Creazione di un job di previsione batch per la previsione tabulare
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Crea un job di previsione batch per la previsione tabulare utilizzando il metodo create_batch_prediction_job.
Esempio di codice
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[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],[],[],[],null,["# Create a batch prediction job for tabular forecasting\n\nCreates a batch prediction job for tabular forecasting using the create_batch_prediction_job method.\n\nCode sample\n-----------\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n from google.cloud import aiplatform_v1beta1\n\n\n def create_batch_prediction_job_tabular_forecasting_sample(\n project: str,\n display_name: str,\n model_name: str,\n gcs_source_uri: str,\n gcs_destination_output_uri_prefix: str,\n predictions_format: str,\n location: str = \"us-central1\",\n api_endpoint: str = \"us-central1-aiplatform.googleapis.com\",\n ):\n # The AI Platform services require regional API endpoints.\n client_options = {\"api_endpoint\": api_endpoint}\n # Initialize client that will be used to create and send requests.\n # This client only needs to be created once, and can be reused for multiple requests.\n client = aiplatform_v1beta1.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.job_service.JobServiceClient.html(client_options=client_options)\n batch_prediction_job = {\n \"display_name\": display_name,\n # Format: 'projects/{project}/locations/{location}/models/{model_id}'\n \"model\": model_name,\n \"input_config\": {\n \"instances_format\": predictions_format,\n \"gcs_source\": {\"uris\": [gcs_source_uri]},\n },\n \"output_config\": {\n \"predictions_format\": predictions_format,\n \"gcs_destination\": {\"output_uri_prefix\": gcs_destination_output_uri_prefix},\n },\n }\n parent = f\"projects/{project}/locations/{location}\"\n response = client.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.job_service.JobServiceClient.html#google_cloud_aiplatform_v1beta1_services_job_service_JobServiceClient_create_batch_prediction_job(\n parent=parent, batch_prediction_job=batch_prediction_job\n )\n print(\"response:\", response)\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=aiplatform)."]]