Configure explanations

To use Vertex Explainable AI with a custom-trained model, you must configure certain options when you create the Model resource that you plan to request explanations from, when you deploy the model, or when you submit a batch explanation job. This page describes configuring these options.

If you want to use Vertex Explainable AI with an AutoML tabular model, then you don't need to perform any configuration; Vertex AI automatically configures the model for Vertex Explainable AI. Skip this document and read Getting explanations.

When and where to configure explanations

You configure explanations when you create or import a model. You can also configure explanations on a model that you have already created, even if you didn't configuring explanations previously.

Configure explanations when creating or importing models

When you create or import a Model, you can set a default configuration for all its explanations using the Model's explanationSpec field.

You can create a custom-trained Model in Vertex AI in two ways:

• Import a Model.
• Create a custom TrainingPipeline resource that imports a Model.

In either case, you can configure the Model to support Vertex Explainable AI. The examples in this document assume that you are importing a Model. To configure Vertex Explainable AI when you create a custom-trained Model using a TrainingPipeline, use the configuration settings described in this document in the TrainingPipeline's modelToUpload field.

Configure explanations when deploying models or getting batch predictions

When you deploy a Model to an Endpoint resource, it creates a DeployedModel. You can set a default explanation configuration for a DeployedModel by populating its explanationSpec field. You can use this step to override configuration set when you created the Model.

Similarly, when you get batch predictions from a Model and request explanations as part of your batch prediction request, you can override some or all of the Model's explanation configuration by populating the BatchPredictionJob resource's explanationSpec field.

These options are useful if you didn't originally plan to get explanations (an omitted the explanationSpec field when you created the model), but decide later that you want explanations for the Model.

Override the configuration when getting online explanations

Regardless of whether you created or imported the Model with explanation settings, and regardless of whether you configured explanation settings during deployment, you can override the Model's initial explanation settings when you get online explanations.

When you send an explain request to Vertex AI, you can override some of the explanation configuration that you previously set for the Model or the DeployedModel.

In the explain request, you can override the following fields:

• Input baselines for any custom-trained model
• Visualization configuration for image models
• ExplanationParameters except for the method

Override these settings in the explanation request's explanationSpecOverride field.

Import a model with an explanationSpec field

Depending on whether you serve predictions using a pre-built container or a custom container, specify slightly different details for the ExplanationSpec. Select the tab that matches the container that you are using:

TensorFlow pre-built container

You can use any of the following attribution methods for Vertex Explainable AI. Read the comparison of feature attribution methods to select the appropriate one for your Model:

The following examples assume that your model accepts a single input feature; this is often the case for sequential Keras models and many other TensorFlow models. Since the feature is a tensor of any shape, it can in fact encode many pieces of information. When you request explanations, Vertex Explainable AI provides attributions for each element of the tensor.

If your model accepts multiple named input tensors, then add corresponding entries to the inputs field.

Sampled Shapley

Depending on which tool you want to use to create or import the Model, select one of the following tabs:

Console

Follow the guide to importing a model using the Cloud Console. When you get to the Explainability step, do the following:

1. For your feature attribution method, select Sampled Shapely (for tabular models).

2. Set the path count to the number of feature permutations to use for the Sampled Shapley attribution method. This must be an integer in the range [1, 50].

   A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

3. Configure each input feature in your model:

   1. Fill in your input feature name.

   2. Optionally, you can add one or more input baselines. Otherwise, Explainable AI chooses input baselines for your model.

   3. If you're importing a TensorFlow model, there are additional input fields:

      1. Fill out the Input tensor name.

      2. If applicable, fill in the Indices tensor name and/or the Dense shape tensor name.

      3. The Modality cannot be updated here. It is set automatically to NUMERIC for tabular models, or IMAGE for image models.

      4. If applicable, set the Encoding field. This defaults to IDENTITY if not set.

      5. If applicable, set the Group name field.

4. If you're importing a TensorFlow model, specify output fields:

   1. Set the Output name of your feature.
   2. Set the Output tensor name of your feature.
   3. If applicable, set the Index display name mapping.
   4. If applicable, set the Display name mapping key.

