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Zaktualizuj 'ml_pytorch_mlflow.py'

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Sebastian Wałęsa 2022-05-15 14:19:02 +02:00
parent f4c044baa1
commit a4fb1a898a
1 changed files with 15 additions and 4 deletions
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ml_pytorch_mlflow.py
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@ -107,7 +107,7 @@ def predict_single(input, target, model):
return "Target: "+str(target)+" Predicted: "+str(prediction)+"\n" return "Target: "+str(target)+" Predicted: "+str(prediction)+"\n"
def prediction(input, target, model): def prediction(input, model):
inputs = input.unsqueeze(0) inputs = input.unsqueeze(0)
predictions = model(inputs) predictions = model(inputs)
predicted = predictions[0].detach() predicted = predictions[0].detach()
@ -133,7 +133,7 @@ def my_main(epochs):
for i in range(0, len(val_ds), 1): for i in range(0, len(val_ds), 1):
input_, target = val_ds[i] input_, target = val_ds[i]
expected.append(float(target)) expected.append(float(target))
predicted.append(float(prediction(input_, target, model))) predicted.append(float(prediction(input_, model)))
MSE = mean_squared_error(expected, predicted) MSE = mean_squared_error(expected, predicted)
MAE = mean_absolute_error(expected, predicted) MAE = mean_absolute_error(expected, predicted)
@ -147,8 +147,19 @@ def my_main(epochs):
input_, target = val_ds[i] input_, target = val_ds[i]
file.write(str(predict_single(input_, target, model))) file.write(str(predict_single(input_, target, model)))
input_example = val_ds[0].unsqueeze(0)
signature = mlflow.models.signature.infer_signature(input_, prediction(input_, model))
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
if tracking_url_type_store != "file":
mlflow.pytorch.log_model(model, "model", registered_model_name="s444356", signature=siganture,
input_example=input_example)
else:
mlflow.pytorch.log_model(model, "model", signature=siganture, input_example=input_example)
mlflow.pytorch.save_model(model, "my_model", signature=siganture, input_example=input_example)
torch.save(model, "Model_xPosition.pkl") torch.save(model, "Model_xPosition.pkl")
# ex.add_artifact("Model_xPosition.pkl")
with mlflow.start_run() as run: with mlflow.start_run() as run:
my_main(epochs) my_main(epochs)