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+%global _empty_manifest_terminate_build 0
+Name:		python-sagemaker-training
+Version:	4.4.10
+Release:	1
+Summary:	Open source library for creating containers to run on Amazon SageMaker.
+License:	Apache License 2.0
+URL:		https://github.com/aws/sagemaker-training-toolkit/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6f/fa/0496339dc1a1f0ca0e7314efc2034cfd51a840c727fe5e4979ad4fcf4c3a/sagemaker_training-4.4.10.tar.gz
+BuildArch:	noarch
+
+
+%description
+![SageMaker](https://github.com/aws/sagemaker-training-toolkit/raw/master/branding/icon/sagemaker-banner.png)
+
+# SageMaker Training Toolkit
+
+[![Latest Version](https://img.shields.io/pypi/v/sagemaker-training.svg)](https://pypi.python.org/pypi/sagemaker-training) [![Supported Python Versions](https://img.shields.io/pypi/pyversions/sagemaker-training.svg)](https://pypi.python.org/pypi/sagemaker-training) [![Code Style: Black](https://img.shields.io/badge/code_style-black-000000.svg)](https://github.com/python/black)
+
+Train 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.
+
+To train a model, you can include your training script and dependencies in a [Docker container](https://www.docker.com/resources/what-container) that runs your training code.
+A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. 
+
+The **SageMaker Training Toolkit** can be easily added to any Docker container, making it compatible with SageMaker for [training models](https://aws.amazon.com/sagemaker/train/).
+If you use a [prebuilt SageMaker Docker image for training](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 [using Docker containers for training](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 sagemaker-training
+```
+
+## :computer: Usage
+
+The following are brief how-to guides.
+For complete, working examples of custom training containers built with the SageMaker Training Toolkit, please see [the example notebooks](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/custom-training-containers).
+
+### Create a Docker image and train a model
+
+1. Write a training script (eg. `train.py`).
+
+2. [Define a container with a Dockerfile](https://docs.docker.com/get-started/part2/#define-a-container-with-dockerfile) that includes the training script and any dependencies.
+
+    The training script must be located in the `/opt/ml/code` directory.
+    The environment variable `SAGEMAKER_PROGRAM` defines which file inside the `/opt/ml/code` directory to use as the training entry point.
+    When training starts, the interpreter executes the entry point defined by `SAGEMAKER_PROGRAM`.
+    Python and shell scripts are both supported.
+    
+    ``` docker
+    FROM yourbaseimage:tag
+  
+    # install the SageMaker Training Toolkit 
+    RUN pip3 install sagemaker-training
+
+    # copy the training script inside the container
+    COPY train.py /opt/ml/code/train.py
+
+    # define train.py as the script entry point
+    ENV SAGEMAKER_PROGRAM train.py
+    ```
+
+3. Build and tag the Docker image.
+
+    ``` shell
+    docker build -t custom-training-container .
+    ```
+
+4. Use the Docker image to start a training job using the [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk).
+
+    ``` python
+    from sagemaker.estimator import Estimator
+
+    estimator = Estimator(image_name="custom-training-container",
+                          role="SageMakerRole",
+                          train_instance_count=1,
+                          train_instance_type="local")
+
+    estimator.fit()
+    ```
+    
+    To train a model using the image on SageMaker, [push the image to ECR](https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-push-ecr-image.html) and start a SageMaker training job with the image URI.
+    
+
+### Pass arguments to the entry point using hyperparameters
+
+Any hyperparameters provided by the training job are passed to the entry point as script arguments.
+The SageMaker Python SDK uses this feature to pass special hyperparameters to the training job, including `sagemaker_program` and `sagemaker_submit_directory`.
+The complete list of SageMaker hyperparameters is available [here](https://github.com/aws/sagemaker-training-toolkit/blob/master/src/sagemaker_training/params.py).
+
+1. Implement an argument parser in the entry point script. For example, in a Python script:
+
+    ``` python
+    import argparse
+
+    if __name__ == "__main__":
+      parser = argparse.ArgumentParser()
+
+      parser.add_argument("--learning-rate", type=int, default=1)
+      parser.add_argument("--batch-size", type=int, default=64)
+      parser.add_argument("--communicator", type=str)
+      parser.add_argument("--frequency", type=int, default=20)
+
+      args = parser.parse_args()
+      ...
+    ```
+
+2. Start a training job with hyperparameters.
+
+    ``` python
+    {"HyperParameters": {"batch-size": 256, "learning-rate": 0.0001, "communicator": "pure_nccl"}}
+    ```
+
+### Read additional information using environment variables
+
+An entry point often needs additional information not available in `hyperparameters`.
