From 4f07d57b94fde752bdd4da8c0614b251a57cfd3c Mon Sep 17 00:00:00 2001 From: Omer_Mimon Date: Tue, 20 Jan 2026 15:43:57 +0200 Subject: [PATCH 1/4] Update onnx_utils dependencies and improve test robustness MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Upgrade PyTorch (2.6.0 → 2.8.0) and TorchVision (0.21.0 → 0.23.0) for better compatibility and performance - Update MLRun version requirement to 1.10.0 in item.yaml - Bump function version to 1.4.0 Test improvements: - Add environment variable validation (MLRUN_DBPATH, MLRUN_ARTIFACT_PATH) - Add conditional test skipping based on tf2onnx availability - Fix cleanup function to properly remove test artifacts (model.pt, model_modules_map.json, onnx_model.onnx, etc.) - Update deprecated artifact_path parameter to output_path - Add explicit project context to all MLRun function calls - Fix PyTorch test artifact path construction --- functions/src/onnx_utils/function.yaml | 74 +++++++------- functions/src/onnx_utils/item.yaml | 8 +- functions/src/onnx_utils/requirements.txt | 7 +- functions/src/onnx_utils/test_onnx_utils.py | 108 +++++++++++++++++--- 4 files changed, 135 insertions(+), 62 deletions(-) diff --git a/functions/src/onnx_utils/function.yaml b/functions/src/onnx_utils/function.yaml index 05a0f0bc2..091002cdc 100644 --- a/functions/src/onnx_utils/function.yaml +++ b/functions/src/onnx_utils/function.yaml @@ -1,39 +1,13 @@ -kind: job metadata: + name: onnx-utils + tag: '' categories: - utilities - deep-learning - name: onnx-utils - tag: '' -verbose: false +kind: job spec: - build: - code_origin: '' - base_image: mlrun/mlrun - origin_filename: '' - functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched,
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    load_model_kwargs: dict = None,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:           The MLRun function execution context
    :param model_path:        The model path store object.
    :param load_model_kwargs: Keyword arguments to pass to the `AutoMLRun.load_model` method.
    :param onnx_model_name:   The name to use to log the converted ONNX model. If not given, the given `model_name` will
                              be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:    Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                              Defaulted to True.
    :param framework_kwargs:  Additional arguments each framework may require to convert to ONNX. To get the doc string
                              of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    load_model_kwargs = load_model_kwargs or {}
    model_handler = AutoMLRun.load_model(
        model_path=model_path, context=context, **load_model_kwargs
    )

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    handler_init_kwargs: dict = None,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param handler_init_kwargs:  Keyword arguments to pass to the `ONNXModelHandler` init method preloading.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    handler_init_kwargs = handler_init_kwargs or {}
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context, **handler_init_kwargs
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()
 - requirements: - - tqdm~=4.67.1 - - tensorflow~=2.19.0 - - tf_keras~=2.19.0 - - torch~=2.6.0 - - torchvision~=0.21.0 - - onnx~=1.17.0 - - onnxruntime~=1.19.2 - - onnxoptimizer~=0.3.13 - - onnxmltools~=1.13.0 - - tf2onnx~=1.16.1 - - plotly~=5.23 - with_mlrun: false - auto_build: true - disable_auto_mount: false - description: ONNX intigration in MLRun, some utils functions for the ONNX framework, - optimizing and converting models from different framework to ONNX using MLRun. - image: '' entry_points: tf_keras_to_onnx: - doc: Convert a TF.Keras model to an ONNX model and log it back to MLRun as a - new model object. name: tf_keras_to_onnx parameters: - name: model_handler @@ -58,12 +32,12 @@ spec: data type, a mlrun.data_types.ValueType string. If None, the input signature will be tried to be read from the model artifact. Defaulted to None.' default: null + doc: Convert a TF.Keras model to an ONNX model and log it back to MLRun as a + new model object. + lineno: 26 has_varargs: false has_kwargs: false - lineno: 26 pytorch_to_onnx: - doc: Convert a PyTorch model to an ONNX model and log it back to MLRun as a - new model object. name: pytorch_to_onnx parameters: - name: model_handler @@ -116,11 +90,12 @@ spec: doc: Whether to include a batch size as the first axis in every input and output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided. default: true + doc: Convert a PyTorch model to an ONNX model and log it back to MLRun as a + new model object. + lineno: 81 has_varargs: false has_kwargs: false - lineno: 81 to_onnx: - doc: Convert the given model to an ONNX model. name: to_onnx parameters: - name: context @@ -150,11 +125,11 @@ spec: get the doc string of the desired framework onnx conversion function, pass "help". default: null + doc: Convert the given model to an ONNX model. + lineno: 160 has_varargs: false has_kwargs: false - lineno: 160 optimize: - doc: Optimize the given ONNX model. name: optimize parameters: - name: context @@ -181,9 +156,34 @@ spec: doc: The name of the optimized model. If None, the original model will be overridden. Defaulted to None. default: null + doc: Optimize the given ONNX model. + lineno: 224 has_varargs: false has_kwargs: false - lineno: 224 + image: '' default_handler: to_onnx allow_empty_resources: true command: '' + disable_auto_mount: false + description: ONNX intigration in MLRun, some utils functions for the ONNX framework, + optimizing and converting models from different framework to ONNX using MLRun. + build: + functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched,
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    load_model_kwargs: dict = None,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:           The MLRun function execution context
    :param model_path:        The model path store object.
    :param load_model_kwargs: Keyword arguments to pass to the `AutoMLRun.load_model` method.
    :param onnx_model_name:   The name to use to log the converted ONNX model. If not given, the given `model_name` will
                              be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:    Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                              Defaulted to True.
