Make predictions with PyTorch models in ONNX format

Open Neural Network Exchange (ONNX) provides a uniform format designed to represent any machine learning framework. BigQuery ML support for ONNX lets you:

  • Train a model using your favorite framework.
  • Convert the model into ONNX model format.
  • Import the ONNX model into BigQuery and make predictions using BigQuery ML.

This tutorial shows you how to import ONNX models trained with PyTorch into a BigQuery dataset and use them to make predictions from a SQL query.

Objectives

Costs

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

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.

    Go to project selector

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

    Go to project selector

  4. Make sure that billing is enabled for your Google Cloud project.

  5. Enable the BigQuery, BigQuery Connection, and Cloud Storage APIs.

    Enable the APIs

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

Required roles

If you create a new project, you're the project owner, and you're 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, do the following.

Make sure that you have the following role or roles on the project:

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 IAM permissions.

Optional: Train a model and convert it to ONNX format

The following code samples show you how to import a pretrained classification model into PyTorch and how to convert the resulting model into ONNX format. This tutorial uses a prebuilt example model stored at gs://cloud-samples-data/bigquery/ml/onnx/resnet18.onnx. You don't have to complete these steps if you're using the sample model.

Create a PyTorch vision model for image classification

Use the following code sample to import a PyTorch pretrained resnet18 model that accepts decoded image data returned by the BigQuery ML ML.DECODE_IMAGE and ML.RESIZE_IMAGE functions.

importtorchimporttorch.nnasnn# Define model input format to match the output format of# ML.DECODE_IMAGE function: [height, width, channels]dummy_input=torch.randn(1,224,224,3,device="cpu")# Load a pretrained pytorch model for image classificationmodel=torch.hub.load('pytorch/vision:v0.10.0','resnet18',pretrained=True)# Reshape input format from [batch_size, height, width, channels]# to [batch_size, channels, height, width]classReshapeLayer(nn.Module):def__init__(self):super().__init__()defforward(self,x):x=x.permute(0,3,1,2)# reorder dimensionsreturnxclassArgMaxLayer(nn.Module):def__init__(self):super().__init__()defforward(self,x):returntorch.argmax(x,dim=1)final_model=nn.Sequential(ReshapeLayer(),model,nn.Softmax(),ArgMaxLayer())

Convert the model into ONNX format

Use the following sample to export the PyTorch vision model using torch.onnx. The exported ONNX file is named resnet18.onnx.

torch.onnx.export(final_model,# model being rundummy_input,# model input"resnet18.onnx",# where to save the modelopset_version=10,# the ONNX version to export the model toinput_names=['input'],# the model's input namesoutput_names=['class_label'])# the model's output names

Upload the ONNX model to Cloud Storage

After you save your model, do the following:

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.

    The Create dataset menu option.

  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).

    • 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:

    bqls

API

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

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

Import the ONNX model into BigQuery

The following steps show you how to import the sample ONNX model from Cloud Storage into your dataset by using a CREATE MODEL statement.

Console

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

    Go to BigQuery Studio

  2. In the query editor, enter the following CREATE MODEL statement.

    CREATEORREPLACEMODEL`bqml_tutorial.imported_onnx_model`OPTIONS(MODEL_TYPE='ONNX',MODEL_PATH='BUCKET_PATH')

    Replace BUCKET_PATH with the path to the model that you uploaded to Cloud Storage. If you're using the sample model, replace BUCKET_PATH with the following value: gs://cloud-samples-data/bigquery/ml/onnx/resnet18.onnx.

    When the operation is complete, you see a message similar to the following: Successfully created model named imported_onnx_model.

    Your new model appears in the Resources panel. Models are indicated by the model icon: The model icon in the Resources panel. If you select the new model in the Resources panel, information about the model appears adjacent to the Query editor.

    The information panel for `imported_onnx_model`.

bq

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

    bqquery--use_legacy_sql=false\"CREATE OR REPLACE MODEL `bqml_tutorial.imported_onnx_model`OPTIONS (MODEL_TYPE='ONNX', MODEL_PATH='BUCKET_PATH')"

    Replace BUCKET_PATH with the path to the model that you uploaded to Cloud Storage. If you're using the sample model, replace BUCKET_PATH with this value: gs://cloud-samples-data/bigquery/ml/onnx/resnet18.onnx.

