snowflake.ml.model¶

Core Classes


`Model`()	Model Object containing multiple versions.
`ModelVersion`()	Model Version Object representing a specific version of the model that could be run.
`HuggingFacePipelineModel`([task, model, ...])	Utility factory method to build a wrapper over transformers [Pipeline].
`TransformersPipeline`(task, model, *[, ...])	Utility factory method to build a wrapper over transformers [Pipeline].

Model Logging Options


`CodePath`(root[, filter])	Specifies a code path with optional filtering for selective inclusion.
`ExportMode`(value)	An enumeration.
`Volatility`(value)	Volatility levels for model functions.

snowflake.ml.model.custom_model¶

Classes


`MethodRef`(model_ref, method_name)	Represents a method invocation of an instance of ModelRef.
`ModelRef`(name, model)	Represents a model in the inference graph.
`ModelContext`(*[, artifacts, models])	Context for a custom model storing paths to file artifacts and model object references.
`CustomModel`([context])	Abstract class for user defined custom model.

Decorators


`inference_api`(func)	Decorator to mark a method as an inference API in a CustomModel.
`partitioned_api`(func)	Decorator to mark a method as a partitioned inference API in a CustomModel.

snowflake.ml.model.model_signature¶

Classes


`DataType`(value)	An enumeration.
`BaseFeatureSpec`(name, shape)	Abstract Class for specification of a feature.
`FeatureSpec`(name, dtype[, shape, nullable])	Specification of a feature in Snowflake native model packaging.
`FeatureGroupSpec`(name, specs[, shape])	Specification of a group of features in Snowflake native model packaging.
`ParamSpec`(name, dtype, default_value[, shape])	Specification of a parameter in Snowflake native model packaging.
`ParamGroupSpec`(name, specs[, shape])	Specification of a group of parameters in Snowflake native model packaging.
`ModelSignature`(inputs, outputs[, params])	Signature of a model that specifies the input and output of a model.

Methods


`infer_signature`(input_data, output_data[, ...])	Infer model signature from given input and output sample data.

snowflake.ml.model.openai_signatures¶

Attributes


`OPENAI_CHAT_SIGNATURE`	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).
`OPENAI_CHAT_SIGNATURE_WITH_CONTENT_FORMAT_STRING`	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).
`OPENAI_CHAT_WITH_PARAMS_SIGNATURE`	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).
`OPENAI_CHAT_WITH_PARAMS_SIGNATURE_WITH_CONTENT_FORMAT_STRING`	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).