2025-06-12 14:00:26 +09:00
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from datahub.metadata.urns import MlModelGroupUrn, MlModelUrn
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from datahub.sdk import DataHubClient
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from datahub.sdk.mlmodel import MLModel
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2023-05-17 10:21:39 +09:00
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2025-06-12 14:00:26 +09:00
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client = DataHubClient.from_env()
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2023-05-17 10:21:39 +09:00
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2025-06-12 14:00:26 +09:00
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mlmodel = MLModel(
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id="my-recommendations-model",
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name="My Recommendations Model",
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description="A model for recommending products to users",
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platform="mlflow",
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model_group=MlModelGroupUrn(platform="mlflow", name="my-recommendations-model"),
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hyper_params={
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"learning_rate": "0.01",
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"num_epochs": "100",
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"batch_size": "32",
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},
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)
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2023-05-17 10:21:39 +09:00
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2025-06-12 14:00:26 +09:00
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client.entities.upsert(mlmodel)
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2023-05-17 10:21:39 +09:00
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2025-06-12 14:00:26 +09:00
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mlmodel = client.entities.get(
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MlModelUrn(platform="mlflow", name="my-recommendations-model")
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)
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print("Model Name: ", mlmodel.name)
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print("Model Description: ", mlmodel.description)
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print("Model Group: ", mlmodel.model_group)
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print("Model Hyper Parameters: ", mlmodel.hyper_params)
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