Run a computation-based evaluation pipeline

REST

To create a model evaluation job, send a POST request by using the pipelineJobs method.

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

• PROJECT_ID: The Google Cloud project that runs the pipeline components.
• PIPELINEJOB_DISPLAYNAME: A display name for the pipelineJob.
• LOCATION: The region to run the pipeline components. Currently, only us-central1 is supported.
• DATASET_URI: The Cloud Storage URI of your reference dataset. You can specify one or multiple URIs. This parameter supports wildcards. To learn more about this parameter, see InputConfig.
• OUTPUT_DIR: The Cloud Storage URI to store evaluation output.
• MODEL_NAME: Specify a publisher model or a tuned model resource as follows:
  • Publisher model: publishers/google/models/MODEL@MODEL_VERSION

    Example: publishers/google/models/text-bison@002

  • Tuned model: projects/PROJECT_NUMBER/locations/LOCATION/models/ENDPOINT_ID

    Example: projects/123456789012/locations/us-central1/models/1234567890123456789

  The evaluation job doesn't impact any existing deployments of the model or their resources.

• EVALUATION_TASK: The task that you want to evaluate the model on. The evaluation job computes a set of metrics relevant to that specific task. Acceptable values include the following:
  • summarization
  • question-answering
  • text-generation
  • classification
• INSTANCES_FORMAT: The format of your dataset. Currently, only jsonl is supported. To learn more about this parameter, see InputConfig.
• PREDICTIONS_FORMAT: The format of the evaluation output. Currently, only jsonl is supported. To learn more about this parameter, see InputConfig.
• MACHINE_TYPE: (Optional) The machine type for running the evaluation job. The default value is e2-highmem-16. For a list of supported machine types, see Machine types.
• SERVICE_ACCOUNT: (Optional) The service account to use for running the evaluation job. To learn how to create a custom service account, see Configure a service account with granular permissions. If unspecified, the Vertex AI Custom Code Service Agent is used.
• NETWORK: (Optional) The fully qualified name of the Compute Engine network to peer the evaluatiuon job to. The format of the network name is projects/PROJECT_NUMBER/global/networks/NETWORK_NAME. If you specify this field, you need to have a VPC Network Peering for Vertex AI. If left unspecified, the evaluation job is not peered with any network.
• KEY_NAME: (Optional) The name of the customer-managed encryption key (CMEK). If configured, resources created by the evaluation job is encrypted using the provided encryption key. The format of the key name is projects/PROJECT_ID/locations/REGION/keyRings/KEY_RING/cryptoKeys/KEY. The key needs to be in the same region as the evaluation job.

HTTP method and URL:

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

Request JSON body:

{
  "displayName": "PIPELINEJOB_DISPLAYNAME",
  "runtimeConfig": {
    "gcsOutputDirectory": "gs://OUTPUT_DIR",
    "parameterValues": {
      "project": "PROJECT_ID",
      "location": "LOCATION",
      "batch_predict_gcs_source_uris": ["gs://DATASET_URI"],
      "batch_predict_gcs_destination_output_uri": "gs://OUTPUT_DIR",
      "model_name": "MODEL_NAME",
      "evaluation_task": "EVALUATION_TASK",
      "batch_predict_instances_format": "INSTANCES_FORMAT",
      "batch_predict_predictions_format: "PREDICTIONS_FORMAT",
      "machine_type": "MACHINE_TYPE",
      "service_account": "SERVICE_ACCOUNT",
      "network": "NETWORK",
      "encryption_spec_key_name": "KEY_NAME"
    }
  },
  "templateUri": "https://us-kfp.pkg.dev/vertex-evaluation/pipeline-templates/evaluation-llm-text-generation-pipeline/1.0.1"
}

To send your request, choose one of these options:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth 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/pipelineJobs"

$cred = gcloud auth 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/pipelineJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following. Note that pipelineSpec has been truncated to save space.