+The SageMaker Training Toolkit writes this information as environment variables that are available from within the script.
+For example, this training job includes the channels `training` and `testing`:
+
+``` python
+from sagemaker.pytorch import PyTorch
+
+estimator = PyTorch(entry_point="train.py", ...)
+
+estimator.fit({"training": "s3://bucket/path/to/training/data", 
+               "testing": "s3://bucket/path/to/testing/data"})
+```
+
+The environment variables `SM_CHANNEL_TRAINING` and `SM_CHANNEL_TESTING` provide the paths to the channels:
+
+``` python
+import argparse
+import os
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+
+  ...
+
+  # reads input channels training and testing from the environment variables
+  parser.add_argument("--training", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
+  parser.add_argument("--testing", type=str, default=os.environ["SM_CHANNEL_TESTING"])
+
+  args = parser.parse_args()
+
+  ...
+```
+
+When training starts, SageMaker Training Toolkit will print all available environment variables. Please see the [reference on environment variables](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md) for a full list of provided environment variables.
+
+### Get information about the container environment
+
+To get information about the container environment, initialize an `Environment` object.
+`Environment` provides access to aspects of the environment relevant to training jobs, including hyperparameters, system characteristics, filesystem locations, environment variables and configuration settings.
+It is a read-only snapshot of the container environment during training, and it doesn't contain any form of state.
+
+``` python
+from sagemaker_training import environment
+
+env = environment.Environment()
+
+# get the path of the channel "training" from the `inputdataconfig.json` file
+training_dir = env.channel_input_dirs["training"]
+
+# get a the hyperparameter "training_data_file" from `hyperparameters.json` file
+file_name = env.hyperparameters["training_data_file"]
+
+# get the folder where the model should be saved
+model_dir = env.model_dir
+
+# train the model
+data = np.load(os.path.join(training_dir, file_name))
+x_train, y_train = data["features"], keras.utils.to_categorical(data["labels"])
+model = ResNet50(weights="imagenet")
+...
+model.fit(x_train, y_train)
+
+#save the model to the model_dir at the end of training
+model.save(os.path.join(model_dir, "saved_model"))
+```
+
+### Execute the entry point
+
+To execute the entry point, call `entry_point.run()`.
+
+``` python
+from sagemaker_training import entry_point, environment
+
+env = environment.Environment()
+
+# read hyperparameters as script arguments
+args = env.to_cmd_args()
+
+# get the environment variables
+env_vars = env.to_env_vars()
+
+# execute the entry point
+entry_point.run(uri=env.module_dir,
+                user_entry_point=env.user_entry_point,
+                args=args,
+                env_vars=env_vars)
+
+```
+
+If the entry point execution fails, `trainer.train()` will write the error message to `/opt/ml/output/failure`. Otherwise, it will write to the file `/opt/ml/success`.
+
+## :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/sagemaker-training-toolkit/blob/master/LICENSE) file.
+
+## :handshake: Contributing
+
+Contributions are welcome!
+Please read our [contributing guidelines](https://github.com/aws/sagemaker-training-toolkit/blob/master/CONTRIBUTING.md)
+if you'd like to open an issue or submit a pull request.
+
+%package -n python3-sagemaker-training
+Summary:	Open source library for creating containers to run on Amazon SageMaker.
+Provides:	python-sagemaker-training
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-sagemaker-training
+![SageMaker](https://github.com/aws/sagemaker-training-toolkit/raw/master/branding/icon/sagemaker-banner.png)
+
+# SageMaker Training Toolkit
+
+[![Latest Version](https://img.shields.io/pypi/v/sagemaker-training.svg)](https://pypi.python.org/pypi/sagemaker-training) [![Supported Python Versions](https://img.shields.io/pypi/pyversions/sagemaker-training.svg)](https://pypi.python.org/pypi/sagemaker-training) [![Code Style: Black](https://img.shields.io/badge/code_style-black-000000.svg)](https://github.com/python/black)
+
+Train 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.
+
+To train a model, you can include your training script and dependencies in a [Docker container](https://www.docker.com/resources/what-container) that runs your training code.
+A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. 
+
+The **SageMaker Training Toolkit** can be easily added to any Docker container, making it compatible with SageMaker for [training models](https://aws.amazon.com/sagemaker/train/).
+If you use a [prebuilt SageMaker Docker image for training](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 [using Docker containers for training](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 sagemaker-training
+```
+
+## :computer: Usage
+
+The following are brief how-to guides.
