Make predictions with imported TensorFlow models

In this tutorial, you import TensorFlow models into aBigQuery ML dataset. Then, you use a SQL query to make predictions fromthe imported models.

Objectives

  • Use theCREATE MODEL statement to import TensorFlow modelsinto BigQuery ML.
  • Use theML.PREDICT function to make predictions with the importedTensorFlow models.

Costs

In this document, you use the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use thepricing calculator.

New Google Cloud users might be eligible for afree trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, seeClean up.

Before you begin

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.
    Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

    Go to project selector

  3. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.
    Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

    Go to project selector

  4. Verify that billing is enabled for your Google Cloud project.

  5. Ensure that the BigQuery API is enabled.

    Enable the API

  6. Ensure that you have thenecessary permissions to perform the tasks in this document.

Required roles

If you create a new project, you are the project owner, and you are granted allof the required Identity and Access Management (IAM) permissions that you need to completethis tutorial.

If you are using an existing project, theBigQuery Studio Admin (roles/bigquery.studioAdmin) role grants all of thepermissions that are needed to complete this tutorial.

Make sure that you have the following role or roles on the project:BigQuery Studio Admin (roles/bigquery.studioAdmin).

Check for the roles

  1. In the Google Cloud console, go to theIAM page.

    Go to IAM
  2. Select the project.

  3. In thePrincipal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

  4. For all rows that specify or include you, check theRole column to see whether the list of roles includes the required roles.

Grant the roles

  1. In the Google Cloud console, go to theIAM page.

    Go to IAM
  2. Select the project.
  3. ClickGrant access.

  4. In theNew principals field, enter your user identifier. This is typically the email address for a Google Account.

  5. In theSelect a role list, select a role.
  6. To grant additional roles, clickAdd another role and add each additional role.
  7. ClickSave.

For more information about IAM permissions in BigQuery,seeBigQuery permissions.

Create a dataset

Create a BigQuery dataset to store your ML model.

Console

  1. In the Google Cloud console, go to theBigQuery page.

    Go to the BigQuery page

  2. In theExplorer pane, click your project name.

  3. ClickView actions > Create dataset

  4. On theCreate dataset page, do the following:

    • ForDataset ID, enterbqml_tutorial.

    • ForLocation type, selectMulti-region, and then selectUS (multiple regions in United States).

    • Leave the remaining default settings as they are, and clickCreate dataset.

bq

To create a new dataset, use thebq mk commandwith the--location flag. For a full list of possible parameters, see thebq mk --dataset commandreference.

  1. Create a dataset namedbqml_tutorial with the data location set toUSand a description ofBigQuery ML tutorial dataset:

    bq --location=US mk -d \ --description "BigQuery ML tutorial dataset." \ bqml_tutorial

    Instead of using the--dataset flag, the command uses the-d shortcut.If you omit-d and--dataset, the command defaults to creating adataset.

  2. Confirm that the dataset was created:

    bqls

API

Call thedatasets.insertmethod with a defineddataset resource.

{"datasetReference":{"datasetId":"bqml_tutorial"}}

BigQuery DataFrames

Before trying this sample, follow the BigQuery DataFrames setup instructions in theBigQuery quickstart using BigQuery DataFrames. For more information, see theBigQuery DataFrames reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, seeSet up ADC for a local development environment.

importgoogle.cloud.bigquerybqclient=google.cloud.bigquery.Client()bqclient.create_dataset("bqml_tutorial",exists_ok=True)

Import a TensorFlow model

The following steps show you how to import a model from Cloud Storage.The path to the model isgs://cloud-training-demos/txtclass/export/exporter/1549825580/*. The importedmodel name isimported_tf_model.

Note the Cloud Storage URI ends in a wildcard character (*).This character indicates that BigQuery ML should import any assetsassociated with the model.

The imported model is a TensorFlow text classifier model thatpredicts which website published a given article title.

To import the TensorFlow model into your dataset, follow thesesteps.

Console

  1. In the Google Cloud console, go to theBigQuery page.

    Go to the BigQuery page

  2. ForCreate new, clickSQL query.

  3. In the query editor, enter thisCREATE MODEL statement, and then clickRun.

    CREATEORREPLACEMODEL`bqml_tutorial.imported_tf_model`OPTIONS(MODEL_TYPE='TENSORFLOW',MODEL_PATH='gs://cloud-training-demos/txtclass/export/exporter/1549825580/*')

    When the operation is complete, you should see a message likeSuccessfully created model named imported_tf_model.

  4. Your new model appears in theResources panel. Models areindicated by the model icon:modelicon.

  5. If you select the new model in theResources panel, informationabout the model appears below theQuery editor.

    TensorFlow model info

bq

  1. Import the TensorFlow model from Cloud Storageby entering the followingCREATE MODEL statement.

    bqquery--use_legacy_sql=false\"CREATE OR REPLACE MODEL`bqml_tutorial.imported_tf_model`OPTIONS(MODEL_TYPE='TENSORFLOW',MODEL_PATH='gs://cloud-training-demos/txtclass/export/exporter/1549825580/*')"

  2. After you import the model, verify that the model appears in thedataset.

    bq ls -m bqml_tutorial

    The output is similar to the following:

    tableIdType--------------------------imported_tf_modelMODEL

API

Insert a new job andpopulate thejobs#configuration.queryproperty in the request body.