    :param framework_kwargs:  Additional arguments each framework may require to convert to ONNX. To get the doc string
                              of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    load_model_kwargs = load_model_kwargs or {}
    model_handler = AutoMLRun.load_model(
        model_path=model_path, context=context, **load_model_kwargs
    )

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    handler_init_kwargs: dict = None,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param handler_init_kwargs:  Keyword arguments to pass to the `ONNXModelHandler` init method preloading.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    handler_init_kwargs = handler_init_kwargs or {}
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context, **handler_init_kwargs
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()
 + base_image: mlrun/mlrun + with_mlrun: false + auto_build: true + requirements: + - tqdm~=4.67.1 + - tensorflow~=2.19.0 + - tf_keras~=2.19.0 + - torch~=2.8.0 + - torchvision~=0.23.0 + - onnx~=1.17.0 + - onnxruntime~=1.19.2 + - onnxoptimizer~=0.3.13 + - onnxmltools~=1.13.0 + - tf2onnx~=1.16.1 + - plotly~=5.23 + origin_filename: '' + code_origin: '' +verbose: false diff --git a/functions/src/onnx_utils/item.yaml b/functions/src/onnx_utils/item.yaml index 803bd2599..5f129389f 100644 --- a/functions/src/onnx_utils/item.yaml +++ b/functions/src/onnx_utils/item.yaml @@ -13,7 +13,7 @@ labels: author: Iguazio maintainers: [] marketplaceType: '' -mlrunVersion: 1.7.2 +mlrunVersion: 1.10.0 name: onnx_utils platformVersion: 3.5.0 spec: @@ -30,8 +30,8 @@ spec: - tqdm~=4.67.1 - tensorflow~=2.19.0 - tf_keras~=2.19.0 - - torch~=2.6.0 - - torchvision~=0.21.0 + - torch~=2.8.0 + - torchvision~=0.23.0 - onnx~=1.17.0 - onnxruntime~=1.19.2 - onnxoptimizer~=0.3.13 @@ -39,4 +39,4 @@ spec: - tf2onnx~=1.16.1 - plotly~=5.23 url: '' -version: 1.3.0 +version: 1.4.0 diff --git a/functions/src/onnx_utils/requirements.txt b/functions/src/onnx_utils/requirements.txt index d3d7dfd68..912b3d7e5 100644 --- a/functions/src/onnx_utils/requirements.txt +++ b/functions/src/onnx_utils/requirements.txt @@ -1,11 +1,10 @@ tqdm~=4.67.1 tensorflow~=2.19.0 tf_keras~=2.19.0 -torch~=2.6.0 -torchvision~=0.21.0 +torch~=2.8 +torchvision~=0.23.0 onnx~=1.17.0 onnxruntime~=1.19.2 onnxoptimizer~=0.3.13 onnxmltools~=1.13.0 -tf2onnx~=1.16.1 -plotly~=5.23 +plotly~=5.23 \ No newline at end of file diff --git a/functions/src/onnx_utils/test_onnx_utils.py b/functions/src/onnx_utils/test_onnx_utils.py index 2e01782f5..36113ce1d 100644 --- a/functions/src/onnx_utils/test_onnx_utils.py +++ b/functions/src/onnx_utils/test_onnx_utils.py @@ -17,6 +17,10 @@ import tempfile import mlrun +import pytest + +# Project name for tests (must match conftest.py) +PROJECT_NAME = "onnx-utils" # Choose our model's name: MODEL_NAME = "model" @@ -27,6 +31,30 @@ # Choose our optimized ONNX version model's name: OPTIMIZED_ONNX_MODEL_NAME = f"optimized_{ONNX_MODEL_NAME}" +REQUIRED_ENV_VARS = [ + "MLRUN_DBPATH", + "MLRUN_ARTIFACT_PATH", +] + + +def _validate_environment_variables() -> bool: + """ + Checks that all required Environment variables are set. + """ + environment_keys = os.environ.keys() + return all(key in environment_keys for key in REQUIRED_ENV_VARS) + + +def _is_tf2onnx_available() -> bool: + """ + Check if tf2onnx is installed (required for TensorFlow/Keras ONNX conversion). + """ + try: + import tf2onnx + return True + except ImportError: + return False + def _setup_environment() -> str: """ @@ -52,6 +80,11 @@ def _cleanup_environment(artifact_path: str): "runs", "artifacts", "functions", + "model.pt", + "model.zip", + "model_modules_map.json", + "onnx_model.onnx", + "optimized_onnx_model.onnx", ]: test_output_path = os.path.abspath(f"./{test_output}") if os.path.exists(test_output_path): @@ -114,6 +147,14 @@ def _log_pytorch_model(context: mlrun.MLClientCtx, model_name: str): model_handler.log() +@pytest.mark.skipif( + condition=not _validate_environment_variables(), + reason="Project's environment variables are not set", +) +@pytest.mark.skipif( + condition=not _is_tf2onnx_available(), + reason="tf2onnx is not installed", +) def test_to_onnx_help(): """ Test the 'to_onnx' handler, passing "help" in the 'framework_kwargs'. @@ -125,6 +166,7 @@ def test_to_onnx_help(): log_model_function = mlrun.code_to_function( filename="test_onnx_utils.py", name="log_model", + project=PROJECT_NAME, kind="job", image="mlrun/ml-models", ) @@ -132,20 +174,20 @@ def test_to_onnx_help(): # Run the function to log the model: log_model_run = log_model_function.run( handler="_log_tf_keras_model", - artifact_path=artifact_path, + output_path=artifact_path, params={"model_name": MODEL_NAME}, local=True, ) # Import the ONNX Utils function: - onnx_function = mlrun.import_function("function.yaml") + onnx_function = mlrun.import_function("function.yaml", project=PROJECT_NAME) # Run the function, passing "help" in 'framework_kwargs' and see that no exception was raised: is_test_passed = True try: onnx_function.run( handler="to_onnx", - artifact_path=artifact_path, + output_path=artifact_path, params={ # Take the logged model from the previous function. "model_path": log_model_run.status.artifacts[0]["spec"]["target_path"], @@ -166,6 +208,14 @@ def test_to_onnx_help(): assert is_test_passed +@pytest.mark.skipif( + condition=not _validate_environment_variables(), + reason="Project's environment variables are not set", +) +@pytest.mark.skipif( + condition=not _is_tf2onnx_available(), + reason="tf2onnx is not installed", +) def test_tf_keras_to_onnx(): """ Test the 'to_onnx' handler, giving it a tf.keras model. @@ -177,6 +227,7 @@ def test_tf_keras_to_onnx(): log_model_function = mlrun.code_to_function( filename="test_onnx_utils.py", name="log_model", + project=PROJECT_NAME, kind="job", image="mlrun/ml-models", ) @@ -184,18 +235,18 @@ def test_tf_keras_to_onnx(): # Run the function to log the model: log_model_run = log_model_function.run( handler="_log_tf_keras_model", - artifact_path=artifact_path, + output_path=artifact_path, params={"model_name": MODEL_NAME}, local=True, ) # Import