REST

To create a model evaluation job, send aPOST request by using thepipelineJobs 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, onlyus-central1 is supported.
• DATASET_URI: The Cloud Storage URI of your reference dataset. You can specify one or multiple URIs. This parameter supportswildcards. To learn more about this parameter, seeInputConfig.
• 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, onlyjsonl is supported. To learn more about this parameter, seeInputConfig.
• PREDICTIONS_FORMAT: The format of the evaluation output. Currently, onlyjsonl is supported. To learn more about this parameter, seeInputConfig.
• MACHINE_TYPE: (Optional) The machine type for running the evaluation job. The default value ise2-highmem-16. For a list of supported machine types, seeMachine types.
• SERVICE_ACCOUNT: (Optional) The service account to use for running the evaluation job. To learn how to create a custom service account, seeConfigure a service account with granular permissions. If unspecified, theVertex 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 isprojects/PROJECT_NUMBER/global/networks/NETWORK_NAME. If you specify this field, you need to have aVPC 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 isprojects/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 thatpipelineSpec 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": "123456789012-compute@developer.gserviceaccount.com",  "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=myprojectREGION=us-central1MODEL_NAME=publishers/google/models/text-bison@002TEST_DATASET_URI=gs://my-gcs-bucket-uri/dataset.jsonlOUTPUT_DIR=gs://my-gcs-bucket-uri/outputcurl\-XPOST\-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, seeInstall the Vertex AI SDK for Python. For more information, see thePython API reference documentation.

importosfromgoogle.authimportdefaultimportvertexaifromvertexai.preview.language_modelsimport(EvaluationTextClassificationSpec,TextGenerationModel,)PROJECT_ID=os.getenv("GOOGLE_CLOUD_PROJECT")defevaluate_model()->object:"""Evaluate the performance of a generative AI model."""# Set credentials for the pipeline components used in the evaluation taskcredentials,_=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 modelmodel=TextGenerationModel.from_pretrained("text-bison@002")# Define the evaluation specification for a text classification tasktask_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 modeleval_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...returneval_metrics

Console

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

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

   Go to Vertex AI Model Registry

2. Click the name of the model that you want to evaluate.
3. In theEvaluate tab, clickCreate 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, onlyjsonl 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.
4. ClickStart evaluation