Menambahkan logika pemrosesan menggunakan fungsi Cloud Run

Dengan fungsi Cloud Run, Anda dapat memproses lebih lanjut data output dari model yang dilatih khusus Vertex AI dan node aplikasi BigQuery. Anda dapat menggunakan integrasi ini dengan node aplikasi dengan cara berikut:

• Node model kustom Vertex AI: Gunakan fungsi Cloud Run untuk memproses hasil prediksi dari model kustom Vertex AI asli.
• Node BigQuery: Gunakan fungsi Cloud Run untuk membuat baris BigQuery yang disesuaikan dengan anotasi asli.

Semua fungsi Cloud Run yang Anda gunakan dengan App Platform harus memenuhi persyaratan berikut:

• Fungsi Cloud Run harus menyediakan pemicu Http.
• Fungsi Cloud Run harus menerima string JSON AppPlatformCloudFunctionRequest, dan harus menampilkan kembali string JSON AppPlatformCloudFunctionResponse.
• Skema payload anotasi yang disimpan dalam permintaan dan respons harus mengikuti spesifikasi model target.

Definisi API: AppPlatformMetadata, AppPlatformCloudFunctionRequest, AppPlatformCloudFunctionResponse

// Message of essential metadata of App Platform.
// This message is usually attached to a certain model output annotation for
// customer to identify the source of the data.
message AppPlatformMetadata {
  // The application resource name.
  string application = 1;
  // The instance resource id. Instance is the nested resource of application
  // under collection 'instances'.
  string instance_id = 2;
  // The node name of the application graph.
  string node = 3;
  // The referred model resource name of the application node.
  string processor = 4;
}

// For any Cloud Run function based customer processing logic, customer's cloud
// function is expected to receive AppPlatformCloudFunctionRequest as request
// and send back AppPlatformCloudFunctionResponse as response.
// Message of request from AppPlatform to Cloud Run functions.
message AppPlatformCloudFunctionRequest {
  // The metadata of the AppPlatform for customer to identify the source of the
  // payload.
  AppPlatformMetadata app_platform_metadata = 1;
  // A general annotation message that uses struct format to represent different
  // concrete annotation protobufs.
  message StructedInputAnnotation {
    // The ingestion time of the current annotation.
    int64 ingestion_time_micros = 1;
    // The struct format of the actual annotation.
    protobuf.Struct annotation = 2;
  }
  // The actual annotations to be processed by the customized Cloud Run function.
  repeated StructedInputAnnotation annotations = 2;
}

// Message of the response from customer's Cloud Run function to AppPlatform.
message AppPlatformCloudFunctionResponse {
  // A general annotation message that uses struct format to represent different
  // concrete annotation protobufs.
  message StructedOutputAnnotation {
    // The struct format of the actual annotation.
    protobuf.Struct annotation = 1;
  }

  // The modified annotations that is returned back to AppPlatform.
  // If the annotations fields are empty, then those annotations will be dropped
  // by AppPlatform.
  repeated StructedOutputAnnotation annotations = 2;
}

Contoh penggunaan

Gunakan kode berikut untuk memproses pasca-anotasi model Vertex AI yang dilatih secara khusus dan mengganti anotasi dengan pasangan nilai kunci konstan.

Python

import functions_framework
from flask import jsonify

@functions_framework.http
def hello_http(request):
   request_json = request.get_json(silent=True)
   request_args = request.args

   if request_json and 'annotations' in request_json:
       annotations = []
       for ele in request_json['annotations']:
           for k, v in ele.items():
               if k == "annotation":
                if "predictions" in v:
                    # Replace the annotation.
                    v["predictions"][0] = {"user": "googler"}
                    annotations.append({"annotation" : v})
   else:
       annotations = 'Failure'
   return jsonify(annotations=annotations)