Create a data labeling job for images
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Creates a data labeling job for images using the create_data_labeling_job method.
Code sample
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[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],[],[],[],null,["# Create a data labeling job for images\n\nCreates a data labeling job for images using the create_data_labeling_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\n from google.protobuf import json_format\n from google.protobuf.struct_pb2 import Value\n\n\n def create_data_labeling_job_images_sample(\n project: str,\n display_name: str,\n dataset: str,\n instruction_uri: str,\n annotation_spec: 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.gapic.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.job_service.JobServiceClient.html(client_options=client_options)\n inputs_dict = {\"annotation_specs\": [annotation_spec]}\n inputs = json_format.ParseDict(inputs_dict, Value())\n\n data_labeling_job = {\n \"display_name\": display_name,\n # Full resource name: projects/{project_id}/locations/{location}/datasets/{dataset_id}\n \"datasets\": [dataset],\n # labeler_count must be 1, 3, or 5\n \"labeler_count\": 1,\n \"instruction_uri\": instruction_uri,\n \"inputs_schema_uri\": \"gs://google-cloud-aiplatform/schema/datalabelingjob/inputs/image_classification_1.0.0.yaml\",\n \"inputs\": inputs,\n \"annotation_labels\": {\n \"aiplatform.googleapis.com/annotation_set_name\": \"my_test_saved_query\"\n },\n }\n parent = f\"projects/{project}/locations/{location}\"\n response = client.https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.job_service.JobServiceClient.html#google_cloud_aiplatform_v1_services_job_service_JobServiceClient_create_data_labeling_job(\n parent=parent, data_labeling_job=data_labeling_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)."]]
