Metadata-Version: 2.1
Name: BentoML
Version: 0.5.6
Summary: A platform for serving and deploying machine learning models in the cloud
Home-page: https://github.com/bentoml/BentoML
Author: bentoml.org
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/bentoml/BentoML/issues
Project-URL: Source Code, https://github.com/bentoml/BentoML
Project-URL: Slack User Group, https://bit.ly/2N5IpbB
Description: [![pypi status](https://img.shields.io/pypi/v/bentoml.svg)](https://pypi.org/project/BentoML)
        [![python versions](https://img.shields.io/pypi/pyversions/bentoml.svg)](https://travis-ci.org/bentoml/BentoML)
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        [![Documentation Status](https://readthedocs.org/projects/bentoml/badge/?version=latest)](https://bentoml.readthedocs.io/en/latest/?badge=latest)
        [![join BentoML Slack](https://badgen.net/badge/Join/BentoML%20Slack/cyan?icon=slack)](https://join.slack.com/t/bentoml/shared_invite/enQtNjcyMTY3MjE4NTgzLTU3ZDc1MWM5MzQxMWQxMzJiNTc1MTJmMzYzMTYwMjQ0OGEwNDFmZDkzYWQxNzgxYWNhNjAxZjk4MzI4OGY1Yjg)
        
        > From ML model to production API endpoint with a few lines of code
        
        
        [![BentoML](https://raw.githubusercontent.com/bentoml/BentoML/master/docs/_static/img/bentoml.png)](https://github.com/bentoml/BentoML)
        
        [Getting Started](https://github.com/bentoml/BentoML#getting-started) | [Documentation](http://bentoml.readthedocs.io) | [Gallery](https://github.com/bentoml/gallery) | [Contributing](https://github.com/bentoml/BentoML#contributing) | [Releases](https://github.com/bentoml/BentoML#releases) | [License](https://github.com/bentoml/BentoML/blob/master/LICENSE) | [Blog](https://medium.com/bentoml)
        
        
        BentoML makes it easy to __serve and deploy machine learning models__ in the cloud.
        
        It is an open source framework for machine learning teams to build cloud-native prediction API
        services that are ready for production. BentoML supports most popular ML training frameworks
        and common deployment platforms including major cloud providers and docker/kubernetes.
        
        👉 [Join BentoML Slack community](https://join.slack.com/t/bentoml/shared_invite/enQtNjcyMTY3MjE4NTgzLTU3ZDc1MWM5MzQxMWQxMzJiNTc1MTJmMzYzMTYwMjQ0OGEwNDFmZDkzYWQxNzgxYWNhNjAxZjk4MzI4OGY1Yjg)
         to hear about the latest development updates.
        
        ---
        
        
        ## Getting Started
        
        Installation with pip:
        ```bash
        pip install bentoml
        ```
        
        Defining a prediction service with BentoML:
        
        ```python
        import bentoml
        from bentoml.handlers import DataframeHandler
        from bentoml.artifact import SklearnModelArtifact
        
        @bentoml.env(pip_dependencies=["scikit-learn"]) # defining pip/conda dependencies to be packed
        @bentoml.artifacts([SklearnModelArtifact('model')]) # defining required artifacts, typically trained models
        class IrisClassifier(bentoml.BentoService):
        
            @bentoml.api(DataframeHandler) # defining prediction service endpoint and expected input format
            def predict(self, df):
                # Pre-processing logic and access to trained mdoel artifacts in API function
                return self.artifacts.model.predict(df)
        ```
        
        Train a classifier model with default Iris dataset and pack the trained model
        with the BentoService `IrisClassifier` defined above:
        
        ```python
        from sklearn import svm
        from sklearn import datasets
        
        if __name__ == "__main__":
            clf = svm.SVC(gamma='scale')
            iris = datasets.load_iris()
            X, y = iris.data, iris.target
            clf.fit(X, y)
        
            # Create a iris classifier service
            iris_classifier_service = IrisClassifier()
        
            # Pack it with the newly trained model artifact
            iris_classifier_service.pack('model', clf)
        
            # Save the prediction service to a BentoService bundle
            saved_path = iris_classifier_service.save()
        ```
        
        A BentoService bundle is a versioned file archive, containing the BentoService you
        defined, along with trained model artifacts, dependencies and configurations.
        