+For complete, working examples of custom training containers built with the SageMaker Training Toolkit, please see [the example notebooks](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/custom-training-containers).
+
+### Create a Docker image and train a model
+
+1. Write a training script (eg. `train.py`).
+
+2. [Define a container with a Dockerfile](https://docs.docker.com/get-started/part2/#define-a-container-with-dockerfile) that includes the training script and any dependencies.
+
+    The training script must be located in the `/opt/ml/code` directory.
+    The environment variable `SAGEMAKER_PROGRAM` defines which file inside the `/opt/ml/code` directory to use as the training entry point.
+    When training starts, the interpreter executes the entry point defined by `SAGEMAKER_PROGRAM`.
+    Python and shell scripts are both supported.
+    
+    ``` docker
+    FROM yourbaseimage:tag
+  
+    # install the SageMaker Training Toolkit 
+    RUN pip3 install sagemaker-training
+
+    # copy the training script inside the container
+    COPY train.py /opt/ml/code/train.py
+
+    # define train.py as the script entry point
+    ENV SAGEMAKER_PROGRAM train.py
+    ```
+
+3. Build and tag the Docker image.
+
+    ``` shell
+    docker build -t custom-training-container .
+    ```
+
+4. Use the Docker image to start a training job using the [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk).
+
+    ``` python
+    from sagemaker.estimator import Estimator
+
+    estimator = Estimator(image_name="custom-training-container",
+                          role="SageMakerRole",
+                          train_instance_count=1,
+                          train_instance_type="local")
+
+    estimator.fit()
+    ```
+    
+    To train a model using the image on SageMaker, [push the image to ECR](https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-push-ecr-image.html) and start a SageMaker training job with the image URI.
+    
+
+### Pass arguments to the entry point using hyperparameters
+
+Any hyperparameters provided by the training job are passed to the entry point as script arguments.
+The SageMaker Python SDK uses this feature to pass special hyperparameters to the training job, including `sagemaker_program` and `sagemaker_submit_directory`.
+The complete list of SageMaker hyperparameters is available [here](https://github.com/aws/sagemaker-training-toolkit/blob/master/src/sagemaker_training/params.py).
+
+1. Implement an argument parser in the entry point script. For example, in a Python script:
+
+    ``` python
+    import argparse
+
+    if __name__ == "__main__":
+      parser = argparse.ArgumentParser()
+
+      parser.add_argument("--learning-rate", type=int, default=1)
+      parser.add_argument("--batch-size", type=int, default=64)
+      parser.add_argument("--communicator", type=str)
+      parser.add_argument("--frequency", type=int, default=20)
+
+      args = parser.parse_args()
+      ...
+    ```
+
+2. Start a training job with hyperparameters.
+
+    ``` python
+    {"HyperParameters": {"batch-size": 256, "learning-rate": 0.0001, "communicator": "pure_nccl"}}
+    ```
+
+### Read additional information using environment variables
+
+An entry point often needs additional information not available in `hyperparameters`.
+The SageMaker Training Toolkit writes this information as environment variables that are available from within the script.
+For example, this training job includes the channels `training` and `testing`:
+
+``` python
+from sagemaker.pytorch import PyTorch
+
+estimator = PyTorch(entry_point="train.py", ...)
+
+estimator.fit({"training": "s3://bucket/path/to/training/data", 
+               "testing": "s3://bucket/path/to/testing/data"})
+```
+
+The environment variables `SM_CHANNEL_TRAINING` and `SM_CHANNEL_TESTING` provide the paths to the channels:
+
+``` python
+import argparse
+import os
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+
+  ...
+
+  # reads input channels training and testing from the environment variables
+  parser.add_argument("--training", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
+  parser.add_argument("--testing", type=str, default=os.environ["SM_CHANNEL_TESTING"])
+
+  args = parser.parse_args()
+
+  ...
+```
+
+When training starts, SageMaker Training Toolkit will print all available environment variables. Please see the [reference on environment variables](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md) for a full list of provided environment variables.
+
+### Get information about the container environment
+
+To get information about the container environment, initialize an `Environment` object.
+`Environment` provides access to aspects of the environment relevant to training jobs, including hyperparameters, system characteristics, filesystem locations, environment variables and configuration settings.
+It is a read-only snapshot of the container environment during training, and it doesn't contain any form of state.
