Dominic Feeney

Dominic Feeney

1648240140

BentoML: Unified Model Serving Framework for Tensorflow

Unified Model Serving Framework

BentoML is an open platform that simplifies ML model deployment and enables you to serve your models at production scale in minutes

👉 Pop into our Slack community! We're happy to help with any issue you face or even just to meet you and hear what you're working on :)

The BentoML version 1.0 is around the corner. For stable release version 0.13, see the 0.13-LTS branch. Version 1.0 is under active development, you can be of great help by testing out the preview release, reporting issues, contribute to the documentation and create sample gallery projects.

Why BentoML

  • The easiest way to turn your ML models into production-ready API endpoints.
  • High performance model serving, all in Python.
  • Standardize model packaging and ML service definition to streamline deployment.
  • Support all major machine-learning training frameworks.
  • Deploy and operate ML serving workload at scale on Kubernetes via Yatai.

Getting Started

  • Quickstart guide will show you a simple example of using BentoML in action. In under 10 minutes, you'll be able to serve your ML model over an HTTP API endpoint, and build a docker image that is ready to be deployed in production.
  • Main concepts will give a comprehensive tour of BentoML's components and introduce you to its philosophy. After reading, you will see what drives BentoML's design, and know what bento and runner stands for.
  • ML Frameworks lays out best practices and example usages by the ML framework used for training models.
  • Advanced Guides showcases advanced features in BentoML, including GPU support, inference graph, monitoring, and customizing docker environment etc.
  • Check out other projects from the BentoML team:

Community

Contributing

There are many ways to contribute to the project:

  • If you have any feedback on the project, share it with the community in Github Discussions of this project.
  • Report issues you're facing and "Thumbs up" on issues and feature requests that are relevant to you.
  • Investigate bugs and reviewing other developer's pull requests.
  • Contributing code or documentation to the project by submitting a Github pull request. See the development guide.
  • See more in the contributing guide.

Usage Reporting

BentoML by default collects anonymous usage data using Amplitude. It only collects BentoML library's own actions and parameters, no user or model data will be collected.  Here is the code that does it.

This helps the BentoML team to understand how the community is using this tool and what to build next. You can easily opt-out of usage tracking by running the BentoML commands with the --do-not-track option.

> bentoml [command] --do-not-track

You can also opt-out via setting environment variable BENTOML_DO_NOT_TRACK=True

> export BENTOML_DO_NOT_TRACK=True

Download Details:
Author: bentoml
Source Code: https://github.com/bentoml/BentoML
License: Apache-2.0 License

#tensorflow  #python #machine-learning #artificial-intelligence 

What is GEEK

Buddha Community

BentoML: Unified Model Serving Framework for Tensorflow
Adam Carter

Adam Carter

1614043320

Serving TensorFlow models with TensorFlow Serving

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.

📖 Introduction

Currently there are a lot of different solutions to serve ML models in production with the growth that **MLOps **is having nowadays as the standard procedure to work with ML models during all their lifecycle. Maybe the most popular one is TensorFlow Serving developed by TensorFlow so as to server their models in production environments.

This post is a guide on how to train, save, serve and use TensorFlow ML models in production environments. Along the GitHub repository linked to this post we will prepare and train a custom CNN model for image classification of The Simpsons Characters Data dataset, that will be later deployed using TensorFlow Serving.

So as to get a better understanding on all the process that is presented in this post, as a personal recommendation, you should read it while you check the resources available in the repository, as well as trying to reproduce it with the same or with a different TensorFlow model, as “practice makes the master”.

alvarobartt/serving-tensorflow-models

#deep-learning #tensorflow-serving #tensorflow

Condo Mark

Condo Mark

1602646888

Deployment of a TensorFlow model to Production using TensorFlow Serving

Learn step by step deployment of a TensorFlow model to Production using TensorFlow Serving.

You created a deep learning model using Tensorflow, fine-tuned the model for better accuracy and precision, and now want to deploy your model to production for users to use it to make predictions.