        Now you can start a REST API server based off the saved BentoService bundle form
        command line:
        ```bash
        bentoml serve {saved_path}
        ```
        
        If you are doing this only local machine, visit [http://127.0.0.1:5000](http://127.0.0.1:5000)
        in your browser to play around with the API server's Web UI for debbugging and
        sending test request. You can also send prediction request with `curl` from command line:
        
        ```bash
        curl -i \
          --header "Content-Type: application/json" \
          --request POST \
          --data '[[5.1, 3.5, 1.4, 0.2]]' \
          http://localhost:5000/predict
        ```
        
        Saved BentoService bundle is also structured to work as a docker build context, which can be
        used to build a docker image for deployment:
        ```bash
        docker build -t my_api_server {saved_path}
        ```
        
        The saved BentoService bundle can also be loaded directly from command line:
        ```bash
        bentoml predict {saved_path} --input='[[5.1, 3.5, 1.4, 0.2]]'
        
        # alternatively:
        bentoml predict {saved_path} --input='./iris_test_data.csv'
        ```
        
        The saved bundle is pip-installable and can be directly distributed as a PyPI package:
        ```bash
        pip install {saved_path}
        ```
        ```python
        # Your BentoService class name will become packaged name
        import IrisClassifier
        
        installed_svc = IrisClassifier.load()
        installed_svc.predict([[5.1, 3.5, 1.4, 0.2]])
        ```
        
        Deploy the saved BentoService to cloud services such as AWS Lambda with the `bentoml `command:
        ```
        bentoml deployment create my-iris-classifier --bento IrisClassifier:{VERSION} --platform=aws-lambda
        ```
        
        To learn more, try out our 5-mins Quick Start notebook using BentoML to turn a trained sklearn model into a containerized REST API server, and then deploy it to AWS Lambda: [Download](https://github.com/bentoml/BentoML/blob/master/guides/quick-start/bentoml-quick-start-guide.ipynb), [Google Colab](https://colab.research.google.com/github/bentoml/BentoML/blob/master/guides/quick-start/bentoml-quick-start-guide.ipynb), [nbviewer](https://nbviewer.jupyter.org/github/bentoml/bentoml/blob/master/guides/quick-start/bentoml-quick-start-guide.ipynb)
        
        
        ## Examples
        
        #### FastAI
        
        * Pet Image Classification - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/fast-ai/pet-image-classification/fast-ai-pet-image-classification.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/fast-ai/pet-image-classification/fast-ai-pet-image-classification.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/fast-ai/pet-image-classification/fast-ai-pet-image-classification.ipynb)
        * Salary Range Prediction - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/fast-ai/salary-range-prediction/fast-ai-salary-range-prediction.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/fast-ai/salary-range-prediction/fast-ai-salary-range-prediction.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/fast-ai/salary-range-prediction/fast-ai-salary-range-prediction.ipynb)
        
        
        #### Scikit-Learn
        
        * Sentiment Analysis - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/scikit-learn/sentiment-analysis/sklearn-sentiment-analysis.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/scikit-learn/sentiment-analysis/sklearn-sentiment-analysis.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/scikit-learn/sentiment-analysis/sklearn-sentiment-analysis.ipynb)
        
        
        #### PyTorch
        
        * Fashion MNIST - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/pytorch/fashion-mnist/pytorch-fashion-mnist.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/pytorch/fashion-mnist/pytorch-fashion-mnist.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/pytorch/fashion-mnist/pytorch-fashion-mnist.ipynb)
        * CIFAR-10 Image Classification - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/pytorch/cifar10-image-classification/pytorch-cifar10-image-classification.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/pytorch/cifar10-image-classification/pytorch-cifar10-image-classification.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/pytorch/cifar10-image-classification/pytorch-cifar10-image-classification.ipynb)
        
