path: root/python-sagemaker-inference.spec

%global _empty_manifest_terminate_build 0
Name:		python-sagemaker-inference
Version:	1.9.2
Release:	1
Summary:	Open source toolkit for helping create serving containers to run on Amazon SageMaker.
License:	Apache License 2.0
URL:		https://github.com/aws/sagemaker-inference-toolkit/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/af/02/63dbc51c108c709bd3b277bc74527214abfceb858f5c20f6edbb1ba7e6e6/sagemaker_inference-1.9.2.tar.gz
BuildArch:	noarch


%description
![SageMaker](https://github.com/aws/sagemaker-inference-toolkit/raw/master/branding/icon/sagemaker-banner.png)

# SageMaker Inference Toolkit

[![Latest Version](https://img.shields.io/pypi/v/sagemaker-inference.svg)](https://pypi.python.org/pypi/sagemaker-inference) [![Supported Python Versions](https://img.shields.io/pypi/pyversions/sagemaker-inference.svg)](https://pypi.python.org/pypi/sagemaker-inference) [![Code Style: Black](https://img.shields.io/badge/code_style-black-000000.svg)](https://github.com/python/black)

Serve machine learning models within a Docker container using Amazon
SageMaker.

## :books: Background

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service for data science and machine learning (ML) workflows.
You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.

Once you have a trained model, you can include it in a [Docker container](https://www.docker.com/resources/what-container) that runs your inference code.
A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed.
Containerizing your model and code enables fast and reliable deployment of your model.

The **SageMaker Inference Toolkit** implements a model serving stack and can be easily added to any Docker container, making it [deployable to SageMaker](https://aws.amazon.com/sagemaker/deploy/).
This library's serving stack is built on [Multi Model Server](https://github.com/awslabs/multi-model-server), and it can serve your own models or those you trained on SageMaker using [machine learning frameworks with native SageMaker support](https://docs.aws.amazon.com/sagemaker/latest/dg/frameworks.html).
If you use a [prebuilt SageMaker Docker image for inference](https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-containers-frameworks-deep-learning.html), this library may already be included.

For more information, see the Amazon SageMaker Developer Guide sections on [building your own container with Multi Model Server](https://docs.aws.amazon.com/sagemaker/latest/dg/build-multi-model-build-container.html) and [using your own models](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).

## :hammer_and_wrench: Installation

To install this library in your Docker image, add the following line to your [Dockerfile](https://docs.docker.com/engine/reference/builder/):

``` dockerfile
RUN pip3 install multi-model-server sagemaker-inference
```

[Here is an example](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/multi_model_bring_your_own/container/Dockerfile) of a Dockerfile that installs SageMaker Inference Toolkit.

## :computer: Usage

### Implementation Steps

To use the SageMaker Inference Toolkit, you need to do the following:

1.  Implement an inference handler, which is responsible for loading the model and providing input, predict, and output functions.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py) of an inference handler.)

    ``` python
    from sagemaker_inference import content_types, decoder, default_inference_handler, encoder, errors

    class DefaultPytorchInferenceHandler(default_inference_handler.DefaultInferenceHandler):

        def default_model_fn(self, model_dir, context=None):
            """Loads a model. For PyTorch, a default function to load a model cannot be provided.
            Users should provide customized model_fn() in script.

            Args:
                model_dir: a directory where model is saved.
                context (obj): the request context (default: None).

            Returns: A PyTorch model.
            """
            raise NotImplementedError(textwrap.dedent("""
            Please provide a model_fn implementation.
            See documentation for model_fn at https://github.com/aws/sagemaker-python-sdk
            """))

        def default_input_fn(self, input_data, content_type, context=None):
            """A default input_fn that can handle JSON, CSV and NPZ formats.

            Args:
                input_data: the request payload serialized in the content_type format
                content_type: the request content_type
                context (obj): the request context (default: None).

            Returns: input_data deserialized into torch.FloatTensor or torch.cuda.FloatTensor depending if cuda is available.
            """
            return decoder.decode(input_data, content_type)

        def default_predict_fn(self, data, model, context=None):
            """A default predict_fn for PyTorch. Calls a model on data deserialized in input_fn.
            Runs prediction on GPU if cuda is available.

            Args:
                data: input data (torch.Tensor) for prediction deserialized by input_fn
                model: PyTorch model loaded in memory by model_fn
                context (obj): the request context (default: None).

