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ONNXModel
(
session
:
typing
.
Optional
[
bigframes
.
session
.
Session
]
=
None
,
model_path
:
typing
.
Optional
[
str
]
=
None
,
)
Imported Open Neural Network Exchange (ONNX) model.
Parameters
session
BigQuery Session
BQ session to create the model
model_path
str
Cloud Storage path that holds the model files.
Methods
__repr__
__repr__
()
Print the estimator's constructor with all non-default parameter values
get_params
get_params
(
deep
:
bool
=
True
)
-
> typing
.
Dict
[
str
,
typing
.
Any
]
Get parameters for this estimator.
deep
bool, default True
Default True
. If True, will return the parameters for this estimator and contained subobjects that are estimators.
Dictionary
predict
predict
(
X
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
]
)
-
> bigframes
.
dataframe
.
DataFrame
Predict the result from input DataFrame.
X
bigframes.dataframe.DataFrame
or bigframes.series.Series
Input DataFrame or Series, schema is defined by the model.
register
register
(
vertex_ai_model_id
:
typing
.
Optional
[
str
]
=
None
)
-
> bigframes
.
ml
.
base
.
_T
Register the model to Vertex AI.
After register, go to Google Cloud Console ( https://console.cloud.google.com/vertex-ai/models ) to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options.
vertex_ai_model_id
Optional[str], default None
optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.
to_gbq
to_gbq
(
model_name
:
str
,
replace
:
bool
=
False
)
-
> bigframes
.
ml
.
imported
.
ONNXModel
Save the model to BigQuery.
model_name
str
the name of the model.
replace
bool, default False
whether to replace if the model already exists. Default to False.
ONNXModel