        
        #### Keras
        
        * Fashion MNIST - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/keras/fashion-mnist/keras-fashion-mnist.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/keras/fashion-mnist/keras-fashion-mnist.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/keras/fashion-mnist/keras-fashion-mnist.ipynb)
        * Text Classification - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/keras/text-classification/keras-text-classification.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/keras/text-classification/keras-text-classification.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/keras/text-classification/keras-text-classification.ipynb)
        * Toxic Comment Classifier - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/keras/toxic-comment-classification/keras-toxic-comment-classification.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/keras/toxic-comment-classification/keras-toxic-comment-classification.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/keras/toxic-comment-classification/keras-toxic-comment-classification.ipynb)
        
        
        #### XGBoost
        
        * Titanic Survival Prediction - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/xgboost/titanic-survival-prediction/xgboost-titanic-survival-prediction.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/xgboost/titanic-survival-prediction/xgboost-titanic-survival-prediction.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/xgboost/titanic-survival-prediction/xgboost-titanic-survival-prediction.ipynb)
        * League of Legend win Prediction - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/xgboost/league-of-legend-win-prediction/xgboost-league-of-legend-win-prediction.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/xgboost/league-of-legend-win-prediction/xgboost-league-of-legend-win-prediction.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/xgboost/league-of-legend-win-prediction/xgboost-league-of-legend-win-prediction.ipynb)
        
        #### LightGBM
        
        * Titanic Survival Prediction -  [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/lightbgm/titanic-survival-prediction/lightbgm-titanic-survival-prediction.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/lightbgm/titanic-survival-prediction/lightbgm-titanic-survival-prediction.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/lightbgm/titanic-survival-prediction/lightbgm-titanic-survival-prediction.ipynb)
        
        
        #### H2O
        
        * Loan Default Prediction - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/h2o/loan-prediction/h2o-loan-prediction.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/h2o/loan-prediction/h2o-loan-prediction.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/h2o/loan-prediction/h2o-loan-prediction.ipynb)
        * Prostate Cancer Prediction - [Google Colab](https://colab.research.google.com/github/bentoml/gallery/blob/master/h2o/prostate-cancer-classification/h2o-prostate-cancer-classification.ipynb) | [nbviewer](https://nbviewer.jupyter.org/github/bentoml/gallery/blob/master/h2o/prostate-cancer-classification/h2o-prostate-cancer-classification.ipynb) | [source](https://github.com/bentoml/gallery/blob/master/h2o/prostate-cancer-classification/h2o-prostate-cancer-classification.ipynb)
        
         Visit [bentoml/gallery](https://github.com/bentoml/gallery) repository for more
         example projects demonstrating how to use BentoML.
        
        
        ### Deployment guides:
        
        - [BentoML AWS Lambda Deployment Guide](https://github.com/bentoml/BentoML/blob/master/guides/deployment/deploy-with-serverless)
        - [BentoML AWS SageMaker Deployment Guide](https://github.com/bentoml/BentoML/blob/master/guides/deployment/deploy-with-sagemaker)
        - [BentoML Clipper.ai Deployment Guide](https://github.com/bentoml/BentoML/blob/master/guides/deployment/deploy-with-clipper/bentoml-clipper-deployment-guide.ipynb)
        - [BentoML AWS ECS Deployment Guide](https://github.com/bentoml/BentoML/tree/master/guides/deployment/deploy-with-aws-ecs)
        - [BentoML Google Cloud Run Deployment Guide](https://github.com/bentoml/BentoML/blob/master/guides/deployment/deploy-with-google-cloud-run/deploy-with-google-cloud-run.ipynb)
        - [(Beta) BentoML Kubernetes Deployment Guide](https://github.com/bentoml/BentoML/tree/master/guides/deployment/deploy-with-kubernetes)
        