5. Click the Import button when you have finished configuring the explainability settings.

gcloud

1. Write the following ExplanationMetadata to a JSON file in your local environment. The filename does not matter, but for this example call the file explanation-metadata.json:

   explanation-metadata.json

   {
     "inputs": {
       "FEATURE_NAME": {
         "inputTensorName": "INPUT_TENSOR_NAME",
       }
     },
     "outputs": {
       "OUTPUT_NAME": {
         "outputTensorName": "OUTPUT_TENSOR_NAME"
       }
     }
   }
   

   Replace the following:

   • FEATURE_NAME: Any memorable name for your input feature.
   • INPUT_TENSOR_NAME: The name of the input tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.
   • OUTPUT_NAME: Any memorable name for the output of your model.
   • OUTPUT_TENSOR_NAME: The name of the output tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.

   You can optionally add input baselines to the ExplanationMetadata. Otherwise, Vertex AI chooses input baselines for the Model.

2. Run the following command to create a Model resource that supports Vertex Explainable AI. The flags most pertinent to Vertex Explainable AI are highlighted.

   gcloud ai models upload \
     --region=LOCATION \
     --display-name=MODEL_NAME \
     --container-image-uri=IMAGE_URI \
     --artifact-uri=PATH_TO_MODEL_ARTIFACT_DIRECTORY \
     --explanation-method=sampled-shapley \
     --explanation-path-count=PATH_COUNT \
     --explanation-metadata-file=explanation-metadata.json
   

   Replace the following:

   • IMAGE_URI: The URI of a TensorFlow pre-built container for serving predictions.

   • PATH_COUNT: The number of feature permutations to use for the Sampled Shapley attribution method. Must be an integer in the range [1, 50].

     A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

   To learn about appropriate values for the other placeholders, read Importing models.

REST & CMD LINE

Before using any of the request data, make the following replacements:

• IMAGE_URI: The URI of a TensorFlow pre-built container for serving predictions.

• PATH_COUNT: The number of feature permutations to use for the Sampled Shapley attribution method. Must be an integer in the range [1, 50].

  A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

• FEATURE_NAME: Any memorable name for your input feature.
• INPUT_TENSOR_NAME: The name of the input tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.
• OUTPUT_NAME: Any memorable name for the output of your model.
• OUTPUT_TENSOR_NAME: The name of the output tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.

To learn about appropriate values for the other placeholders, read Importing models.

You can optionally add input baselines to the ExplanationMetadata. Otherwise, Vertex AI chooses input baselines for the Model.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload

Request JSON body:

{
  "model": {
    "displayName": "MODEL_NAME",
    "containerSpec": {
      "imageUri": "IMAGE_URI"
    },
  "artifactUri": "PATH_TO_MODEL_ARTIFACT_DIRECTORY",
  "explanationSpec": {
    "parameters": {
      "sampledShapleyAttribution": {
        "pathCount": PATH_COUNT
      }
    },
    "metadata": {
       "inputs": {
         "FEATURE_NAME": {
           "inputTensorName": "INPUT_TENSOR_NAME",
         }
       },
       "outputs": {
         "OUTPUT_NAME": {
           "outputTensorName": "OUTPUT_TENSOR_NAME"
         }
       }
    }
  }
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method POST `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -InFile request.json `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload" | Select-Object -Expand Content

Integrated Gradients

Depending on which tool you want to use to create or import the Model, select one of the following tabs:

Console

Follow the guide to importing a model using the Cloud Console. When you get to the Explainability step, do the following:

1. For your feature attribution method, select Integrated gradients (for tabular models) or Integrated gradients (for image classification models), depending on which is more appropriate for your model.

2. If you are importing an image classification model, do the following:

   1. Set the Visualization type and Color map.

   2. You can leave the Clip below, Clip above, Overlay type, and Number of integral steps at their default settings.

   Learn more about visualization settings.

3. Set the number of steps to use for approximating the path integral during feature attribution. This must be an integer in the range [1, 100].