{"query":"CREATE MODEL `PROJECT_ID:bqml_tutorial.imported_tf_model` OPTIONS(MODEL_TYPE='TENSORFLOW' MODEL_PATH='gs://cloud-training-demos/txtclass/export/exporter/1549825580/*')"}

ReplacePROJECT_ID with the name of yourproject and dataset.

BigQuery DataFrames

Before trying this sample, follow the BigQuery DataFrames setup instructions in theBigQuery quickstart using BigQuery DataFrames. For more information, see theBigQuery DataFrames reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, seeSet up ADC for a local development environment.

Import the model by using theTensorFlowModel object.

importbigframesfrombigframes.ml.importedimportTensorFlowModelbigframes.options.bigquery.project=PROJECT_ID# You can change the location to one of the valid locations: https://cloud.google.com/bigquery/docs/locations#supported_locationsbigframes.options.bigquery.location="US"imported_tensorflow_model=TensorFlowModel(model_path="gs://cloud-training-demos/txtclass/export/exporter/1549825580/*")

For more information about importing TensorFlow models intoBigQuery ML, including format and storage requirements, see theCREATE MODEL statement for importing TensorFlow models.

Make predictions with the imported TensorFlow model

After importing the TensorFlow model, you use theML.PREDICT functionto make predictions with the model.

The following query usesimported_tf_model to make predictions using inputdata from thefull table in the public datasethacker_news. In the query,the TensorFlow model'sserving_input_fn function specifies thatthe model expects a single input string namedinput. The subquery assigns thealiasinput to thetitle column in the subquery'sSELECT statement.

To make predictions with the imported TensorFlow model, followthese steps.

Console

  1. In the Google Cloud console, go to theBigQuery page.

    Go to the BigQuery page

  2. UnderCreate new, clickSQL query.

  3. In the query editor, enter this query that uses theML.PREDICTfunction.

    SELECT*FROMML.PREDICT(MODEL`bqml_tutorial.imported_tf_model`,(SELECTtitleASinputFROMbigquery-public-data.hacker_news.full))

    The query results should look like this:

    Query results

bq

Enter this command to run the query that usesML.PREDICT.

bq query \--use_legacy_sql=false \'SELECT *FROM ML.PREDICT(  MODEL`bqml_tutorial.imported_tf_model`,  (SELECT title AS input FROM`bigquery-public-data.hacker_news.full`))'

The results should look like this:

+------------------------------------------------------------------------+----------------------------------------------------------------------------------+|                               dense_1                                  |                                       input                                      |+------------------------------------------------------------------------+----------------------------------------------------------------------------------+|   ["0.6251608729362488","0.2989124357700348","0.07592673599720001"]    | How Red Hat Decides Which Open Source Companies t...                             ||   ["0.014276246540248394","0.972910463809967","0.01281337533146143"]   | Ask HN: Toronto/GTA mastermind around side income for big corp. dev?             ||   ["0.9821603298187256","1.8601855117594823E-5","0.01782100833952427"] | Ask HN: What are good resources on strategy and decision making for your career? ||   ["0.8611106276512146","0.06648492068052292","0.07240450382232666"]   | Forget about promises, use harvests                                              |+------------------------------------------------------------------------+----------------------------------------------------------------------------------+

API

Insert a new job andpopulate thejobs#configuration.queryproperty as in the request body. Replaceproject_id with the name of yourproject.

{"query":"SELECT * FROM ML.PREDICT(MODEL `project_id.bqml_tutorial.imported_tf_model`, (SELECT * FROM input_data))"}

BigQuery DataFrames

Before trying this sample, follow the BigQuery DataFrames setup instructions in theBigQuery quickstart using BigQuery DataFrames. For more information, see theBigQuery DataFrames reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, seeSet up ADC for a local development environment.

Use thepredict function to run the TensorFlow model:

importbigframes.pandasasbpddf=bpd.read_gbq("bigquery-public-data.hacker_news.full")df_pred=df.rename(columns={"title":"input"})predictions=imported_tensorflow_model.predict(df_pred)predictions.head(5)

The results should look like this:

Result_visualization

In the query results, thedense_1 column contains an array ofprobability values, and theinput column contains the correspondingstring values from the input table. Each array element value representsthe probability that the corresponding input string is an article titlefrom a particular publication.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

Console

    Caution: Deleting a project has the following effects:
    • Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
    • Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as anappspot.com URL, delete selected resources inside the project instead of deleting the whole project.

    If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

  1. In the Google Cloud console, go to theManage resources page.

    Go to Manage resources

  2. In the project list, select the project that you want to delete, and then clickDelete.
  3. In the dialog, type the project ID, and then clickShut down to delete the project.

gcloud

    Caution: Deleting a project has the following effects:
    • Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
    • Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as anappspot.com URL, delete selected resources inside the project instead of deleting the whole project.

    If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

  1. In the Google Cloud console, go to theManage resources page.

    Go to Manage resources

  2. In the project list, select the project that you want to delete, and then clickDelete.
  3. In the dialog, type the project ID, and then clickShut down to delete the project.

Delete individual resources

Alternatively, remove the individual resources used in this tutorial:

  1. Delete the imported model.

  2. Optional:Delete the dataset.

What's next

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Last updated 2025-12-15 UTC.