Skip to content

Python

model_navigator.python

Python optimize API.

optimize 

optimize(
    model,
    dataloader,
    sample_count=DEFAULT_SAMPLE_COUNT,
    batching=True,
    target_device=CPU,
    runners=None,
    optimization_profile=None,
    workspace=None,
    verbose=False,
    debug=False,
    verify_func=None,
    custom_configs=None,
)

Entrypoint for Python model optimize.

Perform correctness testing, profiling and model verification.

Parameters:

  • model (Callable) –

    Model inference function

  • dataloader (SizedDataLoader) –

    Sized iterable with data that will be feed to the model

  • sample_count (int, default: DEFAULT_SAMPLE_COUNT ) –

    Limits how many samples will be used from dataloader

  • batching (Optional[bool], default: True ) –

    Enable or disable batching on first (index 0) dimension of the model

  • target_device (Optional[DeviceKind], default: CPU ) –

    Target device for optimize process, default is CPU

  • runners (Optional[Tuple[Union[str, Type[NavigatorRunner]], ...]], default: None ) –

    Use only runners provided as parameter

  • optimization_profile (Optional[OptimizationProfile], default: None ) –

    Optimization profile for conversion and profiling

  • workspace (Optional[Path], default: None ) –

    Workspace where packages will be extracted

  • verbose (bool, default: False ) –

    Enable verbose logging

  • debug (bool, default: False ) –

    Enable debug logging from commands

  • verify_func (Optional[VerifyFunction], default: None ) –

    Function for additional model verification

  • custom_configs (Optional[Sequence[CustomConfig]], default: None ) –

    Sequence of CustomConfigs used to control produced artifacts

Returns:

  • Package –

    Package descriptor representing created package.

Source code in model_navigator/python/__init__.py 
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
def optimize(
    model: Callable,
    dataloader: SizedDataLoader,
    sample_count: int = DEFAULT_SAMPLE_COUNT,
    batching: Optional[bool] = True,
    target_device: Optional[DeviceKind] = DeviceKind.CPU,
    runners: Optional[Tuple[Union[str, Type[NavigatorRunner]], ...]] = None,
    optimization_profile: Optional[OptimizationProfile] = None,
    workspace: Optional[pathlib.Path] = None,
    verbose: bool = False,
    debug: bool = False,
    verify_func: Optional[VerifyFunction] = None,
    custom_configs: Optional[Sequence[CustomConfig]] = None,
) -> Package:
    """Entrypoint for Python model optimize.

    Perform correctness testing, profiling and model verification.

    Args:
        model: Model inference function
        dataloader: Sized iterable with data that will be feed to the model
        sample_count: Limits how many samples will be used from dataloader
        batching: Enable or disable batching on first (index 0) dimension of the model
        target_device: Target device for optimize process, default is CPU
        runners: Use only runners provided as parameter
        optimization_profile: Optimization profile for conversion and profiling
        workspace: Workspace where packages will be extracted
        verbose: Enable verbose logging
        debug: Enable debug logging from commands
        verify_func: Function for additional model verification
        custom_configs: Sequence of CustomConfigs used to control produced artifacts

    Returns:
        Package descriptor representing created package.
    """
    if isinstance(model, str):
        model = pathlib.Path(model)

    if runners is None:
        runners = default_runners(device_kind=target_device)

    if optimization_profile is None:
        optimization_profile = OptimizationProfile()

    runner_names = enums.parse(runners, lambda runner: runner if isinstance(runner, str) else runner.name())

    target_formats = DEFAULT_NONE_FRAMEWORK_TARGET_FORMATS

    config = CommonConfig(
        Framework.NONE,
        model=model,
        dataloader=dataloader,
        target_formats=target_formats,
        target_device=target_device,
        sample_count=sample_count,
        batch_dim=0 if batching else None,
        runner_names=runner_names,
        optimization_profile=optimization_profile,
        verbose=verbose,
        debug=debug,
        verify_func=verify_func,
        custom_configs=map_custom_configs(custom_configs=custom_configs),
    )

    models_config = ModelConfigBuilder.generate_model_config(
        framework=Framework.NONE,
        target_formats=target_formats,
        custom_configs=[],
    )

    builders = [
        preprocessing_builder,
        correctness_builder,
        performance_builder,
        verify_builder,
    ]

    package = optimize_pipeline(
        model=model,
        workspace=workspace,
        builders=builders,
        config=config,
        models_config=models_config,
    )

    return package