@@ -3272,10 +3272,11 @@ def upload(
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if local_model :
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container_spec = local_model .get_serving_container_spec ()
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appended_user_agent = [prediction_constants .CUSTOM_PREDICTION_ROUTINES ]
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+ elif not serving_container_image_uri and not artifact_uri :
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+ # It's a referenced/place holder model.
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+ container_spec = None
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else :
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- # Referenced models do not have container_image and artifact_uri
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- # Skip the container_image if this is a referenced model
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- if not serving_container_image_uri and artifact_uri :
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+ if not serving_container_image_uri :
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raise ValueError (
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"The parameter `serving_container_image_uri` is required "
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"if no `local_model` is provided."
@@ -5256,10 +5257,10 @@ def evaluate(
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the class "cat" corresponds with 0.97 in the example above.
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prediction_label_column (str):
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Optional. The column name of the field containing classes the model is scoring. Formatted to be able to find nested
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- columns, delimeted by `.`. If not set, defaulted to `prediction.classes` for classification.
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+ columns, delimited by `.`. If not set, defaulted to `prediction.classes` for classification.
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prediction_score_column (str):
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Optional. The column name of the field containing batch prediction scores. Formatted to be able to find nested columns,
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- delimeted by `.`. If not set, defaulted to `prediction.scores` for a `classification` problem_type, `prediction.value`
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+ delimited by `.`. If not set, defaulted to `prediction.scores` for a `classification` problem_type, `prediction.value`
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for a `regression` problem_type.
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staging_bucket (str):
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Optional. The GCS directory to use for staging files from this evaluation job. Defaults to the value set in
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