signature
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@ -22,7 +22,8 @@ from sacred import Experiment
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from sacred.observers import FileStorageObserver
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from sacred.observers import FileStorageObserver
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from sacred.observers import MongoObserver
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from sacred.observers import MongoObserver
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import mlflow
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import mlflow
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import mlflow.keras
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import mlflow.pytorch
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from mlflow.models import infer_signature
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# In[2]:
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# In[2]:
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ex = Experiment(save_git_info=False)
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ex = Experiment(save_git_info=False)
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@ -185,16 +186,17 @@ def fit(epochs, lr, model, train_loader, val_loader, _log, _run, opt_func=torch.
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_run.info["epochs"] = epochs
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_run.info["epochs"] = epochs
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signature = mlflow.models.signature.infer_signature(house_price_features, linear_model.predict(house_price_features))
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signature = mlflow.models.signature.infer_signature(train_ds)
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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sampleInp = [0.1, 0.1, 546.0, 555.223, 1., 1., 33.16376, 84.12426]
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input_example = [0.1, 0.1, 546.0, 555.223, 1., 1., 33.16376, 84.12426]
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if tracking_url_type_store != "file":
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if tracking_url_type_store != "file":
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mlflow.keras.log_model(model, "model", registered_model_name="red-wine-quality", signature=signature)
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mlflow.pytorch.log_model(model, "model", registered_model_name="s444354", signature=siganture, input_example=input_example)
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else:
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else:
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mlflow.keras.log_model(model, "model", signature=signature, input_example=np.array(sampleInp))
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mlflow.pytorch.log_model(model, "model", signature=siganture, input_example=input_example)
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mlflow.pytorch.save_model(model, "my_model", signature=siganture, input_example=input_example)
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