{
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        "description": "[![pypi status](https://img.shields.io/pypi/v/bentoml.svg)](https://pypi.org/project/BentoML)\n[![python versions](https://img.shields.io/pypi/pyversions/bentoml.svg)](https://travis-ci.org/bentoml/BentoML)\n[![Downloads](https://pepy.tech/badge/bentoml)](https://pepy.tech/project/bentoml)\n[![build status](https://travis-ci.org/bentoml/BentoML.svg?branch=master)](https://travis-ci.org/bentoml/BentoML)\n[![Documentation Status](https://readthedocs.org/projects/bentoml/badge/?version=latest)](https://bentoml.readthedocs.io/en/latest/?badge=latest)\n[![join BentoML Slack](https://badgen.net/badge/Join/BentoML%20Slack/cyan?icon=slack)](http://bit.ly/2N5IpbB)\n\n> From a model in jupyter notebook to production API service in 5 minutes\n\n\n[![BentoML](https://raw.githubusercontent.com/bentoml/BentoML/master/docs/_static/img/bentoml.png)](https://colab.research.google.com/github/bentoml/BentoML/blob/master/guides/quick-start/bentoml-quick-start-guide.ipynb)\n\n[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)\n\n\nBentoML is a flexible framework that accelerates the workflow of\n__serving and deploying machine learning models__ in the cloud. \n\nCheck out our 5-mins [Quickstart Notebook](https://colab.research.google.com/github/bentoml/BentoML/blob/master/guides/quick-start/bentoml-quick-start-guide.ipynb)\n using BentoML to turn a trained sklearn model into a containerized\n REST API server, and then deploy it to AWS Lambda.\n\nIf you are using BentoML for production workloads or wants to contribute,\nbe sure to join our Slack channel and hear our latest development updates:\n[![join BentoML Slack](https://badgen.net/badge/Join/BentoML%20Slack/cyan?icon=slack)](http://bit.ly/2N5IpbB)\n\n---\n\n\n## Getting Started\n\nInstallation with pip:\n```bash\npip install bentoml\n```\n\nDefining a prediction service with BentoML:\n\n```python\nimport bentoml\nfrom bentoml.handlers import DataframeHandler\nfrom bentoml.artifact import SklearnModelArtifact\n\n@bentoml.env(pip_dependencies=[\"scikit-learn\"])\n@bentoml.artifacts([SklearnModelArtifact('model')])\nclass IrisClassifier(bentoml.BentoService):\n\n    @bentoml.api(DataframeHandler)\n    def predict(self, df):\n        return self.artifacts.model.predict(df)\n```\n\nTrain a classifier model with default Iris dataset and pack the trained model\nwith the BentoService `IrisClassifier` defined above:\n\n```python\nfrom sklearn import svm\nfrom sklearn import datasets\n\nclf = svm.SVC(gamma='scale')\niris = datasets.load_iris()\nX, y = iris.data, iris.target\nclf.fit(X, y)\n\n# Create a iris classifier service with the newly trained model\niris_classifier_service = IrisClassifier.pack(model=clf)\n\n# Save the entire prediction service to file bundle\nsaved_path = iris_classifier_service.save()\n```\n\nA BentoML bundle is a versioned file archive, containing the BentoService you\ndefined, along with trained model artifacts, dependencies and configurations.\n\nNow you can start a REST API server based off the saved BentoML bundle form\ncommand line:\n```bash\nbentoml serve {saved_path}\n```\n\nIf you are doing this only local machine, visit [http://127.0.0.1:5000](http://127.0.0.1:5000)\nin your browser to play around with the API server's Web UI for debbugging and\ntesting. You can also send prediction request with `curl` from command line:\n\n```bash\ncurl -i \\\n  --header \"Content-Type: application/json\" \\\n  --request POST \\\n  --data '[[5.1, 3.5, 1.4, 0.2]]' \\\n  http://localhost:5000/predict\n```\n\nThe saved BentoML bundle can also be loaded directly from command line for inferencing:\n```bash\nbentoml predict {saved_path} --input='[[5.1, 3.5, 1.4, 0.2]]'\n\n# alternatively:\nbentoml predict {saved_path} --input='./iris_test_data.csv'\n```\n\nBentoML bundle is pip-installable and can be directly distributed as a PyPI package:\n```bash\npip install {saved_path}\n```\n```python\n# Your bentoML model class name will become packaged name\nimport IrisClassifier\n\ninstalled_svc = IrisClassifier.load()\ninstalled_svc.predict([[5.1, 3.5, 1.4, 0.2]])\n```\n\nBentoML bundle is structured to work as a docker build context so you can easily\nbuild a docker image for this API server by using it as the build context\ndirectory:\n```bash\ndocker build -t my_api_server {saved_path}\n```\n\nTo learn more, try out the Getting Started with Bentoml notebook: [![Google Colab Badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bentoml/BentoML/blob/master/guides/quick-start/bentoml-quick-start-guide.ipynb)\n\n\n## Examples\n\n#### FastAI\n\n* 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)\n* 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)\n\n\n#### Scikit-Learn\n\n* 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)\n\n\n#### PyTorch\n\n* 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)\n* 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)\n\n\n#### Keras\n\n* 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)\n* 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)\n* 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)\n\n\n#### XGBoost\n\n* 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)\n* 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)\n\n\n#### H2O\n\n* 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)\n* 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)\n\n Visit [bentoml/gallery](https://github.com/bentoml/gallery) repository for more\n example projects demonstrating how to use BentoML.