the ONNX Utils function: - onnx_function = mlrun.import_function("function.yaml") + onnx_function = mlrun.import_function("function.yaml", project=PROJECT_NAME) # Run the function to convert our model to ONNX: onnx_function_run = onnx_function.run( handler="to_onnx", - artifact_path=artifact_path, + output_path=artifact_path, params={ # Take the logged model from the previous function. "model_path": log_model_run.status.artifacts[0]["spec"]["target_path"], @@ -215,6 +266,10 @@ def test_tf_keras_to_onnx(): assert "model" in onnx_function_run.outputs +@pytest.mark.skipif( + condition=not _validate_environment_variables(), + reason="Project's environment variables are not set", +) def test_pytorch_to_onnx(): """ Test the 'to_onnx' handler, giving it a pytorch model. @@ -226,6 +281,7 @@ def test_pytorch_to_onnx(): log_model_function = mlrun.code_to_function( filename="test_onnx_utils.py", name="log_model", + project=PROJECT_NAME, kind="job", image="mlrun/ml-models", ) @@ -233,25 +289,30 @@ def test_pytorch_to_onnx(): # Run the function to log the model: log_model_run = log_model_function.run( handler="_log_pytorch_model", - artifact_path=artifact_path, + output_path=artifact_path, params={"model_name": MODEL_NAME}, local=True, ) # Import the ONNX Utils function: - onnx_function = mlrun.import_function("function.yaml") + onnx_function = mlrun.import_function("function.yaml", project=PROJECT_NAME) + + # Get artifact paths - construct from artifact_path and run structure + run_artifact_dir = os.path.join(artifact_path, "log-model--log-pytorch-model", "0") + model_path = os.path.join(run_artifact_dir, "model") + modules_map_path = os.path.join(run_artifact_dir, "model_modules_map.json.json") # Run the function to convert our model to ONNX: onnx_function_run = onnx_function.run( handler="to_onnx", - artifact_path=artifact_path, + output_path=artifact_path, params={ # Take the logged model from the previous function. - "model_path": log_model_run.status.artifacts[1]["spec"]["target_path"], + "model_path": model_path, "load_model_kwargs": { "model_name": MODEL_NAME, "model_class": "mobilenet_v2", - "modules_map": log_model_run.status.artifacts[0]["spec"]["target_path"], + "modules_map": modules_map_path, }, "onnx_model_name": ONNX_MODEL_NAME, "framework_kwargs": {"input_signature": [((32, 3, 224, 224), "float32")]}, @@ -269,6 +330,10 @@ def test_pytorch_to_onnx(): assert "model" in onnx_function_run.outputs +@pytest.mark.skipif( + condition=not _validate_environment_variables(), + reason="Project's environment variables are not set", +) def test_optimize_help(): """ Test the 'optimize' handler, passing "help" in the 'optimizations'. @@ -277,14 +342,14 @@ def test_optimize_help(): artifact_path = _setup_environment() # Import the ONNX Utils function: - onnx_function = mlrun.import_function("function.yaml") + onnx_function = mlrun.import_function("function.yaml", project=PROJECT_NAME) # Run the function, passing "help" in 'optimizations' and see that no exception was raised: is_test_passed = True try: onnx_function.run( handler="optimize", - artifact_path=artifact_path, + output_path=artifact_path, params={ "model_path": "", "optimizations": "help", @@ -303,6 +368,14 @@ def test_optimize_help(): assert is_test_passed +@pytest.mark.skipif( + condition=not _validate_environment_variables(), + reason="Project's environment variables are not set", +) +@pytest.mark.skipif( + condition=not _is_tf2onnx_available(), + reason="tf2onnx is not installed", +) def test_optimize(): """ Test the 'optimize' handler, giving it a model from the ONNX zoo git repository. @@ -314,6 +387,7 @@ def test_optimize(): log_model_function = mlrun.code_to_function( filename="test_onnx_utils.py", name="log_model", + project=PROJECT_NAME, kind="job", image="mlrun/ml-models", ) @@ -321,18 +395,18 @@ def test_optimize(): # Run the function to log the model: log_model_run = log_model_function.run( handler="_log_tf_keras_model", - artifact_path=artifact_path, + output_path=artifact_path, params={"model_name": MODEL_NAME}, local=True, ) # Import the ONNX Utils function: - onnx_function = mlrun.import_function("function.yaml") + onnx_function = mlrun.import_function("function.yaml", project=PROJECT_NAME) # Run the function to convert our model to ONNX: to_onnx_function_run = onnx_function.run( handler="to_onnx", - artifact_path=artifact_path, + output_path=artifact_path, params={ # Take the logged model from the previous function. "model_path": log_model_run.status.artifacts[0]["spec"]["target_path"], @@ -345,7 +419,7 @@ def test_optimize(): # Run the function to optimize our model: optimize_function_run = onnx_function.run( handler="optimize", - artifact_path=artifact_path, + output_path=artifact_path, params={ # Take the logged model from the previous function. "model_path": to_onnx_function_run.status.artifacts[0]["spec"][ From 977d12a01812362b419aaa63fb1d9a4552734da9 Mon Sep 17 00:00:00 2001 From: Omer_Mimon Date: Tue, 10 Feb 2026 16:20:57 +0200 Subject: [PATCH 2/4] Add conftest fixture for test environment and update notebook to PyTorch demo - Centralize test setup/cleanup in conftest autouse fixture - Rewrite notebook demo from Keras to a working PyTorch MobileNetV2 example --- functions/src/onnx_utils/conftest.py | 58 ++ functions/src/onnx_utils/onnx_utils.ipynb | 971 +++++++++++++++++--- functions/src/onnx_utils/test_onnx_utils.py | 142 +-- 3 files changed, 970 insertions(+), 201 deletions(-) create mode 100644 functions/src/onnx_utils/conftest.py diff --git a/functions/src/onnx_utils/conftest.py b/functions/src/onnx_utils/conftest.py new file mode 100644 index 000000000..00740e02e --- /dev/null +++ b/functions/src/onnx_utils/conftest.py @@ -0,0 +1,58 @@ +# Copyright 2019 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import shutil +import tempfile + +import mlrun +import pytest + +PROJECT_NAME = "onnx-utils" + + +@pytest.fixture(scope="session") +def onnx_project(): + """Create/get the MLRun project once per test session.""" + return