   A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 50.

4. Configure each input feature in your model:

   1. Fill in your input feature name.

   2. Optionally, you can add one or more input baselines. Otherwise, Explainable AI chooses input baselines for your model.

   3. If you're importing a TensorFlow model, there are additional input fields:

      1. Fill out the Input tensor name.

      2. If applicable, fill in the Indices tensor name and/or the Dense shape tensor name.

      3. The Modality cannot be updated here. It is set automatically to NUMERIC for tabular models, or IMAGE for image models.

      4. If applicable, set the Encoding field. This defaults to IDENTITY if not set.

      5. If applicable, set the Group name field.

5. If you're importing a TensorFlow model, specify output fields:

   1. Set the Output name of your feature.
   2. Set the Output tensor name of your feature.
   3. If applicable, set the Index display name mapping.
   4. If applicable, set the Display name mapping key.

6. Click the Import button when you have finished configuring the explainability settings.

gcloud

1. Write the following ExplanationMetadata to a JSON file in your local environment. The filename does not matter, but for this example call the file explanation-metadata.json:

   explanation-metadata.json

   {
     "inputs": {
       "FEATURE_NAME": {
         "inputTensorName": "INPUT_TENSOR_NAME",
         "modality": "MODALITY",
         "visualization": VISUALIZATION_SETTINGS
       }
     },
     "outputs": {
       "OUTPUT_NAME": {
         "outputTensorName": "OUTPUT_TENSOR_NAME"
       }
     }
   }
   

   Replace the following:

   • FEATURE_NAME: Any memorable name for your input feature.
   • INPUT_TENSOR_NAME: The name of the input tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.
   • MODALITY: image if the Model accepts images as input or numeric if the Model accepts tabular data as input. Defaults to numeric.

   • VIZUALIZATION_OPTIONS: Options for visualizing explanations. To learn how to populate this field, read Configuring visualization settings for image data.

     If you omit the modality field or set the modality field to numeric, then omit the visualization field entirely.

   • OUTPUT_NAME: Any memorable name for the output of your model.
   • OUTPUT_TENSOR_NAME: The name of the output tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.

   You can optionally add input baselines to the ExplanationMetadata. Otherwise, Vertex AI chooses input baselines for the Model.

2. Run the following command to create a Model resource that supports Vertex Explainable AI. The flags most pertinent to Vertex Explainable AI are highlighted.

   gcloud ai models upload \
     --region=LOCATION \
     --display-name=MODEL_NAME \
     --container-image-uri=IMAGE_URI \
     --artifact-uri=PATH_TO_MODEL_ARTIFACT_DIRECTORY \
     --explanation-method=integrated-gradients \
     --explanation-step-count=STEP_COUNT \
     --explanation-metadata-file=explanation-metadata.json
   

   Replace the following:

   • IMAGE_URI: The URI of a TensorFlow pre-built container for serving predictions.
   • STEP_COUNT: The number of steps to use for approximating the path integral during feature attribution. Must be an integer in the range [1, 100].

     A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 50.

   To learn about appropriate values for the other placeholders, read Importing models.

   You can optionally add flags to configure the SmoothGrad approximation of gradients.

REST & CMD LINE

Before using any of the request data, make the following replacements:

• IMAGE_URI: The URI of a TensorFlow pre-built container for serving predictions.
• STEP_COUNT: The number of steps to use for approximating the path integral during feature attribution. Must be an integer in the range [1, 100].

  A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 50.

• FEATURE_NAME: Any memorable name for your input feature.
• INPUT_TENSOR_NAME: The name of the input tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.
• MODALITY: image if the Model accepts images as input or numeric if the Model accepts tabular data as input. Defaults to numeric.

• VIZUALIZATION_OPTIONS: Options for visualizing explanations. To learn how to populate this field, read Configuring visualization settings for image data.

  If you omit the modality field or set the modality field to numeric, then omit the visualization field entirely.

• OUTPUT_NAME: Any memorable name for the output of your model.
• OUTPUT_TENSOR_NAME: The name of the output tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.