  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:

    tableIdType ---------------------------- imported_onnx_modelMODEL

For more information about importing ONNX models into BigQuery, including format and storage requirements, see The CREATE MODEL statement for importing ONNX models.

Create an object table in BigQuery to analyze image data

An object table is a read-only table over unstructured data objects that reside in Cloud Storage. Object tables let you analyze unstructured data from BigQuery.

In this tutorial, you use the ML.PREDICT function to output the predicted class label of an input image that is stored in a Cloud Storage bucket.

Creating the object table requires you to do the following:

  • Create a Cloud Storage bucket and upload an image of a goldfish.
  • Create a Cloud resource connection that is used to access the object table.
  • Grant access to the resource connection's service account.

Create a bucket and upload an image

Follow these steps to create a Cloud Storage bucket and to upload an image of a goldfish.

Console

  1. In the Google Cloud console, go to the Cloud Storage Buckets page.

    Go to Buckets

  2. Click Create.

  3. On the Create a bucket page, enter your bucket information.

    1. In the Get started section, do the following:

      1. In the box, enter bqml_images.

      2. Click Continue.

    2. In the Choose where to store your data section, do the following:

      1. For Location type, select Multi-region.

      2. From the location type's menu, select US (multiple regions in United States).

      3. Click Continue.

    3. In the Choose a storage class for your data section:

      1. Select Set a default class.

      2. Select Standard.

      3. Click Continue.

    4. In the remaining sections, leave the default values.

  4. Click Create.

Command line

Enter the following gcloud storage buckets create command:

gcloud storage buckets create gs://bqml_images --location=us

If the request is successful, the command returns the following message:

Creating gs://bqml_images/...

Upload an image to your Cloud Storage bucket

After the bucket is created, download an image of a goldfish, and upload it to your Cloud Storage bucket.

Complete the following steps to upload the image:

Console

  1. In the Google Cloud console, go to the Cloud Storage Buckets page.

    Go to Buckets

  2. In the list of buckets, click bqml_images.

  3. In the Objects tab for the bucket, do one of the following:

    • Drag the file from your desktop or file manager to the main pane in the Google Cloud console.

    • Click Upload > Upload files, select the image file you want to upload in the dialog that appears, then click Open.

Command line

Enter the following gcloud storage cp command:

gcloud storage cp OBJECT_LOCATION gs://bqml_images/IMAGE_NAME

Replace the following:

  • OBJECT_LOCATION: the local path to your image file. For example, Desktop/goldfish.jpg.
  • IMAGE_NAME: the name of the image. For example, goldfish.jpg.

If successful, the response is similar to the following:

Completed files 1/1 | 164.3kiB/164.3kiB

Create a BigQuery Cloud resource connection

You must have a Cloud resource connection to connect to the object table that you create later in this tutorial.

Cloud resource connections let you query data that's stored outside of BigQuery in Google Cloud services like Cloud Storage or Spanner, or in third-party sources like AWS or Azure. These external connections use the BigQuery Connection API.

Follow these steps to create your Cloud resource connection.

Console

  1. Go to the BigQuery Studio page.

    Go to BigQuery Studio

  2. In the Explorer pane, click Add data.

    The Add data dialog opens.

  3. In the Filter By pane, in the Data Source Type section, select Databases.

    Alternatively, in the Search for data sources field, you can enter Vertex AI.

  4. In the Featured data sources section, click Vertex AI.

  5. Click the Vertex AI Models: BigQuery Federation solution card.

  6. In the Connection type list, select Vertex AI remote models, remote functions and BigLake (Cloud Resource).

  7. In the Connection ID field, enter bqml_tutorial.

  8. Verify that Multi-region—US is selected.

  9. Click Create connection.

  10. At the bottom of the window, click Go to connection. Alternatively, in the Explorer pane, expand External connections, and then click us.bqml_tutorial.

  11. In the Connection info pane, copy the service account ID. You need this ID when you configure permissions for the connection. When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.

bq

  1. Create a connection:

    bqmk--connection--location=US--project_id=PROJECT_ID\--connection_type=CLOUD_RESOURCEbqml_tutorial

    Replace PROJECT_ID with your Google Cloud project ID. The --project_id parameter overrides the default project.

    When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.

