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Showing content from http://cloud.google.com/bigquery/docs/making-predictions-with-pytorch-models-in-onnx-format below:

Make predictions with PyTorch models in ONNX format | BigQuery

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.

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.

import torch
import torch.nn as nn

# 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 classification
model = 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]
class ReshapeLayer(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        x = x.permute(0, 3, 1, 2)  # reorder dimensions
        return x

class ArgMaxLayer(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
       return torch.argmax(x, dim=1)

final_model = nn.Sequential(
    ReshapeLayer(),
    model,
    nn.Softmax(),
    ArgMaxLayer()
)

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 run
                  dummy_input,            # model input
                  "resnet18.onnx",        # where to save the model
                  opset_version=10,       # the ONNX version to export the model to
                  input_names = ['input'],         # the model's input names
                  output_names = ['class_label'])  # the model's output names

After you save your model, do the following:

• Create a Cloud Storage bucket to store the model.
• Upload the ONNX model to your Cloud Storage bucket.

Create a BigQuery dataset to store your ML model.

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 more_vert 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).

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

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

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

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

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.

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

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.

   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 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: If you select the new model in the Resources panel, information about the model appears adjacent to the Query editor.

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

   bq query --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:

   tableId               Type
   --------------------- -------
   imported_onnx_model  MODEL

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

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.

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

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

   Go to Buckets

2. Click add_box 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.

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/...

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:

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.

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

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.

1. Go to the BigQuery Studio page.

   Go to BigQuery Studio

2. In the Explorer pane, click add 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.

1. Create a connection:

   bq mk --connection --location=US --project_id=PROJECT_ID \
       --connection_type=CLOUD_RESOURCE bqml_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:

   bq show --connection PROJECT_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"}
   

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 person_add 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.

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

1. Go to the BigQuery Studio page.

   Go to BigQuery Studio

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

   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.

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

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

   bq query --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:

   tableId               Type
   --------------------- --------
   goldfish_image_table  EXTERNAL

For more information, see Create object tables.

If you don't have a reservation, running a query using ML.PREDICT produces this error: BigQuery ML inference using imported models and object tables requires a reservation, but no reservations were assigned for job type `QUERY`...`.

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.

1. Go to the BigQuery Studio page.

   Go to BigQuery Studio

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

    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))
    

   The query results are similar to the following:

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))'

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