            Returns: a prediction
            """
            return model(input_data)

        def default_output_fn(self, prediction, accept, context=None):
            """A default output_fn for PyTorch. Serializes predictions from predict_fn to JSON, CSV or NPY format.

            Args:
                prediction: a prediction result from predict_fn
                accept: type which the output data needs to be serialized
                context (obj): the request context (default: None).

            Returns: output data serialized
            """
            return encoder.encode(prediction, accept)
    ```
    Note, passing context as an argument to the handler functions is optional. Customer can choose to omit context from the function declaration if it's not needed in the runtime. For example, the following handler function declarations will also work:

    ```
    def default_model_fn(self, model_dir)

    def default_input_fn(self, input_data, content_type)

    def default_predict_fn(self, data, model)

    def default_output_fn(self, prediction, accept)
    ``` 

2.  Implement a handler service that is executed by the model server.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/handler_service.py) of a handler service.)
    For more information on how to define your `HANDLER_SERVICE` file, see [the MMS custom service documentation](https://github.com/awslabs/multi-model-server/blob/master/docs/custom_service.md).

    ``` python
    from sagemaker_inference.default_handler_service import DefaultHandlerService
    from sagemaker_inference.transformer import Transformer
    from sagemaker_pytorch_serving_container.default_inference_handler import DefaultPytorchInferenceHandler


    class HandlerService(DefaultHandlerService):
        """Handler service that is executed by the model server.
        Determines specific default inference handlers to use based on model being used.
        This class extends ``DefaultHandlerService``, which define the following:
            - The ``handle`` method is invoked for all incoming inference requests to the model server.
            - The ``initialize`` method is invoked at model server start up.
        Based on: https://github.com/awslabs/multi-model-server/blob/master/docs/custom_service.md
        """
        def __init__(self):
            transformer = Transformer(default_inference_handler=DefaultPytorchInferenceHandler())
            super(HandlerService, self).__init__(transformer=transformer)
    ```

3.  Implement a serving entrypoint, which starts the model server.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/serving.py) of a serving entrypoint.)

    ``` python
    from sagemaker_inference import model_server

    model_server.start_model_server(handler_service=HANDLER_SERVICE)
    ```

4.  Define the location of the entrypoint in your Dockerfile.

    ``` dockerfile
    ENTRYPOINT ["python", "/usr/local/bin/entrypoint.py"]
    ```

### Complete Example

[Here is a complete example](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/multi_model_bring_your_own) demonstrating usage of the SageMaker Inference Toolkit in your own container for deployment to a multi-model endpoint.

## :scroll: License

This library is licensed under the [Apache 2.0 License](http://aws.amazon.com/apache2.0/).
For more details, please take a look at the [LICENSE](https://github.com/aws-samples/sagemaker-inference-toolkit/blob/master/LICENSE) file.

## :handshake: Contributing

Contributions are welcome!
Please read our [contributing guidelines](https://github.com/aws/sagemaker-inference-toolkit/blob/master/CONTRIBUTING.md)
if you'd like to open an issue or submit a pull request.

%package -n python3-sagemaker-inference
Summary:	Open source toolkit for helping create serving containers to run on Amazon SageMaker.
Provides:	python-sagemaker-inference
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-sagemaker-inference
![SageMaker](https://github.com/aws/sagemaker-inference-toolkit/raw/master/branding/icon/sagemaker-banner.png)

# SageMaker Inference Toolkit

[![Latest Version](https://img.shields.io/pypi/v/sagemaker-inference.svg)](https://pypi.python.org/pypi/sagemaker-inference) [![Supported Python Versions](https://img.shields.io/pypi/pyversions/sagemaker-inference.svg)](https://pypi.python.org/pypi/sagemaker-inference) [![Code Style: Black](https://img.shields.io/badge/code_style-black-000000.svg)](https://github.com/python/black)

Serve machine learning models within a Docker container using Amazon
SageMaker.

## :books: Background

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service for data science and machine learning (ML) workflows.
You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.

Once you have a trained model, you can include it in a [Docker container](https://www.docker.com/resources/what-container) that runs your inference code.
A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed.
Containerizing your model and code enables fast and reliable deployment of your model.