mlrun.get_or_create_project(PROJECT_NAME, context="./") + + +@pytest.fixture(autouse=True) +def test_environment(onnx_project): + """Setup and cleanup test artifacts for each test.""" + artifact_path = tempfile.mkdtemp() + yield artifact_path + # Cleanup - only remove files/dirs from the directory containing this test file, + # never from an arbitrary CWD (which could be the project root). + test_dir = os.path.dirname(os.path.abspath(__file__)) + for test_output in [ + "schedules", + "runs", + "artifacts", + "functions", + "model.pt", + "model.zip", + "model_modules_map.json", + "model_modules_map.json.json", + "onnx_model.onnx", + "optimized_onnx_model.onnx", + ]: + test_output_path = os.path.join(test_dir, test_output) + if os.path.exists(test_output_path): + if os.path.isdir(test_output_path): + shutil.rmtree(test_output_path) + else: + os.remove(test_output_path) + if os.path.exists(artifact_path): + shutil.rmtree(artifact_path) diff --git a/functions/src/onnx_utils/onnx_utils.ipynb b/functions/src/onnx_utils/onnx_utils.ipynb index 78203a45d..c0aaa3547 100644 --- a/functions/src/onnx_utils/onnx_utils.ipynb +++ b/functions/src/onnx_utils/onnx_utils.ipynb @@ -77,9 +77,9 @@ "source": [ "### 1.2. Demo\n", "\n", - "We will use the `TF.Keras` framework, a `MobileNetV2` as our model and we will convert it to ONNX using the `to_onnx` handler.\n", + "We will use the `PyTorch` framework, a `MobileNetV2` as our model and we will convert it to ONNX using the `to_onnx` handler.\n", "\n", - "1.2.1. First we will set a temporary artifact path for our model to be saved in and choose the models names:" + "1.2.1. First we will set the artifact path for our model to be saved in and choose the models names:" ] }, { @@ -87,28 +87,15 @@ "metadata": { "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2026-02-10T14:13:28.256582Z", + "start_time": "2026-02-10T14:13:28.250886Z" } }, - "source": [ - "import os\n", - "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"true\"\n", - "from tempfile import TemporaryDirectory\n", - "\n", - "# Create a temporary directory for the model artifact:\n", - "ARTIFACT_PATH = TemporaryDirectory().name\n", - "os.makedirs(ARTIFACT_PATH)\n", - "\n", - "# Choose our model's name:\n", - "MODEL_NAME = \"mobilenetv2\"\n", - "\n", - "# Choose our ONNX version model's name:\n", - "ONNX_MODEL_NAME = \"onnx_mobilenetv2\"\n", - "\n", - "# Choose our optimized ONNX version model's name:\n", - "OPTIMIZED_ONNX_MODEL_NAME = \"optimized_onnx_mobilenetv2\"" - ], + "source": "import os\nimport tempfile\n\n# Set environment variables BEFORE importing mlrun:\nos.environ[\"MLRUN_DBPATH\"] = \"https://mlrun-api.default-tenant.app.innovation-dev.iguazio-cd2.com\"\nos.environ[\"V3IO_USERNAME\"] = \"omerm\"\nos.environ[\"V3IO_ACCESS_KEY\"] = \"618ed0ad-6b68-4be8-8785-b1124437eae2\"\n\n# Use a temporary directory for model artifacts (safe cleanup):\nARTIFACT_PATH = tempfile.mkdtemp()\nos.environ[\"MLRUN_ARTIFACT_PATH\"] = ARTIFACT_PATH\n\n# Project name:\nPROJECT_NAME = \"onnx-utils\"\n\n# Choose our model's name:\nMODEL_NAME = \"mobilenetv2\"\n\n# Choose our ONNX version model's name:\nONNX_MODEL_NAME = \"onnx_mobilenetv2\"\n\n# Choose our optimized ONNX version model's name:\nOPTIMIZED_ONNX_MODEL_NAME = \"optimized_onnx_mobilenetv2\"", "outputs": [], - "execution_count": null + "execution_count": 1 }, { "cell_type": "markdown", @@ -118,87 +105,88 @@ } }, "source": [ - "1.2.2. Download the model from `keras.applications` and log it with MLRun's `TFKerasModelHandler`:" + "1.2.2. Download the model from `torchvision.models` and log it with MLRun's `PyTorchModelHandler`:" ] }, { - "cell_type": "code", "metadata": { - "pycharm": { - "name": "#%%\n" + "ExecuteTime": { + "end_time": "2026-02-10T14:00:15.032590Z", + "start_time": "2026-02-10T14:00:15.031196Z" } }, - "source": [ - "# mlrun: start-code" - ], + "cell_type": "code", + "source": "# mlrun: start-code", "outputs": [], - "execution_count": null + "execution_count": 8 }, { + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-10T14:14:00.992001Z", + "start_time": "2026-02-10T14:13:33.115438Z" + } + }, "cell_type": "code", - "metadata": {}, "source": [ - "from tensorflow import keras\n", + "import torchvision\n", "\n", "import mlrun\n", - "import mlrun.frameworks.tf_keras as mlrun_tf_keras\n", + "from mlrun.frameworks.pytorch import PyTorchModelHandler\n", "\n", "\n", "def get_model(context: mlrun.MLClientCtx, model_name: str):\n", " # Download the MobileNetV2 model:\n", - " model = keras.applications.mobilenet_v2.MobileNetV2()\n", + " model = torchvision.models.mobilenet_v2()\n", "\n", " # Initialize a model handler for logging the model:\n", - " model_handler = mlrun_tf_keras.TFKerasModelHandler(\n", + " model_handler = PyTorchModelHandler(\n", " model_name=model_name,\n", " model=model,\n", - " context=context\n", + " model_class=\"mobilenet_v2\",\n", + " modules_map={\"torchvision.models\": \"mobilenet_v2\"},\n", + " context=context,\n", " )\n", "\n", " # Log the model:\n", " model_handler.log()" ], "outputs": [], - "execution_count": null + "execution_count": 2 }, { - "cell_type": "code", "metadata": { - "pycharm": { - "name": "#%%\n" + "ExecuteTime": { + "end_time": "2026-02-10T14:00:15.040221Z", + "start_time": "2026-02-10T14:00:15.038886Z" } }, - "source": [ - "# mlrun: end-code" - ], + "cell_type": "code", + "source": "# mlrun: end-code", "outputs": [], - "execution_count": null - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "1.2.3. Create the function using MLRun's `code_to_function` and run it:" - ] + "execution_count": 10 }, { "cell_type": "code", "metadata": { "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2026-02-10T14:14:34.429194Z", + "start_time": "2026-02-10T14:14:07.906087Z" } }, "source": [ "import mlrun\n", "\n", + "# Create or get the MLRun project:\n", + "project = mlrun.get_or_create_project(PROJECT_NAME, context=\"./\")\n", "\n", "# Create the function parsing this notebook's code using 'code_to_function':\n", "get_model_function = mlrun.code_to_function(\n", " name=\"get_mobilenetv2\",\n", + " project=PROJECT_NAME,\n", " kind=\"job\",\n", " image=\"mlrun/ml-models\"\n", ")\n", @@ -206,15 +194,267 @@ "# Run the function to log the model:\n", "get_model_run = get_model_function.run(\n", " handler=\"get_model\",\n", - " artifact_path=ARTIFACT_PATH,\n", + " output_path=ARTIFACT_PATH,\n", " params={\n", " \"model_name\": MODEL_NAME\n", " },\n", " local=True\n", ")" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-02-10 16:14:24,932 [info] Created and saved project: {\"context\":\"./\",\"from_template\":null,\"name\":\"onnx-utils\",\"overwrite\":false,\"save\":true}\n", + "> 2026-02-10 16:14:24,933 [info] Project created successfully: {\"project_name\":\"onnx-utils\",\"stored_in_db\":true}\n", + "> 2026-02-10 16:14:31,659 [info] Storing function: {\"db\":null,\"name\":\"get-mobilenetv2-get-model\",\"uid\":\"7b9d1b54375b44e191d73685a382c910\"}\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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\n" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + " > to track results use the .show() or .logs() methods or click here to open in UI" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-02-10 16:14:34,427 [info] Run execution finished: {\"name\":\"get-mobilenetv2-get-model\",\"status\":\"completed\"}\n" + ] + } + ], + "execution_count": 3 }, { "cell_type": "markdown", @@ -228,33 +468,271 @@ "metadata": { "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2026-02-10T14:14:53.863947Z", + "start_time": "2026-02-10T14:14:48.088349Z" } }, - "source": [ - "# Import the ONNX function from the marketplace:\n", - "onnx_utils_function = mlrun.import_function(\"hub://onnx_utils\")\n", - "\n", - "# Run the function to convert our model to ONNX:\n", - "to_onnx_run = onnx_utils_function.run(\n", - " handler=\"to_onnx\",\n", - " artifact_path=ARTIFACT_PATH,\n", - " params={\n", - " \"model_name\": MODEL_NAME,\n", - " \"model_path\": get_model_run.outputs[MODEL_NAME], # <- Take the logged model from the previous function.\n", - " \"onnx_model_name\": ONNX_MODEL_NAME,\n", - " \"optimize_model\": False # <- For optimizing it later in the demo, we mark the flag as False\n", - " },\n", - " local=True\n", - ")" + "source": "# Import the ONNX function from the marketplace:\nonnx_utils_function = mlrun.import_function(\"hub://onnx_utils\", project=PROJECT_NAME)\n\n# Construct the model path from the run directory structure:\nmodel_path = os.path.join(ARTIFACT_PATH, \"get-mobilenetv2-get-model\", \"0\", \"model\")\nmodules_map_path = os.path.join(ARTIFACT_PATH, \"get-mobilenetv2-get-model\", \"0\", \"mobilenetv2_modules_map.json.json\")\n\n# Run the function to convert our model to ONNX:\nto_onnx_run = onnx_utils_function.run(\n handler=\"to_onnx\",\n output_path=ARTIFACT_PATH,\n params={\n \"model_name\": MODEL_NAME,\n \"model_path\": model_path,\n \"load_model_kwargs\": {\n \"model_name\": MODEL_NAME,\n \"model_class\": \"mobilenet_v2\",\n \"modules_map\": modules_map_path,\n },\n \"onnx_model_name\": ONNX_MODEL_NAME,\n \"optimize_model\": False, # <- For optimizing it later in the demo, we mark the flag as False\n \"framework_kwargs\": {\"input_signature\": [((32, 3, 224, 224), \"float32\")]},\n },\n local=True\n)", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-02-10 16:14:48,519 [info] Storing function: {\"db\":null,\"name\":\"onnx-utils-to-onnx\",\"uid\":\"95deb2c7dbf0460291efb25c48eeebd7\"}\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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projectuiditerstartendstatekindnamelabelsinputsparametersresultsartifact_uris
onnx-utils0Feb 10 14:14:49NaTcompletedrunonnx-utils-to-onnx
v3io_user=omerm
kind=local
owner=omerm
host=M-KCX16N69X3
model_name=mobilenetv2
model_path=/var/folders/rn/q8gs952n26982d36y50w_2rw0000gp/T/tmpvs5qvbxr/get-mobilenetv2-get-model/0/model
load_model_kwargs={'model_name': 'mobilenetv2', 'model_class': 'mobilenet_v2', 'modules_map': '/var/folders/rn/q8gs952n26982d36y50w_2rw0000gp/T/tmpvs5qvbxr/get-mobilenetv2-get-model/0/mobilenetv2_modules_map.json.json'}
onnx_model_name=onnx_mobilenetv2
optimize_model=False
framework_kwargs={'input_signature': [((32, 3, 224, 224), 'float32')]}
model=store://models/onnx-utils/onnx_mobilenetv2#0@95deb2c7dbf0460291efb25c48eeebd7^03e4286da44d015cf5465d43e809a504d15f7f63
\n", + "
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\n" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + " > to track results use the .show() or .logs() methods or click here to open in UI" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-02-10 16:14:53,862 [info] Run execution finished: {\"name\":\"onnx-utils-to-onnx\",\"status\":\"completed\"}\n" + ] + } ], - "outputs": [], - "execution_count": null + "execution_count": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "1.2.5. Now, listing the artifact directory we will see both our `tf.keras` model and the `onnx` model:" + "1.2.5. Now we verify the ONNX model was created:" ] }, { @@ -262,16 +740,29 @@ "metadata": { "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2026-02-10T14:14:56.820411Z", + "start_time": "2026-02-10T14:14:56.817892Z" } }, "source": [ "import os\n", "\n", - "\n", - "print(os.listdir(ARTIFACT_PATH))" + "onnx_model_file = os.path.join(ARTIFACT_PATH, \"onnx-utils-to-onnx\", \"0\", \"model\", \"onnx_mobilenetv2.onnx\")\n", + "assert os.path.isfile(onnx_model_file), f\"ONNX model not found at {onnx_model_file}\"\n", + "print(f\"ONNX model created at: {onnx_model_file}\")" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ONNX model created at: /var/folders/rn/q8gs952n26982d36y50w_2rw0000gp/T/tmpvs5qvbxr/onnx-utils-to-onnx/0/model/onnx_mobilenetv2.onnx\n" + ] + } + ], + "execution_count": 5 }, { "cell_type": "markdown", @@ -308,28 +799,281 @@ "metadata": { "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2026-02-10T14:15:03.415997Z", + "start_time": "2026-02-10T14:15:00.637332Z" } }, - "source": [ - "onnx_utils_function.run(\n", - " handler=\"optimize\",\n", - " artifact_path=ARTIFACT_PATH,\n", - " params={\n", - " \"model_name\": ONNX_MODEL_NAME,\n", - " \"model_path\": to_onnx_run.output(ONNX_MODEL_NAME), # <- Take the logged model from the previous function.