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        "description": "![SageMaker](https://github.com/aws/sagemaker-inference-toolkit/raw/master/branding/icon/sagemaker-banner.png)\n\n# SageMaker Inference Toolkit\n\n[![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)\n\nServe machine learning models within a Docker container using Amazon\nSageMaker.\n\n## :books: Background\n\n[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service for data science and machine learning (ML) workflows.\nYou can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.\n\nOnce 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.\nA container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed.\nContainerizing your model and code enables fast and reliable deployment of your model.\n\nThe **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/).\nThis 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).\nIf 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.\n\nFor 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).\n\n## :hammer_and_wrench: Installation\n\nTo install this library in your Docker image, add the following line to your [Dockerfile](https://docs.docker.com/engine/reference/builder/):\n\n``` dockerfile\nRUN pip3 install multi-model-server sagemaker-inference\n```\n\n[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.\n\n## :computer: Usage\n\n### Implementation Steps\n\nTo use the SageMaker Inference Toolkit, you need to do the following:\n\n1.  Implement an inference handler, which is responsible for loading the model and providing input, predict, and output functions.\n    ([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.)\n\n    ``` python\n    from sagemaker_inference import content_types, decoder, default_inference_handler, encoder, errors\n\n    class DefaultPytorchInferenceHandler(default_inference_handler.DefaultInferenceHandler):\n\n        def default_model_fn(self, model_dir):\n            \"\"\"Loads a model. For PyTorch, a default function to load a model cannot be provided.\n            Users should provide customized model_fn() in script.\n\n            Args:\n                model_dir: a directory where model is saved.\n\n            Returns: A PyTorch model.\n            \"\"\"\n            raise NotImplementedError(textwrap.dedent(\"\"\"\n            Please provide a model_fn implementation.\n            See documentation for model_fn at https://github.com/aws/sagemaker-python-sdk\n            \"\"\"))\n\n        def default_input_fn(self, input_data, content_type):\n            \"\"\"A default input_fn that can handle JSON, CSV and NPZ formats.\n\n            Args:\n                input_data: the request payload serialized in the content_type format\n                content_type: the request content_type\n\n            Returns: input_data deserialized into torch.FloatTensor or torch.cuda.FloatTensor depending if cuda is available.\n            \"\"\"\n            return decoder.decode(input_data, content_type)\n\n        def default_predict_fn(self, data, model):\n            \"\"\"A default predict_fn for PyTorch. Calls a model on data deserialized in input_fn.\n            Runs prediction on GPU if cuda is available.\n\n            Args:\n                data: input data (torch.Tensor) for prediction deserialized by input_fn\n                model: PyTorch model loaded in memory by model_fn\n\n            Returns: a prediction\n            \"\"\"\n            return model(input_data)\n\n        def default_output_fn(self, prediction, accept):\n            \"\"\"A default output_fn for PyTorch. Serializes predictions from predict_fn to JSON, CSV or NPY format.\n\n            Args:\n                prediction: a prediction result from predict_fn\n                accept: type which the output data needs to be serialized\n\n            Returns: output data serialized\n            \"\"\"\n            return encoder.encode(prediction, accept)\n    ```\n\n2.  Implement a handler service that is executed by the model server.\n    ([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.)\n    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).\n\n    ``` python\n    from sagemaker_inference.default_handler_service import DefaultHandlerService\n    from sagemaker_inference.transformer import Transformer\n    from sagemaker_pytorch_serving_container.default_inference_handler import DefaultPytorchInferenceHandler\n\n\n    class HandlerService(DefaultHandlerService):\n        \"\"\"Handler service that is executed by the model server.\n        Determines specific default inference handlers to use based on model being used.\n        This class extends ``DefaultHandlerService``, which define the following:\n            - The ``handle`` method is invoked for all incoming inference requests to the model server.\n            - The ``initialize`` method is invoked at model server start up.\n        Based on: https://github.com/awslabs/multi-model-server/blob/master/docs/custom_service.md\n        \"\"\"\n        def __init__(self):\n            transformer = Transformer(default_inference_handler=DefaultPytorchInferenceHandler())\n            super(HandlerService, self).__init__(transformer=transformer)\n    ```\n\n3.  Implement a serving entrypoint, which starts the model server.\n    ([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.)\n\n    ``` python\n    from sagemaker_inference import model_server\n\n    model_server.start_model_server(handler_service=HANDLER_SERVICE)\n    ```\n\n4.  Define the location of the entrypoint in your Dockerfile.\n\n    ``` dockerfile\n    ENTRYPOINT [\"python\", \"/usr/local/bin/entrypoint.py\"]\n    ```\n\n### Complete Example\n\n[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.\n\n## :scroll: License\n\nThis library is licensed under the [Apache 2.0 License](http://aws.amazon.com/apache2.0/).\nFor more details, please take a look at the [LICENSE](https://github.com/aws-samples/sagemaker-inference-toolkit/blob/master/LICENSE) file.\n\n## :handshake: Contributing\n\nContributions are welcome!\nPlease read our [contributing guidelines](https://github.com/aws/sagemaker-inference-toolkit/blob/master/CONTRIBUTING.md)\nif you'd like to open an issue or submit a pull request.",
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