The **SageMaker Inference Toolkit** implements a model serving stack and can be easily added to any Docker container, making it [deployable to SageMaker](https://aws.amazon.com/sagemaker/deploy/).
This library's serving stack is built on [Multi Model Server](https://github.com/awslabs/multi-model-server), and it can serve your own models or those you trained on SageMaker using [machine learning frameworks with native SageMaker support](https://docs.aws.amazon.com/sagemaker/latest/dg/frameworks.html).
If you use a [prebuilt SageMaker Docker image for inference](https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-containers-frameworks-deep-learning.html), this library may already be included.

For more information, see the Amazon SageMaker Developer Guide sections on [building your own container with Multi Model Server](https://docs.aws.amazon.com/sagemaker/latest/dg/build-multi-model-build-container.html) and [using your own models](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).

## :hammer_and_wrench: Installation

To install this library in your Docker image, add the following line to your [Dockerfile](https://docs.docker.com/engine/reference/builder/):

``` dockerfile
RUN pip3 install multi-model-server sagemaker-inference
```

[Here is an example](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/multi_model_bring_your_own/container/Dockerfile) of a Dockerfile that installs SageMaker Inference Toolkit.

## :computer: Usage

### Implementation Steps

To use the SageMaker Inference Toolkit, you need to do the following:

1.  Implement an inference handler, which is responsible for loading the model and providing input, predict, and output functions.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py) of an inference handler.)

    ``` python
    from sagemaker_inference import content_types, decoder, default_inference_handler, encoder, errors

    class DefaultPytorchInferenceHandler(default_inference_handler.DefaultInferenceHandler):

        def default_model_fn(self, model_dir, context=None):
            """Loads a model. For PyTorch, a default function to load a model cannot be provided.
            Users should provide customized model_fn() in script.

            Args:
                model_dir: a directory where model is saved.
                context (obj): the request context (default: None).

            Returns: A PyTorch model.
            """
            raise NotImplementedError(textwrap.dedent("""
            Please provide a model_fn implementation.
            See documentation for model_fn at https://github.com/aws/sagemaker-python-sdk
            """))

        def default_input_fn(self, input_data, content_type, context=None):
            """A default input_fn that can handle JSON, CSV and NPZ formats.

            Args:
                input_data: the request payload serialized in the content_type format
                content_type: the request content_type
                context (obj): the request context (default: None).

            Returns: input_data deserialized into torch.FloatTensor or torch.cuda.FloatTensor depending if cuda is available.
            """
            return decoder.decode(input_data, content_type)

        def default_predict_fn(self, data, model, context=None):
            """A default predict_fn for PyTorch. Calls a model on data deserialized in input_fn.
            Runs prediction on GPU if cuda is available.

            Args:
                data: input data (torch.Tensor) for prediction deserialized by input_fn
                model: PyTorch model loaded in memory by model_fn
                context (obj): the request context (default: None).

            Returns: a prediction
            """
            return model(input_data)

        def default_output_fn(self, prediction, accept, context=None):
            """A default output_fn for PyTorch. Serializes predictions from predict_fn to JSON, CSV or NPY format.

            Args:
                prediction: a prediction result from predict_fn
                accept: type which the output data needs to be serialized
                context (obj): the request context (default: None).

            Returns: output data serialized
            """
            return encoder.encode(prediction, accept)
    ```
    Note, passing context as an argument to the handler functions is optional. Customer can choose to omit context from the function declaration if it's not needed in the runtime. For example, the following handler function declarations will also work:

    ```
    def default_model_fn(self, model_dir)

    def default_input_fn(self, input_data, content_type)

    def default_predict_fn(self, data, model)

    def default_output_fn(self, prediction, accept)
    ``` 

2.  Implement a handler service that is executed by the model server.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/handler_service.py) of a handler service.)
    For more information on how to define your `HANDLER_SERVICE` file, see [the MMS custom service documentation](https://github.com/awslabs/multi-model-server/blob/master/docs/custom_service.md).

