module nb_helper

Short summary

module mlprodict.nb_helper

Helpers for notebooks.

source on GitHub

Classes

class	truncated documentation
OnnxNotebook	Defines magic commands to help with notebooks

Functions

function	truncated documentation
onnxview	Displays an ONNX graph into a notebook.
register_onnx_magics	Register magics function, can be called from a notebook.

Properties

property	truncated documentation
Context	return the context or None
cross_validation_lock	A contextmanager for running a block with our cross validation lock set to True. At the end of the block, …

Methods

method	truncated documentation
onnxview	Defines %onnxview which displays an ONNX graph.

Documentation

Helpers for notebooks.

source on GitHub

class mlprodict.nb_helper.OnnxNotebook(**kwargs: Any)

Bases: MagicClassWithHelpers

Defines magic commands to help with notebooks

New in version 1.1.

source on GitHub

Create a configurable given a config config.

Parameters

configConfig

If this is empty, default values are used. If config is a Config instance, it will be used to configure the instance.

parentConfigurable instance, optional

The parent Configurable instance of this object.

Notes

Subclasses of Configurable must call the __init__() method of Configurable before doing anything else and using super():

class MyConfigurable(Configurable):
    def __init__(self, config=None):
        super(MyConfigurable, self).__init__(config=config)
        # Then any other code you need to finish initialization.

This ensures that instances will be configured properly.

_all_trait_default_generators: Dict[str, Any] = {'config': <bound method TraitType.default of <traitlets.traitlets.Instance object>>, 'parent': <bound method TraitType.default of <traitlets.traitlets.Instance object>>}

_cross_validation_lock: bool

_descriptors = [<traitlets.traitlets.ObserveHandler object>, <traitlets.traitlets.Instance object>, <traitlets.traitlets.Instance object>]

_instance_inits = [<bound method ObserveHandler.instance_init of <traitlets.traitlets.ObserveHandler object>>, <bound method Instance.instance_init of <traitlets.traitlets.Instance object>>, <bound method Instance.instance_init of <traitlets.traitlets.Instance object>>]

_static_immutable_initial_values: Dict[str, Any] = {'parent': None}

_trait_default_generators = {}

_trait_notifiers: Dict[str, Any]

_trait_validators: Dict[str, Any]

_trait_values: Dict[str, Any]

_traits: Dict[str, Any] = {'config': <traitlets.traitlets.Instance object>, 'parent': <traitlets.traitlets.Instance object>}

onnxview(line)

Defines %onnxview which displays an ONNX graph.

onnxview

The magic command %onnxview model_onnx is equivalent to function onnxview:

onnx_view(model_onnx)

It displays a visual representation of an ONNX graph.

source on GitHub

mlprodict.nb_helper.onnxview(graph, recursive=False, local=False, add_rt_shapes=False, runtime='python', size=None, html_size=None)

Displays an ONNX graph into a notebook.

Parameters:

• graph – filename, bytes, or onnx graph.

• recursive – display subgraph

• local – use local path to javascript dependencies, recommanded option if used on MyBinder)

• add_rt_shapes – add information about the shapes the runtime was able to find out, the runtime has to be ‘python’

• runtime – the view fails if a runtime does not implement a specific node unless runtime is ‘empty’

• size – graph size

• html_size – html size

Changed in version 0.6: Parameter runtime was added.

source on GitHub

mlprodict.nb_helper.register_onnx_magics(ip=None)

Register magics function, can be called from a notebook.

Parameters:

ip – from get_ipython()

source on GitHub