To learn about appropriate values for the other placeholders, read Importing models.

You can optionally add input baselines to the ExplanationMetadata. Otherwise, Vertex AI chooses input baselines for the Model.

You can optionally add fields to configure the SmoothGrad approximation of gradients to the ExplanationParameters.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload

Request JSON body:

{
  "model": {
    "displayName": "MODEL_NAME",
    "containerSpec": {
      "imageUri": "IMAGE_URI"
    },
  "artifactUri": "PATH_TO_MODEL_ARTIFACT_DIRECTORY",
  "explanationSpec": {
    "parameters": {
      "integratedGradientsAttribution": {
        "stepCount": STEP_COUNT
      }
    },
    "metadata": {
       "inputs": {
         "FEATURE_NAME": {
           "inputTensorName": "INPUT_TENSOR_NAME",
           "modality": "MODALITY",
           "visualization": VISUALIZATION_SETTINGS
         }
       },
       "outputs": {
         "OUTPUT_NAME": {
           "outputTensorName": "OUTPUT_TENSOR_NAME"
         }
       }
    }
  }
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method POST `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -InFile request.json `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload" | Select-Object -Expand Content

XRAI

Depending on which tool you want to use to create or import the Model, select one of the following tabs:

Console

Follow the guide to importing a model using the Cloud Console. When you get to the Explainability step, do the following:

1. For your feature attribution method, select XRAI (for image classification models).

2. Set the following visualization options:

   1. Set the Color map.

   2. You can leave the Clip below, Clip above, Overlay type, and Number of integral steps at their default settings.

      Learn more about visualization settings.

3. Set the number of steps to use for approximating the path integral during feature attribution. This must be an integer in the range [1, 100].

   A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 50.

4. Configure each input feature in your model:

   1. Fill in your input feature name.

   2. Optionally, you can add one or more input baselines. Otherwise, Explainable AI chooses input baselines for your model.

   3. If you're importing a TensorFlow model, there are additional input fields:

      1. Fill out the Input tensor name.

      2. If applicable, fill in the Indices tensor name and/or the Dense shape tensor name.

      3. The Modality cannot be updated here. It is set automatically to NUMERIC for tabular models, or IMAGE for image models.

      4. If applicable, set the Encoding field. This defaults to IDENTITY if not set.

      5. If applicable, set the Group name field.

5. If you're importing a TensorFlow model, specify output fields:

   1. Set the Output name of your feature.
   2. Set the Output tensor name of your feature.
   3. If applicable, set the Index display name mapping.
   4. If applicable, set the Display name mapping key.

6. Click the Import button when you have finished configuring the explainability settings.

gcloud

1. Write the following ExplanationMetadata to a JSON file in your local environment. The filename does not matter, but for this example call the file explanation-metadata.json:

   explanation-metadata.json

   {
     "inputs": {
       "FEATURE_NAME": {
         "inputTensorName": "INPUT_TENSOR_NAME",
         "modality": "image",
         "visualization": VISUALIZATION_SETTINGS
       }
     },
     "outputs": {
       "OUTPUT_NAME": {
         "outputTensorName": "OUTPUT_TENSOR_NAME"
       }
     }
   }
   

   Replace the following:

   • FEATURE_NAME: Any memorable name for your input feature.
   • INPUT_TENSOR_NAME: The name of the input tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.
   • VIZUALIZATION_OPTIONS: Options for visualizing explanations. To learn how to populate this field, read Configuring visualization settings for image data.
   • OUTPUT_NAME: Any memorable name for the output of your model.
   • OUTPUT_TENSOR_NAME: The name of the output tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.

   You can optionally add input baselines to the ExplanationMetadata. Otherwise, Vertex AI chooses input baselines for the Model.

