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The CREATE MODEL statement for importing TensorFlow models | BigQuery

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This document describes the CREATE MODEL statement for importing TensorFlow models into BigQuery by using SQL. Alternatively, you can use the Google Cloud console user interface to create a model by using a UI (Preview) instead of constructing the SQL statement yourself.

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

{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 ]
);

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

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

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

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`.

Syntax

MODEL_TYPE = 'TENSORFLOW'

Description

Specifies the model type. This option is required.

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/*'

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'

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:

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.

For information about supported locations, see Locations for non-remote models.

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.

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/*")

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-11 UTC.

[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-11 UTC."],[[["The `CREATE MODEL` statement allows the import of existing TensorFlow models into BigQuery for use within BigQuery ML."],["The syntax requires specifying `MODEL_TYPE` as 'TENSORFLOW' and providing a `MODEL_PATH` to the Cloud Storage URI where the TensorFlow model is located."],["BigQuery ML supports a variety of TensorFlow data types, including integer, float, boolean, and string types, while some types such as complex, resource, variant and RaggedTensors are not supported."],["Imported TensorFlow models are subject to several limitations, such as being frozen at creation, must be in SavedModel format, and a size limit of 450 MB, among other limitations."],["The imported model can be managed using one of three statement variations: `CREATE MODEL` (fails if model exists), `CREATE MODEL IF NOT EXISTS` (only creates if model does not exist) and `CREATE OR REPLACE MODEL` (creates or replaces if it exists)."]]],[]]

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