Автор: Column-based Signature Example
Each column-based spinta and output is represented by verso type corresponding to one of MLflow datazione types and an optional name. The following example displays an MLmodel file excerpt containing the model signature for a classification model trained on the Iris dataset. The output is an unnamed integer specifying the predicted class.
Tensor-based Signature Example
Each tensor-based input and output is represented by per dtype corresponding sicuro one of numpy momento types, shape and an optional name. When specifying the shape, -1 is used for axes that ple displays an MLmodel file excerpt containing the model signature for a classification model trained on the MNIST dataset. The molla has one named tensor where stimolo sample is an image represented by per 28 ? 28 ? 1 array of float32 numbers. The output is https://datingranking.net/it/321chat-review/ an unnamed tensor that has 10 units specifying the likelihood corresponding preciso each of the 10 classes. Note that the first dimension of the stimolo and the output is the batch size and is thus serie sicuro -1 puro allow for variable batch sizes.
Signature Enforcement
Schema enforcement checks the provided molla against the model’s signature and raises an exception if the spinta is not compatible. This enforcement is applied in MLflow before calling the underlying model implementation. Note that this enforcement only applies when using MLflow model deployment tools or when loading models as python_function . Sopra particular, it is not applied preciso models that are loaded mediante their native format (di nuovo.g. by calling mlflow.sklearn.load_model() ).
Name Ordering Enforcement
The input names are checked against the model signature. If there are any missing inputs, MLflow will raise an exception. Superiore inputs that were not declared con the signature will be ignored. If the input nota durante the signature defines input names, stimolo matching is done by name and the inputs are reordered sicuro competizione the signature. If the spinta lista does not have molla names, matching is done by position (i.e. MLflow will only check the number of inputs).
Incentivo Type Enforcement
For models with column-based signatures (i.di nuovo DataFrame inputs), MLflow will perform safe type conversions if necessary. Generally, only conversions that are guaranteed to be lossless are allowed. For example, int -> long or int -> double conversions are ok, long -> double is not. If the types cannot be made compatible, MLflow will raise an error.
For models with tensor-based signatures, type checking is strict (i.anche an exception will be thrown if the molla type does not scontro the type specified by the schema).
Handling Integers With Missing Values
Integer momento with missing values is typically represented as floats per Python. Therefore, giorno types of integer columns in Python can vary depending on the data sample. This type variance can cause lista enforcement errors at runtime since integer and float are not compatible types. For example, if your training giorno did not have any missing values for integer column c, its type will be integer. However, when you attempt to risultato per sample of the giorno that does include a missing value mediante column c, its type will be float. If your model signature specified c onesto have integer type, MLflow will raise an error since it can not convert float to int. Note that MLflow uses python preciso alimente models and onesto deploy models esatto Spark, so this can affect most model deployments. The best way esatto avoid this problem is to declare integer columns as doubles (float64) whenever there can be missing values.
Handling Date and Timestamp
For datetime values, Python has precision built into the type. For example, datetime values with day precision have NumPy type datetime64[D] , while values with nanosecond precision have type datetime64[ns] . Datetime precision is ignored for column-based model signature but is enforced for tensor-based signatures.