2. Run the following command to create a Model resource that supports Vertex Explainable AI. The flags most pertinent to Vertex Explainable AI are highlighted.

   gcloud ai models upload \
     --region=LOCATION \
     --display-name=MODEL_NAME \
     --container-image-uri=IMAGE_URI \
     --artifact-uri=PATH_TO_MODEL_ARTIFACT_DIRECTORY \
     --explanation-method=xrai \
     --explanation-step-count=STEP_COUNT \
     --explanation-metadata-file=explanation-metadata.json
   

   Replace the following:

   • IMAGE_URI: The URI of a TensorFlow pre-built container for serving predictions.
   • STEP_COUNT: The number of steps to use for approximating the path integral during feature attribution. Must be an integer in the range [1, 100].

     A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 50.

   To learn about appropriate values for the other placeholders, read Importing models.

   You can optionally add flags to configure the SmoothGrad approximation of gradients.

REST & CMD LINE

Before using any of the request data, make the following replacements:

• IMAGE_URI: The URI of a TensorFlow pre-built container for serving predictions.
• STEP_COUNT: The number of steps to use for approximating the path integral during feature attribution. Must be an integer in the range [1, 100].

  A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 50.

• FEATURE_NAME: Any memorable name for your input feature.
• INPUT_TENSOR_NAME: The name of the input tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.
• VIZUALIZATION_OPTIONS: Options for visualizing explanations. To learn how to populate this field, read Configuring visualization settings for image data.
• OUTPUT_NAME: Any memorable name for the output of your model.
• OUTPUT_TENSOR_NAME: The name of the output tensor in TensorFlow. To find the correct value for this field, read Using TensorFlow with Vertex Explainable AI.

To learn about appropriate values for the other placeholders, read Importing models.

You can optionally add input baselines to the ExplanationMetadata. Otherwise, Vertex AI chooses input baselines for the Model.

You can optionally add fields to configure the SmoothGrad approximation of gradients to the ExplanationParameters.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload

Request JSON body:

{
  "model": {
    "displayName": "MODEL_NAME",
    "containerSpec": {
      "imageUri": "IMAGE_URI"
    },
  "artifactUri": "PATH_TO_MODEL_ARTIFACT_DIRECTORY",
  "explanationSpec": {
    "parameters": {
      "xraiAttribution": {
        "stepCount": STEP_COUNT
      }
    },
    "metadata": {
       "inputs": {
         "FEATURE_NAME": {
           "inputTensorName": "INPUT_TENSOR_NAME",
           "modality": "image",
           "visualization": VISUALIZATION_SETTINGS
         }
       },
       "outputs": {
         "OUTPUT_NAME": {
           "outputTensorName": "OUTPUT_TENSOR_NAME"
         }
       }
    }
  }
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method POST `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -InFile request.json `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload" | Select-Object -Expand Content

scikit-learn and XGBoost pre-built containers

If your Model accepts tabular data as input and serves predictions using a pre-built scikit-learn or XGBoost container for prediction, then you can configure it to use the Sampled Shapley attribution method for explanations.

The following examples assume that your model accepts a single 2-D matrix as input; this is often the case for scikit-learn and XGBoost models. When you request explanations, Vertex Explainable AI provides attributions for each element of the input matrix.

Depending on which tool you want to use to create or import the Model, select one of the following tabs:

Console

Follow the guide to importing a model using the Cloud Console. When you get to the Explainability step, do the following:

1. For your feature attribution method, select Sampled Shapely (for tabular models).

2. Set the path count to the number of feature permutations to use for the Sampled Shapley attribution method. This must be an integer in the range [1, 50].

   A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

3. Configure each input feature in your model:

   1. Fill in your input feature name.

   2. Optionally, you can add one or more input baselines. Otherwise, Explainable AI chooses input baselines for your model.

4. Set the Output name of your feature.

5. Click the Import button when you have finished configuring the explainability settings.

gcloud

1. Write the following ExplanationMetadata to a JSON file in your local environment. The filename does not matter, but for this example call the file explanation-metadata.json:

   explanation-metadata.json

   {
     "inputs": {
       "FEATURE_NAME": {
       }
     },
     "outputs": {
       "OUTPUT_NAME": {
       }
     }
   }
   

   Replace the following:

   • FEATURE_NAME: Any memorable name for your input feature.
   • OUTPUT_NAME: Any memorable name for the output of your model.