    ``` python
    from sagemaker_inference.default_handler_service import DefaultHandlerService
    from sagemaker_inference.transformer import Transformer
    from sagemaker_pytorch_serving_container.default_inference_handler import DefaultPytorchInferenceHandler


    class HandlerService(DefaultHandlerService):
        """Handler service that is executed by the model server.
        Determines specific default inference handlers to use based on model being used.
        This class extends ``DefaultHandlerService``, which define the following:
            - The ``handle`` method is invoked for all incoming inference requests to the model server.
            - The ``initialize`` method is invoked at model server start up.
        Based on: https://github.com/awslabs/multi-model-server/blob/master/docs/custom_service.md
        """
        def __init__(self):
            transformer = Transformer(default_inference_handler=DefaultPytorchInferenceHandler())
            super(HandlerService, self).__init__(transformer=transformer)
    ```

3.  Implement a serving entrypoint, which starts the model server.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/serving.py) of a serving entrypoint.)

    ``` python
    from sagemaker_inference import model_server

    model_server.start_model_server(handler_service=HANDLER_SERVICE)
    ```

4.  Define the location of the entrypoint in your Dockerfile.

    ``` dockerfile
    ENTRYPOINT ["python", "/usr/local/bin/entrypoint.py"]
    ```

### Complete Example

[Here is a complete example](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/multi_model_bring_your_own) demonstrating usage of the SageMaker Inference Toolkit in your own container for deployment to a multi-model endpoint.

## :scroll: License

This library is licensed under the [Apache 2.0 License](http://aws.amazon.com/apache2.0/).
For more details, please take a look at the [LICENSE](https://github.com/aws-samples/sagemaker-inference-toolkit/blob/master/LICENSE) file.

## :handshake: Contributing

Contributions are welcome!
Please read our [contributing guidelines](https://github.com/aws/sagemaker-inference-toolkit/blob/master/CONTRIBUTING.md)
if you'd like to open an issue or submit a pull request.

%package help
Summary:	Development documents and examples for sagemaker-inference
Provides:	python3-sagemaker-inference-doc
%description help
![SageMaker](https://github.com/aws/sagemaker-inference-toolkit/raw/master/branding/icon/sagemaker-banner.png)

# SageMaker Inference Toolkit

[![Latest Version](https://img.shields.io/pypi/v/sagemaker-inference.svg)](https://pypi.python.org/pypi/sagemaker-inference) [![Supported Python Versions](https://img.shields.io/pypi/pyversions/sagemaker-inference.svg)](https://pypi.python.org/pypi/sagemaker-inference) [![Code Style: Black](https://img.shields.io/badge/code_style-black-000000.svg)](https://github.com/python/black)

Serve machine learning models within a Docker container using Amazon
SageMaker.

## :books: Background

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service for data science and machine learning (ML) workflows.
You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.

Once you have a trained model, you can include it in a [Docker container](https://www.docker.com/resources/what-container) that runs your inference code.
A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed.
Containerizing your model and code enables fast and reliable deployment of your model.

The **SageMaker Inference Toolkit** implements a model serving stack and can be easily added to any Docker container, making it [deployable to SageMaker](https://aws.amazon.com/sagemaker/deploy/).
This library's serving stack is built on [Multi Model Server](https://github.com/awslabs/multi-model-server), and it can serve your own models or those you trained on SageMaker using [machine learning frameworks with native SageMaker support](https://docs.aws.amazon.com/sagemaker/latest/dg/frameworks.html).
If you use a [prebuilt SageMaker Docker image for inference](https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-containers-frameworks-deep-learning.html), this library may already be included.

For more information, see the Amazon SageMaker Developer Guide sections on [building your own container with Multi Model Server](https://docs.aws.amazon.com/sagemaker/latest/dg/build-multi-model-build-container.html) and [using your own models](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).

## :hammer_and_wrench: Installation

To install this library in your Docker image, add the following line to your [Dockerfile](https://docs.docker.com/engine/reference/builder/):

``` dockerfile
RUN pip3 install multi-model-server sagemaker-inference
```

[Here is an example](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/multi_model_bring_your_own/container/Dockerfile) of a Dockerfile that installs SageMaker Inference Toolkit.

## :computer: Usage

### Implementation Steps

To use the SageMaker Inference Toolkit, you need to do the following:

1.  Implement an inference handler, which is responsible for loading the model and providing input, predict, and output functions.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py) of an inference handler.)

    ``` python
    from sagemaker_inference import content_types, decoder, default_inference_handler, encoder, errors

    class DefaultPytorchInferenceHandler(default_inference_handler.DefaultInferenceHandler):

        def default_model_fn(self, model_dir, context=None):
            """Loads a model. For PyTorch, a default function to load a model cannot be provided.
            Users should provide customized model_fn() in script.

