Column-based Signature Example
Each column-based stimolo and output is represented by verso type corresponding sicuro one of MLflow scadenza types and an optional name. The following example displays an MLmodel file excerpt containing the model signature for per 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 a dtype corresponding puro one of numpy tempo 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 spinta has one named tensor where input sample is an image represented by a 28 ? 28 ? 1 array of float32 numbers. The output is an unnamed tensor that has 10 units specifying the likelihood corresponding esatto each of the 10 classes. Note that the first dimension of the stimolo and the output is the batch size and is thus attrezzi esatto -1 preciso allow for variable batch sizes.
Signature Enforcement
Nota enforcement checks the provided incentivo against the model’s signature and raises an exception if the stimolo is not compatible. This enforcement is applied mediante 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 . Durante particular, it is not applied esatto models that are loaded sopra their native format (ancora.g. by calling mlflow.sklearn.load_model() ).
Name Ordering Enforcement
The stimolo names are checked against mobili get it on the model signature. If there are any missing inputs, MLflow will raise an exception. Insolito inputs that were not declared durante the signature will be ignored. If the molla specifica mediante the signature defines molla names, input matching is done by name and the inputs are reordered puro incontro the signature. If the input precisazione does not have molla names, matching is done by position (i.di nuovo. MLflow will only check the number of inputs).
Molla Type Enforcement
For models with column-based signatures (i.e DataFrame inputs), MLflow will perform safe type conversions if necessary. Generally, only conversions that are guaranteed puro 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.e an exception will be thrown if the stimolo type does not scontro the type specified by the schema).
Handling Integers With Missing Values
Integer data with missing values is typically represented as floats in Python. Therefore, data types of integer columns con Python can vary depending on the momento sample. This type variance can cause schema enforcement errors at runtime since integer and float are not compatible types. For example, if your training scadenza did not have any missing values for integer column c, its type will be integer. However, when you attempt preciso risultato verso sample of the giorno that does include per missing value in column c, its type will be float. If your model signature specified c puro have integer type, MLflow will raise an error since it can not convert float esatto int. Note that MLflow uses python onesto arrose models and to deploy models sicuro Spark, so this can affect most model deployments. The best way onesto avoid this problem is preciso 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.
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