Zaktualizuj 'ml_pytorch_mlflow.py'
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@ -147,19 +147,19 @@ def my_main(epochs):
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input_, target = val_ds[i]
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input_, target = val_ds[i]
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file.write(str(predict_single(input_, target, model)))
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file.write(str(predict_single(input_, target, model)))
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print(input_)
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print(val_ds)
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# input_example = val_ds[0]
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# input_example = val_ds[0]
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# # input_example = input_example.unsqueeze(0)
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# # input_example = input_example.unsqueeze(0)
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# signature = infer_signature(input_[0], prediction(input_[0], model))
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# signature = infer_signature(input_[0], prediction(input_[0], model))
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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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if tracking_url_type_store != "file":
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# if tracking_url_type_store != "file":
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mlflow.pytorch.log_model(model, "model", registered_model_name="s478839", signature=siganture,
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# mlflow.pytorch.log_model(model, "model", registered_model_name="s478839", signature=siganture,
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input_example=input_example)
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# input_example=input_example)
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else:
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# else:
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mlflow.pytorch.log_model(model, "model", signature=siganture, input_example=input_example)
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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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# mlflow.pytorch.save_model(model, "my_model", signature=siganture, input_example=input_example)
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with mlflow.start_run() as run:
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with mlflow.start_run() as run:
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