1.16.15

flytekitplugins.onnxscikitlearn.schema

Directory

Classes

Class Description
ScikitLearn2ONNX
ScikitLearn2ONNXConfig ScikitLearn2ONNXConfig is the config used during the scikitlearn to ONNX conversion.
ScikitLearn2ONNXTransformer

Methods

Method Description
extract_config()
to_onnx()

Methods

extract_config() 

def extract_config(
    t: Type[ScikitLearn2ONNX],
) -> Tuple[Type[ScikitLearn2ONNX], ScikitLearn2ONNXConfig]
Parameter Type Description
t Type[ScikitLearn2ONNX]

to_onnx() 

def to_onnx(
    ctx,
    model,
    config,
)
Parameter Type Description
ctx
model
config

flytekitplugins.onnxscikitlearn.schema.ScikitLearn2ONNX 

class ScikitLearn2ONNX(
    model: sklearn.base.BaseEstimator,
)
Parameter Type Description
model sklearn.base.BaseEstimator

Methods

Method Description
from_dict()
from_json()
schema()
to_dict()
to_json()

from_dict() 

def from_dict(
    kvs: typing.Union[dict, list, str, int, float, bool, NoneType],
    infer_missing,
) -> ~A
Parameter Type Description
kvs typing.Union[dict, list, str, int, float, bool, NoneType]
infer_missing

from_json() 

def from_json(
    s: typing.Union[str, bytes, bytearray],
    parse_float,
    parse_int,
    parse_constant,
    infer_missing,
    kw,
) -> ~A
Parameter Type Description
s typing.Union[str, bytes, bytearray]
parse_float
parse_int
parse_constant
infer_missing
kw

schema() 

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
) -> SchemaType[A]
Parameter Type Description
infer_missing bool
only
exclude
many bool
context
load_only
dump_only
partial bool
unknown

to_dict() 

def to_dict(
    encode_json,
) -> typing.Dict[str, typing.Union[dict, list, str, int, float, bool, NoneType]]
Parameter Type Description
encode_json

to_json() 

def to_json(
    skipkeys: bool,
    ensure_ascii: bool,
    check_circular: bool,
    allow_nan: bool,
    indent: typing.Union[int, str, NoneType],
    separators: typing.Tuple[str, str],
    default: typing.Callable,
    sort_keys: bool,
    kw,
) -> str
Parameter Type Description
skipkeys bool
ensure_ascii bool
check_circular bool
allow_nan bool
indent typing.Union[int, str, NoneType]
separators typing.Tuple[str, str]
default typing.Callable
sort_keys bool
kw

flytekitplugins.onnxscikitlearn.schema.ScikitLearn2ONNXConfig

ScikitLearn2ONNXConfig is the config used during the scikitlearn to ONNX conversion.

class ScikitLearn2ONNXConfig(
    initial_types: List[Tuple[str, Type]],
    name: Optional[str],
    doc_string: str,
    target_opset: Optional[int],
    custom_conversion_functions: Dict[Callable[..., Any], Callable[..., None]],
    custom_shape_calculators: Dict[Callable[..., Any], Callable[..., None]],
    custom_parsers: Dict[Callable[..., Any], Callable[..., None]],
    options: Dict[Any, Any],
    intermediate: bool,
    naming: Optional[Union[str, Callable[..., Any]]],
    white_op: Optional[Set[str]],
    black_op: Optional[Set[str]],
    verbose: int,
    final_types: Optional[List[Tuple[str, Type]]],
)
Parameter Type Description
initial_types List[Tuple[str, Type]] The types of the inputs to the model.
name Optional[str] The name of the graph in the produced ONNX model.
doc_string str A string attached onto the produced ONNX model.
target_opset Optional[int] The ONNX opset number.
custom_conversion_functions Dict[Callable[..., Any], Callable[..., None]] A dictionary for specifying the user customized conversion function.
custom_shape_calculators Dict[Callable[..., Any], Callable[..., None]] A dictionary for specifying the user customized shape calculator.
custom_parsers Dict[Callable[..., Any], Callable[..., None]] Parsers determine which outputs are expected for which particular task.
options Dict[Any, Any] Specific options given to converters.
intermediate bool If True, the function returns the converted model and the instance of Topology used, else, it returns the converted model.
naming Optional[Union[str, Callable[..., Any]]] Change the way intermediates are named.
white_op Optional[Set[str]] White list of ONNX nodes allowed while converting a pipeline.
black_op Optional[Set[str]] Black list of ONNX nodes disallowed while converting a pipeline.
verbose int Display progress while converting a model.
final_types Optional[List[Tuple[str, Type]]] Used to overwrite the type (if type is not None) and the name of every output.

Methods

Method Description
from_dict()
from_json()
schema()
to_dict()
to_json()

from_dict() 

def from_dict(
    kvs: typing.Union[dict, list, str, int, float, bool, NoneType],
    infer_missing,
) -> ~A
Parameter Type Description
kvs typing.Union[dict, list, str, int, float, bool, NoneType]
infer_missing

from_json() 

def from_json(
    s: typing.Union[str, bytes, bytearray],
    parse_float,
    parse_int,
    parse_constant,
    infer_missing,
    kw,
) -> ~A
Parameter Type Description
s typing.Union[str, bytes, bytearray]
parse_float
parse_int
parse_constant
infer_missing
kw

schema() 