......
.....
 "state": "PIPELINE_STATE_PENDING",
  "labels": {
    "vertex-ai-pipelines-run-billing-id": "1234567890123456789"
  },
  "runtimeConfig": {
    "gcsOutputDirectory": "gs://my-evaluation-bucket/output",
    "parameterValues": {
      "project": "my-project",
      "location": "us-central1",
      "batch_predict_gcs_source_uris": [
        "gs://my-evaluation-bucket/reference-datasets/eval_data.jsonl"
      ],
      "batch_predict_gcs_destination_output_uri": "gs://my-evaluation-bucket/output",
      "model_name": "publishers/google/models/text-bison@002"
    }
  },
  "serviceAccount": "[email protected]",
  "templateUri": "https://us-kfp.pkg.dev/vertex-evaluation/pipeline-templates/evaluation-llm-text-generation-pipeline/1.0.1",
  "templateMetadata": {
    "version": "sha256:d4c0d665533f6b360eb474111aa5e00f000fb8eac298d367e831f3520b21cb1a"
  }
}

Example curl command

PROJECT_ID=myproject
REGION=us-central1
MODEL_NAME=publishers/google/models/text-bison@002
TEST_DATASET_URI=gs://my-gcs-bucket-uri/dataset.jsonl
OUTPUT_DIR=gs://my-gcs-bucket-uri/output

curl \
-X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
"https://${REGION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${REGION}/pipelineJobs" -d \
$'{
  "displayName": "evaluation-llm-text-generation-pipeline",
  "runtimeConfig": {
    "gcsOutputDirectory": "'${OUTPUT_DIR}'",
    "parameterValues": {
      "project": "'${PROJECT_ID}'",
      "location": "'${REGION}'",
      "batch_predict_gcs_source_uris": ["'${TEST_DATASET_URI}'"],
      "batch_predict_gcs_destination_output_uri": "'${OUTPUT_DIR}'",
      "model_name": "'${MODEL_NAME}'",
    }
  },
  "templateUri": "https://us-kfp.pkg.dev/vertex-evaluation/pipeline-templates/evaluation-llm-text-generation-pipeline/1.0.1"
}'

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

import os

from google.auth import default

import vertexai
from vertexai.preview.language_models import (
    EvaluationTextClassificationSpec,
    TextGenerationModel,
)

PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")


def evaluate_model() -> object:
    """Evaluate the performance of a generative AI model."""

    # Set credentials for the pipeline components used in the evaluation task
    credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])

    vertexai.init(project=PROJECT_ID, location="us-central1", credentials=credentials)

    # Create a reference to a generative AI model
    model = TextGenerationModel.from_pretrained("text-bison@002")

    # Define the evaluation specification for a text classification task
    task_spec = EvaluationTextClassificationSpec(
        ground_truth_data=[
            "gs://cloud-samples-data/ai-platform/generative_ai/llm_classification_bp_input_prompts_with_ground_truth.jsonl"
        ],
        class_names=["nature", "news", "sports", "health", "startups"],
        target_column_name="ground_truth",
    )

    # Evaluate the model
    eval_metrics = model.evaluate(task_spec=task_spec)
    print(eval_metrics)
    # Example response:
    # ...
    # PipelineJob run completed.
    # Resource name: projects/123456789/locations/us-central1/pipelineJobs/evaluation-llm-classification-...
    # EvaluationClassificationMetric(label_name=None, auPrc=0.53833705, auRoc=0.8...

    return eval_metrics

Console

To create a model evaluation job by using the Google Cloud console, perform the following steps:

1. In the Google Cloud console, go to the Vertex AI Model Registry page.

   Go to Vertex AI Model Registry

2. Click the name of the model that you want to evaluate.
3. In the Evaluate tab, click Create evaluation and configure as follows:

• Objective: Select the task that you want to evaluate.
• Target column or field: (Classification only) Enter the target column for prediction. Example: ground_truth.
• Source path: Enter or select the URI of your evaluation dataset.
• Output format: Enter the format of the evaluation output. Currently, only jsonl is supported.
• Cloud Storage path: Enter or select the URI to store evaluation output.
• Class names: (Classification only) Enter the list of possible class names.
• Number of compute nodes: Enter the number of compute nodes to run the evaluation job.
• Machine type: Select a machine type to use for running the evaluation job.