        
        ## Feature Highlights
        
        
        * __Multiple Distribution Format__ - Easily package your Machine Learning models
          and preprocessing code into a format that works best with your inference scenario:
          * Docker Image - deploy as containers running REST API Server
          * PyPI Package - integrate into your python applications seamlessly
          * CLI tool - put your model into Airflow DAG or CI/CD pipeline
          * Spark UDF - run batch serving on a large dataset with Spark
          * Serverless Function - host your model on serverless platforms such as AWS Lambda
        
        * __Multiple Framework Support__ - BentoML supports a wide range of ML frameworks
          out-of-the-box including [Tensorflow](https://github.com/tensorflow/tensorflow/),
          [PyTorch](https://github.com/pytorch/pytorch),
          [Keras](https://keras.io/),
          [Scikit-Learn](https://github.com/scikit-learn/scikit-learn),
          [xgboost](https://github.com/dmlc/xgboost),
          [H2O](https://github.com/h2oai/h2o-3),
          [FastAI](https://github.com/fastai/fastai) and can be easily extended to work
          with new or custom frameworks
        
        * __Deploy Anywhere__ - BentoService bundle can be easily deployed with
          platforms such as [Docker](https://www.docker.com/),
          [Kubernetes](https://kubernetes.io/),
          [Serverless](https://github.com/serverless/serverless),
          [Airflow](https://airflow.apache.org) and [Clipper](http://clipper.ai),
          on cloud platforms including AWS, Google Cloud, and Azure
        
        * __Custom Runtime Backend__ - Easily integrate your python pre-processing code with
          high-performance deep learning runtime backend, such as
          [tensorflow-serving](https://github.com/tensorflow/serving)
        
        * __Workflow Designed For Teams__ - The YataiService component in BentoML provides
          Web UI and APIs for managing and deploying all the models and preidciton services
          your team has created or deployed, in a centralized service.
        
        
        ## Documentation
        
        Full documentation and API references can be found at [bentoml.readthedocs.io](http://bentoml.readthedocs.io)
        
        
        ## Usage Tracking
        
        BentoML library by default reports basic usages using
        [Amplitude](https://amplitude.com). It helps BentoML authors to understand how
        people are using this tool and improve it over time. You can easily opt-out by
        running the following command from terminal:
        
        ```bash
        bentoml config set usage_tracking=false
        ```
        
        ## Contributing
        
        Have questions or feedback? Post a [new github issue](https://github.com/bentoml/BentoML/issues/new/choose)
        or discuss in our Slack channel: [![join BentoML Slack](https://badgen.net/badge/Join/BentoML%20Slack/cyan?icon=slack)](https://join.slack.com/t/bentoml/shared_invite/enQtNjcyMTY3MjE4NTgzLTU3ZDc1MWM5MzQxMWQxMzJiNTc1MTJmMzYzMTYwMjQ0OGEwNDFmZDkzYWQxNzgxYWNhNjAxZjk4MzI4OGY1Yjg)
        
        Want to help build BentoML? Check out our
        [contributing guide](https://github.com/bentoml/BentoML/blob/master/CONTRIBUTING.md) and the
        [development guide](https://github.com/bentoml/BentoML/blob/master/DEVELOPMENT.md).
        
        ## Releases
        
        BentoML is under active development and is evolving rapidly. **Currently it is a
        Beta release, we may change APIs in future releases**.
        
        Read more about the latest features and changes in BentoML from the [releases page](https://github.com/bentoml/BentoML/releases).
        
        
        ## License
        
        [Apache License 2.0](https://github.com/bentoml/BentoML/blob/master/LICENSE)
        
        [![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fbentoml%2FBentoML.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fbentoml%2FBentoML?ref=badge_large)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.4
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: dev
Provides-Extra: api_server
Provides-Extra: test
Provides-Extra: doc_builder