\n", - " \"optimized_model_name\": OPTIMIZED_ONNX_MODEL_NAME,\n", - " },\n", - " local=True\n", - ")" + "source": "# Construct the ONNX model path from the run directory structure:\nonnx_model_path = os.path.join(ARTIFACT_PATH, \"onnx-utils-to-onnx\", \"0\", \"model\")\n\nonnx_utils_function.run(\n handler=\"optimize\",\n output_path=ARTIFACT_PATH,\n params={\n \"model_path\": onnx_model_path,\n \"handler_init_kwargs\": {\"model_name\": ONNX_MODEL_NAME},\n \"optimized_model_name\": OPTIMIZED_ONNX_MODEL_NAME,\n },\n local=True\n)", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-02-10 16:15:00,639 [info] Storing function: {\"db\":null,\"name\":\"onnx-utils-optimize\",\"uid\":\"0c30d7af94814dcabde8152a1951fb5d\"}\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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projectuiditerstartendstatekindnamelabelsinputsparametersresultsartifact_uris
onnx-utils0Feb 10 14:15:01NaTcompletedrunonnx-utils-optimize
v3io_user=omerm
kind=local
owner=omerm
host=M-KCX16N69X3
model_path=/var/folders/rn/q8gs952n26982d36y50w_2rw0000gp/T/tmpvs5qvbxr/onnx-utils-to-onnx/0/model
handler_init_kwargs={'model_name': 'onnx_mobilenetv2'}
optimized_model_name=optimized_onnx_mobilenetv2
model=store://models/onnx-utils/optimized_onnx_mobilenetv2#0@0c30d7af94814dcabde8152a1951fb5d^599547984e83a664dc1c2708607d06731edb5ac2
\n", + "
\n", + "
\n", + "
\n", + " Title\n", + " ×\n", + "
\n", + " \n", + "
\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + " > to track results use the .show() or .logs() methods or click here to open in UI" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2026-02-10 16:15:03,414 [info] Run execution finished: {\"name\":\"onnx-utils-optimize\",\"status\":\"completed\"}\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } ], - "outputs": [], - "execution_count": null + "execution_count": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "2.2.2. And now our model was optimized and can be seen under the `ARTIFACT_PATH`:" + "2.2.2. And now our model was optimized. Let us verify:" ] }, { @@ -337,13 +1081,27 @@ "metadata": { "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2026-02-10T14:15:05.748413Z", + "start_time": "2026-02-10T14:15:05.745309Z" } }, "source": [ - "print(os.listdir(ARTIFACT_PATH))" + "optimized_model_file = os.path.join(ARTIFACT_PATH, \"onnx-utils-optimize\", \"0\", \"model\", \"optimized_onnx_mobilenetv2.onnx\")\n", + "assert os.path.isfile(optimized_model_file), f\"Optimized ONNX model not found at {optimized_model_file}\"\n", + "print(f\"Optimized ONNX model created at: {optimized_model_file}\")" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimized ONNX model created at: /var/folders/rn/q8gs952n26982d36y50w_2rw0000gp/T/tmpvs5qvbxr/onnx-utils-optimize/0/model/optimized_onnx_mobilenetv2.onnx\n" + ] + } + ], + "execution_count": 7 }, { "cell_type": "markdown", @@ -353,7 +1111,7 @@ } }, "source": [ - "Lastly, run this code to clean up the models:" + "Lastly, run this code to clean up all generated files and directories:" ] }, { @@ -361,23 +1119,22 @@ "metadata": { "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2026-02-10T14:00:28.409998Z", + "start_time": "2026-02-10T13:57:21.679146Z" } }, - "source": [ - "import shutil\n", - "\n", - "\n", - "shutil.rmtree(ARTIFACT_PATH)" - ], + "source": "import shutil\n\n# Clean up the temporary artifact directory:\nif os.path.exists(ARTIFACT_PATH):\n shutil.rmtree(ARTIFACT_PATH)", "outputs": [], "execution_count": null } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "mlrun_functions", "language": "python", - "name": "python3" + "name": "mlrun_functions" }, "language_info": { "codemirror_mode": { @@ -394,4 +1151,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/functions/src/onnx_utils/test_onnx_utils.py b/functions/src/onnx_utils/test_onnx_utils.py index 36113ce1d..08a536128 100644 --- a/functions/src/onnx_utils/test_onnx_utils.py +++ b/functions/src/onnx_utils/test_onnx_utils.py @@ -13,8 +13,6 @@ # limitations under the License. # import os -import shutil -import tempfile import mlrun import pytest @@ -34,6 +32,8 @@ REQUIRED_ENV_VARS = [ "MLRUN_DBPATH", "MLRUN_ARTIFACT_PATH", + "V3IO_USERNAME", + "V3IO_ACCESS_KEY", ] @@ -56,47 +56,6 @@ def _is_tf2onnx_available() -> bool: return False -def _setup_environment() -> str: - """ - Setup the test environment, creating the artifacts path of the test. - - :returns: The temporary directory created for the test artifacts path. - """ - artifact_path = tempfile.TemporaryDirectory().name - os.makedirs(artifact_path) - return artifact_path - - -def _cleanup_environment(artifact_path: str): - """ - Cleanup the test environment, deleting files and artifacts created during the test. - - :param artifact_path: The artifact path to delete. - """ - # Clean the local directory: - for test_output in [ - *os.listdir(artifact_path), - "schedules", - "runs", - "artifacts", - "functions", - "model.pt", - "model.zip", - "model_modules_map.json", - "onnx_model.onnx", - "optimized_onnx_model.onnx", - ]: - test_output_path = os.path.abspath(f"./{test_output}") - if os.path.exists(test_output_path): - if os.path.isdir(test_output_path): - shutil.rmtree(test_output_path) - else: - os.remove(test_output_path) - - # Clean the artifacts directory: - shutil.rmtree(artifact_path) - - def _log_tf_keras_model(context: mlrun.MLClientCtx, model_name: str): """ Create and log a tf.keras model - MobileNetV2. @@ -151,16 +110,11 @@ def _log_pytorch_model(context: mlrun.MLClientCtx, model_name: str): condition=not _validate_environment_variables(), reason="Project's environment variables are not set", ) -@pytest.mark.skipif( - condition=not _is_tf2onnx_available(), - reason="tf2onnx is not installed", -) -def test_to_onnx_help(): +def test_to_onnx_help(test_environment): """ Test the 'to_onnx' handler, passing "help" in the 'framework_kwargs'. """ - # Setup the tests environment: - artifact_path = _setup_environment() + artifact_path = test_environment # Create the function: log_model_function = mlrun.code_to_function( @@ -172,13 +126,18 @@ def test_to_onnx_help(): ) # Run the function to log the model: - log_model_run = log_model_function.run( - handler="_log_tf_keras_model", + log_model_function.run( + handler="_log_pytorch_model", output_path=artifact_path, params={"model_name": MODEL_NAME}, local=True, ) + # Get artifact paths - construct from artifact_path and run structure + run_artifact_dir = os.path.join(artifact_path, "log-model--log-pytorch-model", "0") + model_path = os.path.join(run_artifact_dir, "model") + modules_map_path = os.path.join(run_artifact_dir, "model_modules_map.json.json") + # Import the ONNX Utils function: onnx_function = mlrun.import_function("function.yaml", project=PROJECT_NAME) @@ -190,8 +149,12 @@ def test_to_onnx_help(): output_path=artifact_path, params={ # Take the logged model from the previous function. - "model_path": log_model_run.status.artifacts[0]["spec"]["target_path"], - "load_model_kwargs": {"model_name": MODEL_NAME}, + "model_path": model_path, + "load_model_kwargs": { + "model_name": MODEL_NAME, + "model_class": "mobilenet_v2", + "modules_map": modules_map_path, + }, "framework_kwargs": "help", }, local=True, @@ -202,9 +165,6 @@ def test_to_onnx_help(): ) is_test_passed = False - # Cleanup the tests environment: - _cleanup_environment(artifact_path=artifact_path) - assert is_test_passed @@ -216,12 +176,11 @@ def test_to_onnx_help(): condition=not _is_tf2onnx_available(), reason="tf2onnx is not installed", ) -def test_tf_keras_to_onnx(): +def test_tf_keras_to_onnx(test_environment): """ Test the 'to_onnx' handler, giving it a tf.keras model. """ - # Setup the tests environment: - artifact_path = _setup_environment() + artifact_path = test_environment # Create the function: log_model_function = mlrun.code_to_function( @@ -256,9 +215,6 @@ def test_tf_keras_to_onnx(): local=True, ) - # Cleanup the tests environment: - _cleanup_environment(artifact_path=artifact_path) - # Print the outputs list: print(f"Produced outputs: {onnx_function_run.outputs}") @@ -270,12 +226,11 @@ def test_tf_keras_to_onnx(): condition=not _validate_environment_variables(), reason="Project's environment variables are not set", ) -def test_pytorch_to_onnx(): +def test_pytorch_to_onnx(test_environment): """ Test the 'to_onnx' handler, giving it a pytorch model. """ - # Setup the tests environment: - artifact_path = _setup_environment() + artifact_path = test_environment # Create the function: log_model_function = mlrun.code_to_function( @@ -320,9 +275,6 @@ def test_pytorch_to_onnx(): local=True, ) - # Cleanup the tests environment: - _cleanup_environment(artifact_path=artifact_path) - # Print the outputs list: print(f"Produced outputs: {onnx_function_run.outputs}") @@ -334,12 +286,11 @@ def test_pytorch_to_onnx(): condition=not _validate_environment_variables(), reason="Project's environment variables are not set", ) -def test_optimize_help(): +def test_optimize_help(test_environment): """ Test the 'optimize' handler, passing "help" in the 'optimizations'. """ - # Setup the tests environment: - artifact_path = _setup_environment() + artifact_path = test_environment # Import the ONNX Utils function: onnx_function = mlrun.import_function("function.yaml", project=PROJECT_NAME) @@ -362,9 +313,6 @@ def test_optimize_help(): ) is_test_passed = False - # Cleanup the tests environment: - _cleanup_environment(artifact_path=artifact_path) - assert is_test_passed @@ -372,16 +320,11 @@ def test_optimize_help(): condition=not _validate_environment_variables(), reason="Project's environment variables are not set", ) -@pytest.mark.skipif( - condition=not _is_tf2onnx_available(), - reason="tf2onnx is not installed", -) -def test_optimize(): +def test_optimize(test_environment): """ - Test the 'optimize' handler, giving it a model from the ONNX zoo git repository. + Test the 'optimize' handler, giving it a pytorch model converted to ONNX. """ - # Setup the tests environment: - artifact_path = _setup_environment() + artifact_path = test_environment # Create the function: log_model_function = mlrun.code_to_function( @@ -393,47 +336,58 @@ def test_optimize(): ) # Run the function to log the model: - log_model_run = log_model_function.run( - handler="_log_tf_keras_model", + log_model_function.run( + handler="_log_pytorch_model", output_path=artifact_path, params={"model_name": MODEL_NAME}, local=True, ) + # Get artifact paths - construct from artifact_path and run structure + run_artifact_dir = os.path.join(artifact_path, "log-model--log-pytorch-model", "0") + model_path = os.path.join(run_artifact_dir, "model") + modules_map_path = os.path.join(run_artifact_dir, "model_modules_map.json.json") + # Import the ONNX Utils function: onnx_function = mlrun.import_function("function.yaml", project=PROJECT_NAME) # Run the function to convert our model to ONNX: - to_onnx_function_run = onnx_function.run( + onnx_function.run( handler="to_onnx", output_path=artifact_path, params={ # Take the logged model from the previous function. - "model_path": log_model_run.status.artifacts[0]["spec"]["target_path"], - "load_model_kwargs": {"model_name": MODEL_NAME}, + "model_path": model_path, + "load_model_kwargs": { + "model_name": MODEL_NAME, + "model_class": "mobilenet_v2", + "modules_map": modules_map_path, + }, "onnx_model_name": ONNX_MODEL_NAME, + "framework_kwargs": {"input_signature": [((32, 3, 224, 224), "float32")]}, }, local=True, ) + # Get the ONNX model path from the to_onnx run output + onnx_run_artifact_dir = os.path.join( + artifact_path, "onnx-utils-to-onnx", "0" + ) + onnx_model_path = os.path.join(onnx_run_artifact_dir, "model") + # Run the function to optimize our model: optimize_function_run = onnx_function.run( handler="optimize", output_path=artifact_path, params={ # Take the logged model from the previous function. - "model_path": to_onnx_function_run.status.artifacts[0]["spec"][ - "target_path" - ], + "model_path": onnx_model_path, "handler_init_kwargs": {"model_name": ONNX_MODEL_NAME}, "optimized_model_name": OPTIMIZED_ONNX_MODEL_NAME, }, local=True, ) - # Cleanup