   If you specify input baselines, make sure they match your model's input, usually a list of 2-D matrices. Otherwise, the default value for the input baseline is a 0-value 2-D matrix of the input shape.

2. Run the following command to create a Model resource that supports Vertex Explainable AI. The flags most pertinent to Vertex Explainable AI are highlighted.

   gcloud ai models upload \
     --region=LOCATION \
     --display-name=MODEL_NAME \
     --container-image-uri=IMAGE_URI \
     --artifact-uri=PATH_TO_MODEL_ARTIFACT_DIRECTORY \
     --explanation-method=sampled-shapley \
     --explanation-path-count=PATH_COUNT \
     --explanation-metadata-file=explanation-metadata.json
   

   Replace the following:

   • IMAGE_URI: The URI of a pre-built container for serving predictions.

   • PATH_COUNT: The number of feature permutations to use for the Sampled Shapley attribution method. Must be an integer in the range [1, 50].

     A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

   To learn about appropriate values for the other placeholders, read Importing models.

REST & CMD LINE

Before using any of the request data, make the following replacements:

• IMAGE_URI: The URI of a pre-built container for serving predictions.

• PATH_COUNT: The number of feature permutations to use for the Sampled Shapley attribution method. Must be an integer in the range [1, 50].

  A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

• FEATURE_NAME: Any memorable name for your input feature.
• OUTPUT_NAME: Any memorable name for the output of your model.

To learn about appropriate values for the other placeholders, read Importing models.

If you specify input baselines, make sure they match your model's input, usually a list of 2-D matrices. Otherwise, the default value for the input baseline is a 0-value 2-D matrix of the input shape.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload

Request JSON body:

{
  "model": {
    "displayName": "MODEL_NAME",
    "containerSpec": {
      "imageUri": "IMAGE_URI"
    },
  "artifactUri": "PATH_TO_MODEL_ARTIFACT_DIRECTORY",
  "explanationSpec": {
    "parameters": {
      "sampledShapleyAttribution": {
        "pathCount": PATH_COUNT
      }
    },
    "metadata": {
       "inputs": {
         "FEATURE_NAME": {
         }
       },
       "outputs": {
         "OUTPUT_NAME": {
         }
       }
    }
  }
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method POST `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -InFile request.json `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload" | Select-Object -Expand Content

Custom container

If your Model accepts tabular data as input and serves predictions using a custom container, then you can configure it to use the Sampled Shapley attribution method for explanations.

Note: The following instructions do not work if you are using any of Vertex AI's pre-built containers for serving predictions. You must use a custom container image that is not managed by Vertex AI and that meets the custom container requirements.

Determining feature and output names

In the following steps, you must provide Vertex AI with the names of the features that your Model expects as input. You must also specify the key used for outputs in the Model's predictions.

Determining feature names

If your Model expects each input instance to have certain top-level keys, then those keys are your feature names.

For example, consider a Model that expects each input instance to have the following format:

{
  "length": <value>,
  "width": <value>
}

In this case, the feature names are length and width. Even if the values of these fields contain nested lists or objects, length and width are the only keys you need for the following steps. When you request explanations, Vertex Explainable AI provides attributions for each nested element of your features.

If your Model expects unkeyed input, then Vertex Explainable AI considers the Model to have a single feature. You can use any memorable string for the feature name.

For example, consider a Model that expects each input instance to have the following format:

[
  <value>,
  <value>
]

In this case, provide Vertex Explainable AI with a single feature name of your choosing, like dimensions.

Note: The following examples assume that your model has a single input feature. If your model has multiple features, as described in this section, then add corresponding entries to the inputs field.

Determining the output name

If your Model returns each online prediction instance with keyed output, then that key is your output name.

