The CREATE MODEL statement for importing TensorFlow models

This document describes the CREATE MODEL statement for importing TensorFlow models into BigQuery.

For information about the supported SQL statements and functions for each model type, see End-to-end user journey for each model.

CREATE MODEL syntax

{CREATE MODEL | CREATE MODEL IF NOT EXISTS | CREATE OR REPLACE MODEL}
model_name
OPTIONS(MODEL_TYPE = 'TENSORFLOW', MODEL_PATH = string_value
  [, KMS_KEY_NAME = string_value ]
);

CREATE MODEL

Creates and trains a new model in the specified dataset. If the model name exists, CREATE MODEL returns an error.

CREATE MODEL IF NOT EXISTS

Creates and trains a new model only if the model doesn't exist in the specified dataset.

CREATE OR REPLACE MODEL

Creates and trains a model and replaces an existing model with the same name in the specified dataset.

model_name

The name of the model you're creating or replacing. The model name must be unique in the dataset: no other model or table can have the same name. The model name must follow the same naming rules as a BigQuery table. A model name can:

  • Contain up to 1,024 characters
  • Contain letters (upper or lower case), numbers, and underscores

model_name is not case-sensitive.

If you don't have a default project configured, then you must prepend the project ID to the model name in the following format, including backticks:

`[PROJECT_ID].[DATASET].[MODEL]`

For example, `myproject.mydataset.mymodel`.

MODEL_TYPE

Syntax

MODEL_TYPE = 'TENSORFLOW'

Description

Specifies the model type. This option is required.

MODEL_PATH

Syntax

MODEL_PATH = string_value

Description

Specifies the Cloud Storage URI of the TensorFlow model to import. This option is required.

Arguments

A STRING value specifying the URI of a Cloud Storage bucket that contains the model to import.

BigQuery ML imports the model from Cloud Storage by using the credentials of the user who runs the CREATE MODEL statement.

Example

MODEL_PATH = 'gs://bucket/path/to/saved_model/*'

KMS_KEY_NAME

Syntax

KMS_KEY_NAME = string_value

Description

The Cloud Key Management Service customer-managed encryption key (CMEK) to use to encrypt the model.

Arguments

A STRING value containing the fully-qualified name of the CMEK. For example,

'projects/my_project/locations/my_location/keyRings/my_ring/cryptoKeys/my_key'

Supported data types for input and output columns

BigQuery ML converts some TensorFlow model input and output columns to BigQuery ML types, and some TensorFlow types aren't supported. Supported data types for input and output columns include the following:

TensorFlow types Supported BigQuery ML type
tf.int8, tf.int16, tf.int32, tf.int64, tf.uint8, tf.uint16, tf.uint32, tf.uint64 Supported INT64
tf.float16, tf.float32, tf.float64, tf.bfloat16 Supported FLOAT64
tf.complex64, tf.complex128 Unsupported N/a
tf.qint8, tf.quint8, tf.qint16, tf.quint16, tf.qint32 Unsupported N/A
tf.bool Supported BOOL
tf.string Supported STRING
tf.resource Unsupported N/A
tf.variant Unsupported N/A
SparseTensor of a supported TensorFlow type Supported A NULL, the associated BigQuery ML type, or an ARRAY of the associated BigQuery ML type.
tf.train.Example containing supported TensorFlow types Supported BigQuery ML automatically takes features and converts into a tf.train.Example.

The model input columns can be either dense Tensors or SparseTensors; RaggedTensors aren't supported. You can pass SparseTensors as dense arrays and BigQuery ML automatically converts them into Sparse format to pass into TensorFlow.

If the model expects input columns in tf.train.Example format, then BigQuery ML automatically determines the feature names and converts the input BigQuery columns into the model's expected format.

Limitations

Imported TensorFlow models have the following limitations:

  • The TensorFlow model must already exist before you can import it into BigQuery ML.
  • Models must be stored in Cloud Storage.
  • Models are frozen at the time of model creation.
  • TensorFlow models must be in SavedModel format.
  • The following functions don't support TensorFlow models:
    • ML.CONFUSION
    • ML.EVALUATE
    • ML.FEATURE
    • ML.ROC_CURVE
    • ML.TRAINING_INFO
    • ML.WEIGHTS
  • Models are limited to 450 MB in size.
  • Models trained using a version of GraphDef earlier than version 20 aren't supported.
  • Models trained using an unreleased version of TensorFlow aren't supported.
  • Only core TensorFlow operations are supported; models that use custom or tf.contrib operations aren't supported.
  • RaggedTensors aren't supported.
  • You can only use an imported TensorFlow model with an object table when you use capacity-based pricing through reservations. On-demand pricing isn't supported.

Example

The following example imports a TensorFlow model into BigQuery ML as a BigQuery ML model. The example assumes that there is an existing TensorFlow model located at gs://bucket/path/to/saved_model/*.

CREATE MODEL `project_id.mydataset.mymodel`
 OPTIONS(MODEL_TYPE='TENSORFLOW',
         MODEL_PATH="gs://bucket/path/to/saved_model/*")