            Args:
                model_dir: a directory where model is saved.
                context (obj): the request context (default: None).

            Returns: A PyTorch model.
            """
            raise NotImplementedError(textwrap.dedent("""
            Please provide a model_fn implementation.
            See documentation for model_fn at https://github.com/aws/sagemaker-python-sdk
            """))

        def default_input_fn(self, input_data, content_type, context=None):
            """A default input_fn that can handle JSON, CSV and NPZ formats.

            Args:
                input_data: the request payload serialized in the content_type format
                content_type: the request content_type
                context (obj): the request context (default: None).

            Returns: input_data deserialized into torch.FloatTensor or torch.cuda.FloatTensor depending if cuda is available.
            """
            return decoder.decode(input_data, content_type)

        def default_predict_fn(self, data, model, context=None):
            """A default predict_fn for PyTorch. Calls a model on data deserialized in input_fn.
            Runs prediction on GPU if cuda is available.

            Args:
                data: input data (torch.Tensor) for prediction deserialized by input_fn
                model: PyTorch model loaded in memory by model_fn
                context (obj): the request context (default: None).

            Returns: a prediction
            """
            return model(input_data)

        def default_output_fn(self, prediction, accept, context=None):
            """A default output_fn for PyTorch. Serializes predictions from predict_fn to JSON, CSV or NPY format.

            Args:
                prediction: a prediction result from predict_fn
                accept: type which the output data needs to be serialized
                context (obj): the request context (default: None).

            Returns: output data serialized
            """
            return encoder.encode(prediction, accept)
    ```
    Note, passing context as an argument to the handler functions is optional. Customer can choose to omit context from the function declaration if it's not needed in the runtime. For example, the following handler function declarations will also work:

    ```
    def default_model_fn(self, model_dir)

    def default_input_fn(self, input_data, content_type)

    def default_predict_fn(self, data, model)

    def default_output_fn(self, prediction, accept)
    ``` 

2.  Implement a handler service that is executed by the model server.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/handler_service.py) of a handler service.)
    For more information on how to define your `HANDLER_SERVICE` file, see [the MMS custom service documentation](https://github.com/awslabs/multi-model-server/blob/master/docs/custom_service.md).

    ``` python
    from sagemaker_inference.default_handler_service import DefaultHandlerService
    from sagemaker_inference.transformer import Transformer
    from sagemaker_pytorch_serving_container.default_inference_handler import DefaultPytorchInferenceHandler


    class HandlerService(DefaultHandlerService):
        """Handler service that is executed by the model server.
        Determines specific default inference handlers to use based on model being used.
        This class extends ``DefaultHandlerService``, which define the following:
            - The ``handle`` method is invoked for all incoming inference requests to the model server.
            - The ``initialize`` method is invoked at model server start up.
        Based on: https://github.com/awslabs/multi-model-server/blob/master/docs/custom_service.md
        """
        def __init__(self):
            transformer = Transformer(default_inference_handler=DefaultPytorchInferenceHandler())
            super(HandlerService, self).__init__(transformer=transformer)
    ```

3.  Implement a serving entrypoint, which starts the model server.
    ([Here is an example](https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/serving.py) of a serving entrypoint.)

    ``` python
    from sagemaker_inference import model_server

    model_server.start_model_server(handler_service=HANDLER_SERVICE)
    ```

4.  Define the location of the entrypoint in your Dockerfile.

    ``` dockerfile
    ENTRYPOINT ["python", "/usr/local/bin/entrypoint.py"]
    ```

### Complete Example

[Here is a complete example](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/multi_model_bring_your_own) demonstrating usage of the SageMaker Inference Toolkit in your own container for deployment to a multi-model endpoint.

## :scroll: License

This library is licensed under the [Apache 2.0 License](http://aws.amazon.com/apache2.0/).
For more details, please take a look at the [LICENSE](https://github.com/aws-samples/sagemaker-inference-toolkit/blob/master/LICENSE) file.

## :handshake: Contributing

Contributions are welcome!
Please read our [contributing guidelines](https://github.com/aws/sagemaker-inference-toolkit/blob/master/CONTRIBUTING.md)
if you'd like to open an issue or submit a pull request.

%prep
%autosetup -n sagemaker-inference-1.9.2

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-sagemaker-inference -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 1.9.2-1
- Package Spec generated