將資料匯出至外部ML環境

from aepp import flowservice
flow_conn = flowservice.FlowService()

training_dataset_id = <YOUR_TRAINING_DATASET_ID>

source_res = flow_conn.createSourceConnectionDataLake(
    name=f"[CMLE] Featurized Dataset source connection created by {username}",
    dataset_ids=[training_dataset_id],
    format="parquet"
)
source_connection_id = source_res["id"]

connection_spec_id = "10440537-2a7b-4583-ac39-ed38d4b848e8"
base_connection_res = flow_conn.createConnection(data={
    "name": "Base Connection to DLZ created by",
    "auth": None,
    "connectionSpec": {
        "id": connection_spec_id,
        "version": "1.0"
    }
})
base_connection_id = base_connection_res["id"]

target_res = flow_conn.createTargetConnection(
    data={
        "name": "Data Landing Zone target connection",
        "baseConnectionId": base_connection_id,
        "params": {
            "mode": "Server-to-server",
            "compression": config.get("Cloud", "compression_type"),
            "datasetFileType": config.get("Cloud", "data_format"),
            "path": config.get("Cloud", "export_path")
        },
        "connectionSpec": {
            "id": connection_spec_id,
            "version": "1.0"
        }
    }
)
target_connection_id = target_res["id"]

import time

on_schedule = False
if on_schedule:
    schedule_params = {
        "interval": 3,
        "timeUnit": "hour",
        "startTime": int(time.time())
    }
else:
    schedule_params = {
        "interval": 1,
        "timeUnit": "day",
        "startTime": int(time.time() + 60*60*24*365) # Start the schedule far in the future
    }

flow_spec_id = "cd2fc47e-e838-4f38-a581-8fff2f99b63a"
flow_obj = {
    "name": "Flow for Feature Dataset to DLZ",
    "flowSpec": {
        "id": flow_spec_id,
        "version": "1.0"
    },
    "sourceConnectionIds": [
        source_connection_id
    ],
    "targetConnectionIds": [
        target_connection_id
    ],
    "transformations": [],
    "scheduleParams": schedule_params
}
flow_res = flow_conn.createFlow(
    obj = flow_obj,
    flow_spec_id = flow_spec_id
)
dataflow_id = flow_res["id"]

from aepp import connector

connector = connector.AdobeRequest(
  config_object=aepp.config.config_object,
  header=aepp.config.header,
  loggingEnabled=False,
  logger=None,
)

endpoint = aepp.config.endpoints["global"] + "/data/core/activation/disflowprovider/adhocrun"

payload = {
    "activationInfo": {
        "destinations": [
            {
                "flowId": dataflow_id,
                "datasets": [
                    {"id": created_dataset_id}
                ]
            }
        ]
    }
}

connector.header.update({"Accept":"application/vnd.adobe.adhoc.dataset.activation+json; version=1"})
activation_res = connector.postData(endpoint=endpoint, data=payload)
activation_res

from aepp import exportDatasetToDataLandingZone

export = exportDatasetToDataLandingZone.ExportDatasetToDataLandingZone()

dataflow_id = export.createDataFlowRunIfNotExists(
    dataset_id = created_dataset_id,
    data_format = data_format,
    export_path= export_path,
    compression_type = compression_type,
    on_schedule = False,
    config_path = config_path,
    entity_name = "Flow for Featurized Dataset to DLZ"
)