\n\n\n### Deployment guides:\n\n- [Serverless deployment with AWS Lambda](https://github.com/bentoml/BentoML/blob/master/guides/deployment/deploy-with-serverless)\n- [API server deployment with AWS SageMaker](https://github.com/bentoml/BentoML/blob/master/guides/deployment/deploy-with-sagemaker)\n- [(Beta) API server deployment with Clipper](https://github.com/bentoml/BentoML/blob/master/guides/deployment/deploy-with-clipper/deploy-iris-classifier-to-clipper.ipynb)\n- [(Beta) API server deployment on Kubernetes](https://github.com/bentoml/BentoML/tree/master/guides/deployment/deploy-with-kubernetes)\n\n\n## Project Overview\n\nBentoML provides two set of high-level APIs:\n\n* BentoService: Turn your trained ML model into versioned file bundle that can be\n  deployed as containerize REST API server, PyPI package, CLI tool, or\n  batch/streaming job\n\n* YataiService: Manage and deploy your saved BentoML bundles into prediction\n  services on Kubernetes cluster or cloud platforms such as AWS Lambda, SageMaker,\n  Azure ML, and GCP Function etc\n\n\n## Feature Highlights\n\n\n* __Multiple Distribution Format__ - Easily package your Machine Learning models\n  and preprocessing code into a format that works best with your inference scenario:\n  * Docker Image - deploy as containers running REST API Server\n  * PyPI Package - integrate into your python applications seamlessly\n  * CLI tool - put your model into Airflow DAG or CI/CD pipeline\n  * Spark UDF - run batch serving on a large dataset with Spark\n  * Serverless Function - host your model on serverless platforms such as AWS Lambda\n\n* __Multiple Framework Support__ - BentoML supports a wide range of ML frameworks\n  out-of-the-box including [Tensorflow](https://github.com/tensorflow/tensorflow/),\n  [PyTorch](https://github.com/pytorch/pytorch),\n  [Keras](https://keras.io/),\n  [Scikit-Learn](https://github.com/scikit-learn/scikit-learn),\n  [xgboost](https://github.com/dmlc/xgboost),\n  [H2O](https://github.com/h2oai/h2o-3),\n  [FastAI](https://github.com/fastai/fastai) and can be easily extended to work\n  with new or custom frameworks.\n\n* __Deploy Anywhere__ - BentoML bundle can be easily deployed with\n  platforms such as [Docker](https://www.docker.com/),\n  [Kubernetes](https://kubernetes.io/),\n  [Serverless](https://github.com/serverless/serverless),\n  [Airflow](https://airflow.apache.org) and [Clipper](http://clipper.ai),\n  on cloud platforms including AWS, Google Cloud, and Azure.\n\n* __Custom Runtime Backend__ - Easily integrate your python pre-processing code with\n  high-performance deep learning runtime backend, such as\n  [tensorflow-serving](https://github.com/tensorflow/serving).\n\n\n## Documentation\n\nFull documentation and API references can be found at [bentoml.readthedocs.io](http://bentoml.readthedocs.io)\n\n\n## Usage Tracking\n\nBentoML library by default reports basic usages using\n[Amplitude](https://amplitude.com). It helps BentoML authors to understand how\npeople are using this tool and improve it over time. You can easily opt-out by\nrunning the following command from terminal:\n\n```bash\nbentoml config set usage_tracking=false\n```\n\n## Contributing\n\nHave questions or feedback? Post a [new github issue](https://github.com/bentoml/BentoML/issues/new/choose)\nor join our Slack channel: [![join BentoML Slack](https://badgen.net/badge/Join/BentoML%20Slack/cyan?icon=slack)](http://bit.ly/2N5IpbB)\n\nWant to help build BentoML? Check out our\n[contributing guide](https://github.com/bentoml/BentoML/blob/master/CONTRIBUTING.md) and the\n[development guide](https://github.com/bentoml/BentoML/blob/master/DEVELOPMENT.md).\n\n## Releases\n\nBentoML is under active development and is evolving rapidly. **Currently it is a\nBeta release, we may change APIs in future releases**.\n\nRead more about the latest features and changes in BentoML from the [releases page](https://github.com/bentoml/BentoML/releases).\n\n\n## License\n\n[Apache License 2.0](https://github.com/bentoml/BentoML/blob/master/LICENSE)\n\n[![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)\n\n\n",
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