onnxruntime helpers

Frequent functions

onnxruntime.get_device() → str

Return the device used to compute the prediction (CPU, MKL, …)

<<<

import onnxruntime
print(onnxruntime.get_device())

>>>

    CPU
onnxruntime.get_all_providers() → List[str]

Return list of Execution Providers that this version of Onnxruntime can support. The order of elements represents the default priority order of Execution Providers from highest to lowest.

<<<

import pprint
import onnxruntime
pprint.pprint(onnxruntime.get_all_providers())

>>>

    ['TensorrtExecutionProvider',
     'CUDAExecutionProvider',
     'MIGraphXExecutionProvider',
     'ROCMExecutionProvider',
     'OpenVINOExecutionProvider',
     'DnnlExecutionProvider',
     'NupharExecutionProvider',
     'TvmExecutionProvider',
     'VitisAIExecutionProvider',
     'NnapiExecutionProvider',
     'CoreMLExecutionProvider',
     'ArmNNExecutionProvider',
     'ACLExecutionProvider',
     'DmlExecutionProvider',
     'RknpuExecutionProvider',
     'CPUExecutionProvider']
onnxruntime.get_available_providers() → List[str]

Return list of available Execution Providers in this installed version of Onnxruntime. The order of elements represents the default priority order of Execution Providers from highest to lowest.

<<<

import onnxruntime
import pprint
pprint.pprint(onnxruntime.get_available_providers())

>>>

    ['CPUExecutionProvider']
onnxruntime.set_default_logger_severity(arg0: int) → None

Sets the default logging severity. 0:Verbose, 1:Info, 2:Warning, 3:Error, 4:Fatal

onnxruntime.set_seed(arg0: int) → None

Sets the seed used for random number generation in Onnxruntime.

Python Wrapper OrtDevice

class onnxruntime.OrtDevice(c_ort_device)

A data structure that exposes the underlying C++ OrtDevice

Internal constructor

__init__(c_ort_device)

Internal constructor

_get_c_device()

Internal accessor to underlying object

device_id()
device_type()
static make(ort_device_name, device_id)

C class, OrtDevice or C_OrtDevice

class onnxruntime.capi._pybind_state.OrtDevice(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtDevice, arg0: int, arg1: int, arg2: int) → None

ONNXRuntime device informaion.

__init__(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtDevice, arg0: int, arg1: int, arg2: int) → None
static cpu() → int
static cuda() → int
static default_memory() → int
device_id(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtDevice) → int

Device Id.

device_type(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtDevice) → int

Device Type.

OrtMemoryInfo

class onnxruntime.capi._pybind_state.OrtMemoryInfo(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemoryInfo, arg0: str, arg1: onnxruntime.capi.onnxruntime_pybind11_state.OrtAllocatorType, arg2: int, arg3: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemType) → None
__init__(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemoryInfo, arg0: str, arg1: onnxruntime.capi.onnxruntime_pybind11_state.OrtAllocatorType, arg2: int, arg3: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemType) → None

C classes, frequent types

class onnxruntime.capi._pybind_state.ModelMetadata

Pre-defined and custom metadata about the model. It is usually used to identify the model used to run the prediction and facilitate the comparison.

__init__(*args, **kwargs)
property custom_metadata_map

additional metadata

property description

description of the model

property domain

ONNX domain

property graph_description

description of the graph hosted in the model

property graph_name

graph name

property producer_name

producer name

property version

version of the model

class onnxruntime.capi._pybind_state.OrtMemType(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemType, value: int) → None

Members:

CPU_INPUT

CPU_OUTPUT

CPU

DEFAULT

__eq__(self: object, other: object) → bool
__getstate__(self: object) → int
__hash__(self: object) → int
__index__(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemType) → int
__init__(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemType, value: int) → None
__int__(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemType) → int
__members__ = {'CPU': <OrtMemType.CPU_OUTPUT: -1>, 'CPU_INPUT': <OrtMemType.CPU_INPUT: -2>, 'CPU_OUTPUT': <OrtMemType.CPU_OUTPUT: -1>, 'DEFAULT': <OrtMemType.DEFAULT: 0>}
__ne__(self: object, other: object) → bool
__repr__(self: object) → str
__setstate__(self: onnxruntime.capi.onnxruntime_pybind11_state.OrtMemType, state: int) → None
__str__()

name(self: handle) -> str

property name

Rare functions

onnxruntime.capi._pybind_state.clear_training_ep_instances() → None

Clean the execution provider instances used in ort training module.

onnxruntime.capi._pybind_state.create_and_register_allocator(arg0: OrtMemoryInfo, arg1: OrtArenaCfg) → None
onnxruntime.capi._pybind_state.enable_telemetry_events() → None

Enables platform-specific telemetry collection where applicable.

onnxruntime.capi._pybind_state.disable_telemetry_events() → None

Disables platform-specific telemetry collection.

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Summary of onnxruntime and onnxruntime-training API

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OrtValue