the tests environment: - _cleanup_environment(artifact_path=artifact_path) - # Print the outputs list: print(f"Produced outputs: {optimize_function_run.outputs}") From 3de423d26fabeaac1ac5d4b6717010575732992f Mon Sep 17 00:00:00 2001 From: Omer_Mimon Date: Tue, 10 Feb 2026 16:21:57 +0200 Subject: [PATCH 3/4] deleted iguazio credentials --- functions/src/onnx_utils/onnx_utils.ipynb | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/functions/src/onnx_utils/onnx_utils.ipynb b/functions/src/onnx_utils/onnx_utils.ipynb index c0aaa3547..14c810fab 100644 --- a/functions/src/onnx_utils/onnx_utils.ipynb +++ b/functions/src/onnx_utils/onnx_utils.ipynb @@ -93,7 +93,25 @@ "start_time": "2026-02-10T14:13:28.250886Z" } }, - "source": "import os\nimport tempfile\n\n# Set environment variables BEFORE importing mlrun:\nos.environ[\"MLRUN_DBPATH\"] = \"https://mlrun-api.default-tenant.app.innovation-dev.iguazio-cd2.com\"\nos.environ[\"V3IO_USERNAME\"] = \"omerm\"\nos.environ[\"V3IO_ACCESS_KEY\"] = \"618ed0ad-6b68-4be8-8785-b1124437eae2\"\n\n# Use a temporary directory for model artifacts (safe cleanup):\nARTIFACT_PATH = tempfile.mkdtemp()\nos.environ[\"MLRUN_ARTIFACT_PATH\"] = ARTIFACT_PATH\n\n# Project name:\nPROJECT_NAME = \"onnx-utils\"\n\n# Choose our model's name:\nMODEL_NAME = \"mobilenetv2\"\n\n# Choose our ONNX version model's name:\nONNX_MODEL_NAME = \"onnx_mobilenetv2\"\n\n# Choose our optimized ONNX version model's name:\nOPTIMIZED_ONNX_MODEL_NAME = \"optimized_onnx_mobilenetv2\"", + "source": [ + "import os\n", + "import tempfile\n", + "# Use a temporary directory for model artifacts (safe cleanup):\n", + "ARTIFACT_PATH = tempfile.mkdtemp()\n", + "os.environ[\"MLRUN_ARTIFACT_PATH\"] = ARTIFACT_PATH\n", + "\n", + "# Project name:\n", + "PROJECT_NAME = \"onnx-utils\"\n", + "\n", + "# Choose our model's name:\n", + "MODEL_NAME = \"mobilenetv2\"\n", + "\n", + "# Choose our ONNX version model's name:\n", + "ONNX_MODEL_NAME = \"onnx_mobilenetv2\"\n", + "\n", + "# Choose our optimized ONNX version model's name:\n", + "OPTIMIZED_ONNX_MODEL_NAME = \"optimized_onnx_mobilenetv2\"" + ], "outputs": [], "execution_count": 1 }, @@ -1151,4 +1169,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From 29ff211962e8d811d4df377593a759c0f6fb9677 Mon Sep 17 00:00:00 2001 From: Omer_Mimon Date: Wed, 11 Feb 2026 11:21:08 +0200 Subject: [PATCH 4/4] Remove conftest.py and inline fixtures into test_onnx_utils.py Move onnx_project and test_environment fixtures directly into the test file to reduce unnecessary indirection for a single test module. --- functions/src/onnx_utils/conftest.py | 58 --------------------- functions/src/onnx_utils/test_onnx_utils.py | 39 +++++++++++++- 2 files changed, 38 insertions(+), 59 deletions(-) delete mode 100644 functions/src/onnx_utils/conftest.py diff --git a/functions/src/onnx_utils/conftest.py b/functions/src/onnx_utils/conftest.py deleted file mode 100644 index 00740e02e..000000000 --- a/functions/src/onnx_utils/conftest.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright 2019 Iguazio -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -import shutil -import tempfile - -import mlrun -import pytest - -PROJECT_NAME = "onnx-utils" - - -@pytest.fixture(scope="session") -def onnx_project(): - """Create/get the MLRun project once per test session.""" - return mlrun.get_or_create_project(PROJECT_NAME, context="./") - - -@pytest.fixture(autouse=True) -def test_environment(onnx_project): - """Setup and cleanup test artifacts for each test.""" - artifact_path = tempfile.mkdtemp() - yield artifact_path - # Cleanup - only remove files/dirs from the directory containing this test file, - # never from an arbitrary CWD (which could be the project root). - test_dir = os.path.dirname(os.path.abspath(__file__)) - for test_output in [ - "schedules", - "runs", - "artifacts", - "functions", - "model.pt", - "model.zip", - "model_modules_map.json", - "model_modules_map.json.json", - "onnx_model.onnx", - "optimized_onnx_model.onnx", - ]: - test_output_path = os.path.join(test_dir, test_output) - if os.path.exists(test_output_path): - if os.path.isdir(test_output_path): - shutil.rmtree(test_output_path) - else: - os.remove(test_output_path) - if os.path.exists(artifact_path): - shutil.rmtree(artifact_path) diff --git a/functions/src/onnx_utils/test_onnx_utils.py b/functions/src/onnx_utils/test_onnx_utils.py index 08a536128..59c6c2b38 100644 --- a/functions/src/onnx_utils/test_onnx_utils.py +++ b/functions/src/onnx_utils/test_onnx_utils.py @@ -13,11 +13,12 @@ # limitations under the License. # import os +import shutil +import tempfile import mlrun import pytest -# Project name for tests (must match conftest.py) PROJECT_NAME = "onnx-utils" # Choose our model's name: @@ -56,6 +57,42 @@ def _is_tf2onnx_available() -> bool: return False +@pytest.fixture(scope="session") +def onnx_project(): + """Create/get the MLRun project once per test session.""" + return mlrun.get_or_create_project(PROJECT_NAME, context="./") + + +@pytest.fixture(autouse=True) +def test_environment(onnx_project): + """Setup and cleanup test artifacts for each test.""" + artifact_path = tempfile.mkdtemp() + yield artifact_path + # Cleanup - only remove files/dirs from the directory containing this test file, + # never from an arbitrary CWD (which could be the project root). + test_dir = os.path.dirname(os.path.abspath(__file__)) + for test_output in [ + "schedules", + "runs", + "artifacts", + "functions", + "model.pt", + "model.zip", + "model_modules_map.json", + "model_modules_map.json.json", + "onnx_model.onnx", + "optimized_onnx_model.onnx", + ]: + test_output_path = os.path.join(test_dir, test_output) + if os.path.exists(test_output_path): + if os.path.isdir(test_output_path): + shutil.rmtree(test_output_path) + else: + os.remove(test_output_path) + if os.path.exists(artifact_path): + shutil.rmtree(artifact_path) + + def _log_tf_keras_model(context: mlrun.MLClientCtx, model_name: str): """ Create and log a tf.keras model - MobileNetV2.