TensorFlow Serving allows you to

  • Easily manage multiple versions of your model, like an experimental or stable version.
  • Keep your server architecture and APIs the same
  • Dynamically discovers a new version of the TensorFlow flow model and serves it using (remote procedure protocol) using a consistent API structure
  • Consistent experience for all clients making inferences by centralizing the location of the model

The key components of TF Serving are

  • Servables: A Servable is an underlying object used by clients to perform computation or inference**. TensorFlow serving represents the deep learning models as one ore more Servables.
  • Loaders: Manage the lifecycle of the Servables as Servables cannot manage their own lifecycle. Loaders standardize the APIs for loading and unloading the Servables, independent of the specific learning algorithm.
  • Source: Finds and provides Servables and then supplies one Loader instance for each version of the servable.
  • Managers: Manage the full lifecycle of the servable: Loading the servable, Serving the servable, and Unloading the servable
  • TensorFlow Core: Manages lifecycle and metrics of the Servable by making the Loader and servable as opaque objects

#tensorflow-serving #deep-learning #mnist #tensorflow #windows-10

Martin Soit

Martin Soit

1603159159

How to Serve Different Model Versions using TensorFlow Serving

This article explains how to manage multiple models and multiple versions of the same model in TensorFlow Serving using configuration files along with a brief understanding of batching.

Image for post

You have TensorFlow deep learning models with different architectures or have trained your models with different hyperparameters and would like to test them locally or in production. The easiest way is to serve the models using a Model Server Config file.

A Model Server Configuration file is a protocol buffer file(protobuf), which is a language-neutral, platform-neutral extensible yet simple and faster way to serialize the structure data.

#deep-learning #python #tensorflow-serving #tensorflow

Dominic Feeney

Dominic Feeney

1648240140

BentoML: Unified Model Serving Framework for Tensorflow

Unified Model Serving Framework

BentoML is an open platform that simplifies ML model deployment and enables you to serve your models at production scale in minutes

👉 Pop into our Slack community! We're happy to help with any issue you face or even just to meet you and hear what you're working on :)

The BentoML version 1.0 is around the corner. For stable release version 0.13, see the 0.13-LTS branch. Version 1.0 is under active development, you can be of great help by testing out the preview release, reporting issues, contribute to the documentation and create sample gallery projects.

Why BentoML

  • The easiest way to turn your ML models into production-ready API endpoints.
  • High performance model serving, all in Python.
  • Standardize model packaging and ML service definition to streamline deployment.
  • Support all major machine-learning training frameworks.
  • Deploy and operate ML serving workload at scale on Kubernetes via Yatai.

Getting Started

  • Quickstart guide will show you a simple example of using BentoML in action. In under 10 minutes, you'll be able to serve your ML model over an HTTP API endpoint, and build a docker image that is ready to be deployed in production.
  • Main concepts will give a comprehensive tour of BentoML's components and introduce you to its philosophy. After reading, you will see what drives BentoML's design, and know what bento and runner stands for.
  • ML Frameworks lays out best practices and example usages by the ML framework used for training models.
  • Advanced Guides showcases advanced features in BentoML, including GPU support, inference graph, monitoring, and customizing docker environment etc.
  • Check out other projects from the BentoML team:

Community

Contributing

There are many ways to contribute to the project:

  • If you have any feedback on the project, share it with the community in Github Discussions of this project.
  • Report issues you're facing and "Thumbs up" on issues and feature requests that are relevant to you.
  • Investigate bugs and reviewing other developer's pull requests.
  • Contributing code or documentation to the project by submitting a Github pull request. See the development guide.
  • See more in the contributing guide.

Usage Reporting

BentoML by default collects anonymous usage data using Amplitude. It only collects BentoML library's own actions and parameters, no user or model data will be collected.  Here is the code that does it.

This helps the BentoML team to understand how the community is using this tool and what to build next. You can easily opt-out of usage tracking by running the BentoML commands with the --do-not-track option.

> bentoml [command] --do-not-track

You can also opt-out via setting environment variable BENTOML_DO_NOT_TRACK=True

> export BENTOML_DO_NOT_TRACK=True

Download Details:
Author: bentoml
Source Code: https://github.com/bentoml/BentoML
License: Apache-2.0 License

#tensorflow  #python #machine-learning #artificial-intelligence 

Uriah Dietrich

Uriah Dietrich

1616652120

Hands-On Guide to Model Search: A Tensorflow-based Framework for AutoML

When it comes to Deep Neural Network (DNNs), we are often confused about their architecture(like types of layers, number of layers, type of optimization, etc.) for a specific problem. This sudden template shift of using deep learning models for a various number of problems has made it even harder for researchers to design a new neural network and generalize it. In recent years, automated ML or AutoML has really helped researchers and developers to create high quality deep learning models without human intervention and to extend its usability, Google has developed a new framework called Model Search.

Model Search is an open-source, TensorFlow based python framework for building AutoML algorithms at a large scale. This framework allows :

  • To run many AutoML algorithms from the search of the right model architecture to the best-distilled models.
  • To compare different algorithms from search space.
  • To customize the neural network layers in search space.

#automl framework #google automl framework on tensorflow #noval neural architecture search #tensorflow based