https://github.com/rstudio/tfdeploy

tfdeploy: Deploy Tensorflow Models from R
https://github.com/rstudio/tfdeploy

deployment keras r rest tensorflow

Last synced: about 2 months ago
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tfdeploy: Deploy Tensorflow Models from R

• Host: GitHub
• URL: https://github.com/rstudio/tfdeploy
• Owner: rstudio
• Created: 2017-10-02T20:54:00.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2021-11-22T19:11:36.000Z (about 3 years ago)
• Last Synced: 2024-08-03T22:04:30.419Z (5 months ago)
• Topics: deployment, keras, r, rest, tensorflow
• Language: R
• Homepage: https://tensorflow.rstudio.com/tools/tfdeploy/
• Size: 29.8 MB
• Stars: 23
• Watchers: 16
• Forks: 12
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

        

## Deploying TensorFlow Models from R

[![Build

Status](https://travis-ci.org/rstudio/tfdeploy.svg?branch=master)](https://travis-ci.org/rstudio/tfdeploy)

[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/tfdeploy)](https://cran.r-project.org/package=tfdeploy)

[![codecov](https://codecov.io/gh/rstudio/tfdeploy/branch/master/graph/badge.svg)](https://codecov.io/gh/rstudio/tfdeploy)

While TensorFlow models are typically defined and trained using R or Python code, it is possible to deploy TensorFlow models in a wide variety of environments without any runtime dependency on R or Python:

- [TensorFlow Serving](https://www.tensorflow.org/serving/) is an open-source software library for serving TensorFlow models using a [gRPC](https://grpc.io/) interface.

- [CloudML](https://tensorflow.rstudio.com/tools/cloudml/) is a managed cloud service that serves TensorFlow models using a [REST](https://cloud.google.com/ml-engine/reference/rest/v1/projects/predict) interface.

- [RStudio Connect](https://www.rstudio.com/products/connect/) provides support for serving models using the same REST API as CloudML, but on a server within your own organization.

TensorFlow models can also be deployed to [mobile](https://www.tensorflow.org/mobile/tflite/) and [embedded](https://aws.amazon.com/blogs/machine-learning/how-to-deploy-deep-learning-models-with-aws-lambda-and-tensorflow/) devices including iOS and Android mobile phones and Raspberry Pi computers.

The tfdeploy package includes a variety of tools designed to make exporting and serving TensorFlow models straightforward. For documentation on using tfdeploy, see the package website at .