    Troubleshooting: If you get the following connection error, update the Google Cloud SDK:

     Flags parsing error: flag --connection_type=CLOUD_RESOURCE: value should be one of...

  2. Retrieve and copy the service account ID for use in a later step:

    bqshow--connectionPROJECT_ID.us.bqml_tutorial

    The output is similar to the following:

     name properties 1234.REGION.CONNECTION_ID {"serviceAccountId": "connection-1234-9u56h9@gcp-sa-bigquery-condel.iam.gserviceaccount.com"}

Set up connection access

Grant the Storage Object Admin role to the Cloud resource connection's service account. You must grant this role in the same project where you created the remote model endpoint.

To grant the role, follow these steps:

  1. Go to the IAM & Admin page.

    Go to IAM & Admin

  2. Click Grant Access.

  3. In the New principals field, enter the Cloud resource connection's Service account ID that you copied previously.

  4. In the Select a role field, choose Cloud Storage, and then select Storage object admin.

  5. Click Save.

Create the object table

Follow these steps to create an object table named goldfish_image_table using the goldfish image you uploaded to Cloud Storage.

Console

  1. Go to the BigQuery Studio page.

    Go to BigQuery Studio

  2. In the query editor, enter this query to create the object table.

    CREATEEXTERNALTABLE`bqml_tutorial.goldfish_image_table`WITHCONNECTION`us.bqml_tutorial`OPTIONS(object_metadata='SIMPLE',uris=['gs://bqml_images/IMAGE_NAME'],max_staleness=INTERVAL1DAY,metadata_cache_mode='AUTOMATIC');

    Replace IMAGE_NAME with the name of the image file—for example, goldfish.jpg.

    When the operation is complete, you see a message like This statement created a new table named goldfish_image_table.

bq

  1. Create the object table by entering the following CREATE EXTERNAL TABLE statement.

    bqquery--use_legacy_sql=false\"CREATE EXTERNAL TABLE `bqml_tutorial.goldfish_image_table`WITH CONNECTION `us.bqml_tutorial`OPTIONS(object_metadata = 'SIMPLE',uris = ['gs://bqml_images/IMAGE_NAME'],max_staleness = INTERVAL 1 DAY,metadata_cache_mode = 'AUTOMATIC')"

    Replace IMAGE_NAME with the name of the image file—for example, goldfish.jpg.

  2. After you create the object table, verify that it appears in the dataset.

    bq ls bqml_tutorial

    The output is similar to the following:

    tableIdType ----------------------------- goldfish_image_tableEXTERNAL

For more information, see Create object tables.

Make predictions with the imported ONNX model

You use the following query that contains the ML.PREDICT function to make predictions from image data in the input object table goldfish_image_table. This query outputs the predicted class label of the input image based on the ImageNet labels dictionary.

In the query, the ML.DECODE_IMAGE function is required to decode the image data so that it can be interpreted by ML.PREDICT. The ML.RESIZE_IMAGE function is called to resize the image to fit the size of the model's input (224*224).

For more information about running inference on image object tables, see Run inference on image object tables.

To make predictions from your image data, do the following.

Console

  1. Go to the BigQuery Studio page.

    Go to BigQuery Studio

  2. In the query editor, enter the following ML.PREDICT query.

    SELECTclass_labelFROMML.PREDICT(MODELbqml_tutorial.imported_onnx_model,(SELECTML.RESIZE_IMAGE(ML.DECODE_IMAGE(DATA),224,224,FALSE)ASinputFROMbqml_tutorial.goldfish_image_table))

    The query results are similar to the following:

    The results of the ML.PREDICT query

bq

Enter the following bq query command:

bq query --use_legacy_sql=false \ 'SELECT class_label FROM ML.PREDICT(MODEL `bqml_tutorial.imported_onnx_model`, ( SELECT ML.RESIZE_IMAGE(ML.DECODE_IMAGE(DATA), 224, 224, FALSE) AS input FROM bqml_tutorial.goldfish_image_table))'

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

  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

    Delete a Google Cloud project:

    gcloud projects delete PROJECT_ID

Delete individual resources

Alternatively, to remove the individual resources used in this tutorial, do the following:

  1. Delete the imported model.

  2. (Optional) Delete the dataset.

  3. Delete the Cloud resource connection.

  4. Delete the Cloud Storage bucket.

What's next