+
+``` python
+from sagemaker_training import environment
+
+env = environment.Environment()
+
+# get the path of the channel "training" from the `inputdataconfig.json` file
+training_dir = env.channel_input_dirs["training"]
+
+# get a the hyperparameter "training_data_file" from `hyperparameters.json` file
+file_name = env.hyperparameters["training_data_file"]
+
+# get the folder where the model should be saved
+model_dir = env.model_dir
+
+# train the model
+data = np.load(os.path.join(training_dir, file_name))
+x_train, y_train = data["features"], keras.utils.to_categorical(data["labels"])
+model = ResNet50(weights="imagenet")
+...
+model.fit(x_train, y_train)
+
+#save the model to the model_dir at the end of training
+model.save(os.path.join(model_dir, "saved_model"))
+```
+
+### Execute the entry point
+
+To execute the entry point, call `entry_point.run()`.
+
+``` python
+from sagemaker_training import entry_point, environment
+
+env = environment.Environment()
+
+# read hyperparameters as script arguments
+args = env.to_cmd_args()
+
+# get the environment variables
+env_vars = env.to_env_vars()
+
+# execute the entry point
+entry_point.run(uri=env.module_dir,
+                user_entry_point=env.user_entry_point,
+                args=args,
+                env_vars=env_vars)
+
+```
+
+If the entry point execution fails, `trainer.train()` will write the error message to `/opt/ml/output/failure`. Otherwise, it will write to the file `/opt/ml/success`.
+
+## :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/sagemaker-training-toolkit/blob/master/LICENSE) file.
+
+## :handshake: Contributing
+
+Contributions are welcome!
+Please read our [contributing guidelines](https://github.com/aws/sagemaker-training-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-training
+Provides:	python3-sagemaker-training-doc
+%description help
+![SageMaker](https://github.com/aws/sagemaker-training-toolkit/raw/master/branding/icon/sagemaker-banner.png)
+
+# SageMaker Training Toolkit
+
+[![Latest Version](https://img.shields.io/pypi/v/sagemaker-training.svg)](https://pypi.python.org/pypi/sagemaker-training) [![Supported Python Versions](https://img.shields.io/pypi/pyversions/sagemaker-training.svg)](https://pypi.python.org/pypi/sagemaker-training) [![Code Style: Black](https://img.shields.io/badge/code_style-black-000000.svg)](https://github.com/python/black)
+
+Train 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.
+
+To train a model, you can include your training script and dependencies in a [Docker container](https://www.docker.com/resources/what-container) that runs your training code.
+A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. 
+
+The **SageMaker Training Toolkit** can be easily added to any Docker container, making it compatible with SageMaker for [training models](https://aws.amazon.com/sagemaker/train/).
+If you use a [prebuilt SageMaker Docker image for training](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 [using Docker containers for training](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 sagemaker-training
+```
+
+## :computer: Usage
+
+The following are brief how-to guides.
+For complete, working examples of custom training containers built with the SageMaker Training Toolkit, please see [the example notebooks](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/custom-training-containers).
+
+### Create a Docker image and train a model
+
+1. Write a training script (eg. `train.py`).
+
+2. [Define a container with a Dockerfile](https://docs.docker.com/get-started/part2/#define-a-container-with-dockerfile) that includes the training script and any dependencies.
+
+    The training script must be located in the `/opt/ml/code` directory.
+    The environment variable `SAGEMAKER_PROGRAM` defines which file inside the `/opt/ml/code` directory to use as the training entry point.
+    When training starts, the interpreter executes the entry point defined by `SAGEMAKER_PROGRAM`.
+    Python and shell scripts are both supported.
+    
+    ``` docker
+    FROM yourbaseimage:tag
+  
+    # install the SageMaker Training Toolkit 
+    RUN pip3 install sagemaker-training
+
+    # copy the training script inside the container
+    COPY train.py /opt/ml/code/train.py
+
+    # define train.py as the script entry point
+    ENV SAGEMAKER_PROGRAM train.py
+    ```
+
+3. Build and tag the Docker image.
+
+    ``` shell
+    docker build -t custom-training-container .
+    ```
+
+4. Use the Docker image to start a training job using the [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk).
+
+    ``` python
+    from sagemaker.estimator import Estimator
+
+    estimator = Estimator(image_name="custom-training-container",
+                          role="SageMakerRole",
+                          train_instance_count=1,
+                          train_instance_type="local")
+
+    estimator.fit()
+    ```
+    
+    To train a model using the image on SageMaker, [push the image to ECR](https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-push-ecr-image.html) and start a SageMaker training job with the image URI.
+    
+
+### Pass arguments to the entry point using hyperparameters
+
+Any hyperparameters provided by the training job are passed to the entry point as script arguments.
