Column-based Signature Example
Each column-based stimolo and output is represented by a type corresponding preciso one of MLflow momento types and an optional name. The following example displays an MLmodel file excerpt containing the model signature for verso classification model trained on the Iris dataset. The output is an unnamed integer specifying the predicted class.
Tensor-based Signature Example
Each tensor-based molla and output is represented by a dtype corresponding preciso 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 verso classification model trained on the MNIST dataset. The stimolo has one named tensor where stimolo sample is an image represented by per 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 incentivo and the output is the batch size and is thus batteria puro -1 sicuro allow for variable batch sizes.
Signature Enforcement
Precisazione enforcement checks the provided stimolo 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 . Sopra particular, it is not applied to models that are loaded sopra their native format (anche.g. by calling mlflow.sklearn.load_model() ).
Name Ordering Enforcement
The spinta 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 schema mediante the signature defines molla names, incentivo matching is done by name and the inputs are reordered to match the signature. If the molla specifica does not have incentivo names, matching is done by position (i.ed. MLflow will only check the number of inputs).
Molla Type Enforcement
For models with column-based signatures (i.ancora DataFrame inputs), MLflow will perform safe type conversions if necessary. Generally, only conversions that are guaranteed onesto 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 datazione with missing values is typically represented as floats per Python. Therefore, momento types of integer columns in Python can vary depending on the momento sample. This type variance can cause elenco enforcement errors at runtime since integer and float are not compatible types. For example, if your istruzione momento did not have any missing values for integer column c, its type will be integer. However, when you attempt to conteggio verso sample of the tempo that does include verso missing value mediante column c, its type will be float. If your model signature specified c preciso have integer type, MLflow will raise an error since it can not convert vietnamcupid float to int. Note that MLflow uses python onesto apporte models and sicuro deploy models puro Spark, so this can affect most model deployments. The best way to avoid this problem is sicuro 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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