Make predictions with imported TensorFlow models

---

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

Objectives

• Use the CREATE MODEL statement to import TensorFlow models into BigQuery ML.
• Use the ML.PREDICT function to make predictions with the imported TensorFlow models.

Costs

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

• BigQuery
• BigQuery ML

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free 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, see Clean 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.

   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.

   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. Make sure 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 the necessary 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 all of the required Identity and Access Management (IAM) permissions that you need to complete this tutorial.

If you are using an existing project, the BigQuery Studio Admin (roles/bigquery.studioAdmin) role grants all of the permissions 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 the IAM page.

   Go to IAM
2. Select the project.

3. In the Principal 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 the Role column to see whether the list of roles includes the required roles.

Grant the roles

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

   Go to IAM
2. Select the project.
3. Click Grant access.

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

5. In the Select a role list, select a role.
6. To grant additional roles, click Add another role and add each additional role.
7. Click Save.

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

Create a dataset

Create a BigQuery dataset to store your ML model.

Console

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

   Go to the BigQuery page

2. In the Explorer pane, click your project name.

3. Click View actions > Create dataset.

   

4. On the Create dataset page, do the following:

   • For Dataset ID, enter bqml_tutorial.

   • For Location type, select Multi-region, and then select US (multiple regions in United States).

   The public datasets are stored in the US multi-region. For simplicity, store your dataset in the same location.

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

   

bq

To create a new dataset, use the bq mk command with the --location flag. For a full list of possible parameters, see the bq mk --dataset command reference.

1. Create a dataset named bqml_tutorial with the data location set to US and a description of BigQuery 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 a dataset.

2. Confirm that the dataset was created:

   bq ls

API

Call the datasets.insert method with a defined dataset resource.

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

Import a TensorFlow model

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

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

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

To import the TensorFlow model into your dataset, follow these steps.

Console

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

   Go to the BigQuery page

2. For Create new, click SQL query.

3. In the query editor, enter this CREATE MODEL statement, and then click Run.

     CREATE OR REPLACE MODEL `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 like Successfully created model named imported_tf_model.

4. Your new model appears in the Resources panel. Models are indicated by the model icon: .

5. If you select the new model in the Resources panel, information about the model appears below the Query editor.

   

bq

1. Import the TensorFlow model from Cloud Storage by entering the following CREATE MODEL statement.

   bq query --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 the dataset.

   bq ls bqml_tutorial

   The output is similar to the following:

   tableId             Type
   ------------------- -------
   imported_tf_model   MODEL

API

Insert a new job and populate the jobs#configuration.query property 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/*')"
}

Replace PROJECT_ID with the name of your project and dataset.

BigQuery DataFrames

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

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

Import the model by using the TensorFlowModel object.

import bigframes
from bigframes.ml.imported import TensorFlowModel

bigframes.options.bigquery.project = PROJECT_ID
# You can change the location to one of the valid locations: https://cloud.google.com/bigquery/docs/locations#supported_locations
bigframes.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 into BigQuery ML, including format and storage requirements, see the CREATE MODEL statement for importing TensorFlow models.

Make predictions with the imported TensorFlow model

After importing the TensorFlow model, you use the ML.PREDICT function to make predictions with the model.

The following query uses imported_tf_model to make predictions using input data from the full table in the public dataset hacker_news. In the query, the TensorFlow model's serving_input_fn function specifies that the model expects a single input string named input. The subquery assigns the alias input to the title column in the subquery's SELECT statement.

To make predictions with the imported TensorFlow model, follow these steps.

Console

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

   Go to the BigQuery page

2. Under Create new, click SQL query.

3. In the query editor, enter this query that uses the ML.PREDICT function.

   SELECT *
     FROM ML.PREDICT(MODEL `bqml_tutorial.imported_tf_model`,
       (
        SELECT title AS input
        FROM bigquery-public-data.hacker_news.full
       )
   )

   The query results should look like this:

   

bq

Enter this command to run the query that uses ML.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 and populate the jobs#configuration.query property as in the request body. Replace project_id:bqml_tutorial with the name of your project.

{
  "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 the BigQuery quickstart using BigQuery DataFrames. For more information, see the BigQuery DataFrames reference documentation.

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

Use the predict function to run the remote model:

import bigframes.pandas as bpd

df = 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:

In the query results, the dense_1 column contains an array of probability values, and the input column contains the corresponding string values from the input table. Each array element value represents the probability that the corresponding input string is an article title from 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 an appspot.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 the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut 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 an appspot.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.

Delete a Google Cloud project:

gcloud projects delete PROJECT_ID

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

• For an overview of BigQuery ML, see Introduction to BigQuery ML.
• To get started using BigQuery ML, see Create machine learning models in BigQuery ML.
• For more information about importing TensorFlow models, see The CREATE MODEL statement for importing TensorFlow models.
• For more information about working with models, see these resources:
  • Get model metadata
  • Update model metadata
  • Manage models
• For more information on using the BigQuery DataFrames API in a BigQuery notebook, see:
  • Introduction to BigQuery notebooks
  • Overview of BigQuery DataFrames