For example, consider a Model that returns each prediction in the following format:

{
  "scores": <value>
}

In this case, the output name is scores. If the value of the scores field is an array, then when you get explanations, Vertex Explainable AI returns feature attributions for the element with the highest value in each prediction. To configure Vertex Explainable AI to provide feature attributions for additional elements of the output field, you can specify the topK or outputIndices fields of ExplanationParameters. However, the examples in this document do not demonstrate these options.

If your Model returns unkeyed predictions, then you can use any memorable string for the output name. For example, this applies if your Model returns an array or a scalar for each prediction.

Creating the Model

Depending on which tool you want to use to create or import the Model, select one of the following tabs:

Console

Follow the guide to importing a model using the Cloud Console. When you get to the Explainability step, do the following:

1. For your feature attribution method, select Sampled Shapely (for tabular models).

2. Set the path count to the number of feature permutations to use for the Sampled Shapley attribution method. This must be an integer in the range [1, 50].

   A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

3. Configure each input feature in your model:

   1. Fill in your input feature name.

   2. Optionally, you can add one or more input baselines. Otherwise, Explainable AI chooses input baselines for your model.

4. Set the Output name of your feature.

5. Click the Import button when you have finished configuring the explainability settings.

gcloud

1. Write the following ExplanationMetadata to a JSON file in your local environment. The filename does not matter, but for this example call the file explanation-metadata.json:

   explanation-metadata.json

   {
     "inputs": {
       "FEATURE_NAME": {}
     },
     "outputs": {
       "OUTPUT_NAME": {}
     }
   }
   

   Replace the following:

   • FEATURE_NAME: The name of the feature, as described in the "Determining feature names" section of this document.
   • OUTPUT_NAME: The name of the output, as described in the "Determining the output name" section of this document.

   You can optionally add input baselines to the ExplanationMetadata. Otherwise, Vertex AI chooses input baselines for the Model.

   If you specify input baselines, make sure they match your model's input, usually a list of 2-D matrices. Otherwise, the default value for the input baseline is a 0-value 2-D matrix of the input shape.

2. Run the following command to create a Model resource that supports Vertex Explainable AI. The flags most pertinent to Vertex Explainable AI are highlighted.

   gcloud ai models upload \
     --region=LOCATION \
     --display-name=MODEL_NAME \
     --container-image-uri=IMAGE_URI \
     --artifact-uri=PATH_TO_MODEL_ARTIFACT_DIRECTORY \
     --explanation-method=sampled-shapley \
     --explanation-path-count=PATH_COUNT \
     --explanation-metadata-file=explanation-metadata.json
   

   Replace the following:

   • PATH_COUNT: The number of feature permutations to use for the Sampled Shapley attribution method. Must be an integer in the range [1, 50].

     A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

   To learn about appropriate values for the other placeholders, read Importing models.

REST & CMD LINE

Before using any of the request data, make the following replacements:

• PATH_COUNT: The number of feature permutations to use for the Sampled Shapley attribution method. Must be an integer in the range [1, 50].

  A higher value might reduce approximation error but is more computationally intensive. If you don't know what value to use, try 25.

• FEATURE_NAME: The name of the feature, as described in the "Determining feature names" section of this document.
• OUTPUT_NAME: The name of the output, as described in the "Determining the output name" section of this document.

To learn about appropriate values for the other placeholders, read Importing models.

You can optionally add input baselines to the ExplanationMetadata. Otherwise, Vertex AI chooses input baselines for the Model.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload

Request JSON body:

{
  "model": {
    "displayName": "MODEL_NAME",
    "containerSpec": {
      "imageUri": "IMAGE_URI"
    },
  "artifactUri": "PATH_TO_MODEL_ARTIFACT_DIRECTORY",
  "explanationSpec": {
    "parameters": {
      "sampledShapleyAttribution": {
        "pathCount": PATH_COUNT
      }
    },
    "metadata": {
       "inputs": {
         "FEATURE_NAME": {}
       },
       "outputs": {
         "OUTPUT_NAME": {}
       }
    }
  }
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method POST `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -InFile request.json `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/models:upload" | Select-Object -Expand Content

What's next

• Deploy your Model to an Endpoint and get explanations.