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
) -> SchemaType[A]
Parameter Type Description
infer_missing bool
only
exclude
many bool
context
load_only
dump_only
partial bool
unknown

to_dict() 

def to_dict(
    encode_json,
) -> typing.Dict[str, typing.Union[dict, list, str, int, float, bool, NoneType]]
Parameter Type Description
encode_json

to_json() 

def to_json(
    skipkeys: bool,
    ensure_ascii: bool,
    check_circular: bool,
    allow_nan: bool,
    indent: typing.Union[int, str, NoneType],
    separators: typing.Tuple[str, str],
    default: typing.Callable,
    sort_keys: bool,
    kw,
) -> str
Parameter Type Description
skipkeys bool
ensure_ascii bool
check_circular bool
allow_nan bool
indent typing.Union[int, str, NoneType]
separators typing.Tuple[str, str]
default typing.Callable
sort_keys bool
kw

flytekitplugins.onnxscikitlearn.schema.ScikitLearn2ONNXTransformer 

def ScikitLearn2ONNXTransformer()

Properties

Property Type Description
is_async None
name None
python_type None This returns the python type
type_assertions_enabled None Indicates if the transformer wants type assertions to be enabled at the core type engine layer

Methods

Method Description
assert_type()
from_binary_idl() This function primarily handles deserialization for untyped dicts, dataclasses, Pydantic BaseModels, and attribute access.
from_generic_idl() TODO: Support all Flyte Types.
get_literal_type() Converts the python type to a Flyte LiteralType.
guess_python_type() Converts the Flyte LiteralType to a python object type.
isinstance_generic()
schema_match() Check if a JSON schema fragment matches this transformer’s python_type.
to_html() Converts any python val (dataframe, int, float) to a html string, and it will be wrapped in the HTML div.
to_literal() Converts a given python_val to a Flyte Literal, assuming the given python_val matches the declared python_type.
to_python_value() Converts the given Literal to a Python Type.

assert_type() 

def assert_type(
    t: Type[T],
    v: T,
)
Parameter Type Description
t Type[T]
v T

from_binary_idl() 

def from_binary_idl(
    binary_idl_object: Binary,
    expected_python_type: Type[T],
) -> Optional[T]

This function primarily handles deserialization for untyped dicts, dataclasses, Pydantic BaseModels, and attribute access.｀

For untyped dict, dataclass, and pydantic basemodel: Life Cycle (Untyped Dict as example): python val -> msgpack bytes -> binary literal scalar -> msgpack bytes -> python val (to_literal) (from_binary_idl)

For attribute access: Life Cycle: python val -> msgpack bytes -> binary literal scalar -> resolved golang value -> binary literal scalar -> msgpack bytes -> python val (to_literal) (propeller attribute access) (from_binary_idl)

Parameter Type Description
binary_idl_object Binary
expected_python_type Type[T]

from_generic_idl() 

def from_generic_idl(
    generic: Struct,
    expected_python_type: Type[T],
) -> Optional[T]

TODO: Support all Flyte Types. This is for dataclass attribute access from input created from the Flyte Console.

Note:

  • This can be removed in the future when the Flyte Console support generate Binary IDL Scalar as input.
Parameter Type Description
generic Struct
expected_python_type Type[T]

get_literal_type() 

def get_literal_type(
    t: Type[ScikitLearn2ONNX],
) -> LiteralType

Converts the python type to a Flyte LiteralType

Parameter Type Description
t Type[ScikitLearn2ONNX]

guess_python_type() 

def guess_python_type(
    literal_type: LiteralType,
) -> Type[ScikitLearn2ONNX]

Converts the Flyte LiteralType to a python object type.

Parameter Type Description
literal_type LiteralType

isinstance_generic() 

def isinstance_generic(
    obj,
    generic_alias,
)
Parameter Type Description
obj
generic_alias

schema_match() 

def schema_match(
    schema: dict,
) -> bool

Check if a JSON schema fragment matches this transformer’s python_type.

For BaseModel subclasses, automatically compares the schema’s title, type, and required fields against the type’s own JSON schema. For other types, returns False by default — override if needed.

Parameter Type Description
schema dict

to_html() 

def to_html(
    ctx: FlyteContext,
    python_val: T,
    expected_python_type: Type[T],
) -> str

Converts any python val (dataframe, int, float) to a html string, and it will be wrapped in the HTML div

Parameter Type Description
ctx FlyteContext
python_val T
expected_python_type Type[T]

to_literal() 

def to_literal(
    ctx: FlyteContext,
    python_val: ScikitLearn2ONNX,
    python_type: Type[ScikitLearn2ONNX],
    expected: LiteralType,
) -> Literal

Converts a given python_val to a Flyte Literal, assuming the given python_val matches the declared python_type. Implementers should refrain from using type(python_val) instead rely on the passed in python_type. If these do not match (or are not allowed) the Transformer implementer should raise an AssertionError, clearly stating what was the mismatch

Parameter Type Description
ctx FlyteContext A FlyteContext, useful in accessing the filesystem and other attributes
python_val ScikitLearn2ONNX The actual value to be transformed
python_type Type[ScikitLearn2ONNX] The assumed type of the value (this matches the declared type on the function)
expected LiteralType Expected Literal Type

to_python_value() 

def to_python_value(
    ctx: FlyteContext,
    lv: Literal,
    expected_python_type: Type[ONNXFile],
) -> ONNXFile

Converts the given Literal to a Python Type. If the conversion cannot be done an AssertionError should be raised

Parameter Type Description
ctx FlyteContext FlyteContext
lv Literal The received literal Value
expected_python_type Type[ONNXFile] Expected native python type that should be returned