(input_uris=["gs://YOUR_BUCKET_ID/path/to/your/input/csv_or_jsonl"])
deployment_state = automl.Model.DeploymentState.DEPLOYED

Resource Path Helper Methods

The following resource name helpers have been removed. Please construct the strings manually.

from google.cloud import automl

project = "my-project"
location = "us-central1"
dataset = "my-dataset"
model = "my-model"
annotation_spec = "test-annotation"
model_evaluation = "test-evaluation"

# AutoMlClient
annotation_spec_path = f"projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}"
location_path = f"projects/{project}/locations/{location}"
model_evaluation_path = f"projects/{project}/locations/{location}/models/{model}/modelEvaluations/{model_evaluation}",

# PredictionServiceClient
model_path = f"projects/{project}/locations/{location}/models/{model}"
# alternatively you can use `model_path` from AutoMlClient
model_path = automl.AutoMlClient.model_path(project_id, location, model_id)