+The SageMaker Python SDK uses this feature to pass special hyperparameters to the training job, including `sagemaker_program` and `sagemaker_submit_directory`.
+The complete list of SageMaker hyperparameters is available [here](https://github.com/aws/sagemaker-training-toolkit/blob/master/src/sagemaker_training/params.py).
+
+1. Implement an argument parser in the entry point script. For example, in a Python script:
+
+    ``` python
+    import argparse
+
+    if __name__ == "__main__":
+      parser = argparse.ArgumentParser()
+
+      parser.add_argument("--learning-rate", type=int, default=1)
+      parser.add_argument("--batch-size", type=int, default=64)
+      parser.add_argument("--communicator", type=str)
+      parser.add_argument("--frequency", type=int, default=20)
+
+      args = parser.parse_args()
+      ...
+    ```
+
+2. Start a training job with hyperparameters.
+
+    ``` python
+    {"HyperParameters": {"batch-size": 256, "learning-rate": 0.0001, "communicator": "pure_nccl"}}
+    ```
+
+### Read additional information using environment variables
+
+An entry point often needs additional information not available in `hyperparameters`.
+The SageMaker Training Toolkit writes this information as environment variables that are available from within the script.
+For example, this training job includes the channels `training` and `testing`:
+
+``` python
+from sagemaker.pytorch import PyTorch
+
+estimator = PyTorch(entry_point="train.py", ...)
+
+estimator.fit({"training": "s3://bucket/path/to/training/data", 
+               "testing": "s3://bucket/path/to/testing/data"})
+```
+
+The environment variables `SM_CHANNEL_TRAINING` and `SM_CHANNEL_TESTING` provide the paths to the channels:
+
+``` python
+import argparse
+import os
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+
+  ...
+
+  # reads input channels training and testing from the environment variables
+  parser.add_argument("--training", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
+  parser.add_argument("--testing", type=str, default=os.environ["SM_CHANNEL_TESTING"])
+
+  args = parser.parse_args()
+
+  ...
+```
+
+When training starts, SageMaker Training Toolkit will print all available environment variables. Please see the [reference on environment variables](https://github.com/aws/sagemaker-training-toolkit/blob/master/ENVIRONMENT_VARIABLES.md) for a full list of provided environment variables.
+
+### Get information about the container environment
+
+To get information about the container environment, initialize an `Environment` object.
+`Environment` provides access to aspects of the environment relevant to training jobs, including hyperparameters, system characteristics, filesystem locations, environment variables and configuration settings.
+It is a read-only snapshot of the container environment during training, and it doesn't contain any form of state.
+
+``` python
+from sagemaker_training import environment
+
+env = environment.Environment()
+
+# get the path of the channel "training" from the `inputdataconfig.json` file
+training_dir = env.channel_input_dirs["training"]
+
+# get a the hyperparameter "training_data_file" from `hyperparameters.json` file
+file_name = env.hyperparameters["training_data_file"]
+
+# get the folder where the model should be saved
+model_dir = env.model_dir
+
+# train the model
+data = np.load(os.path.join(training_dir, file_name))
+x_train, y_train = data["features"], keras.utils.to_categorical(data["labels"])
+model = ResNet50(weights="imagenet")
+...
+model.fit(x_train, y_train)
+
+#save the model to the model_dir at the end of training
+model.save(os.path.join(model_dir, "saved_model"))
+```
+
+### Execute the entry point
+
+To execute the entry point, call `entry_point.run()`.
+
+``` python
+from sagemaker_training import entry_point, environment
+
+env = environment.Environment()
+
+# read hyperparameters as script arguments
+args = env.to_cmd_args()
+
+# get the environment variables
+env_vars = env.to_env_vars()
+
+# execute the entry point
+entry_point.run(uri=env.module_dir,
+                user_entry_point=env.user_entry_point,
+                args=args,
+                env_vars=env_vars)
+
+```
+
+If the entry point execution fails, `trainer.train()` will write the error message to `/opt/ml/output/failure`. Otherwise, it will write to the file `/opt/ml/success`.
+
+## :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/sagemaker-training-toolkit/blob/master/LICENSE) file.
+
+## :handshake: Contributing
+
+Contributions are welcome!
+Please read our [contributing guidelines](https://github.com/aws/sagemaker-training-toolkit/blob/master/CONTRIBUTING.md)
+if you'd like to open an issue or submit a pull request.
+
+%prep
+%autosetup -n sagemaker-training-4.4.10
+
+%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-training -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 4.4.10-1
+- Package Spec generated