Skip to content
master
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
R
 
 
 
 
 
 
 
 
man
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Deploying TensorFlow Models from R

Build Status CRAN_Status_Badge codecov

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 is an open-source software library for serving TensorFlow models using a gRPC interface.

  • CloudML is a managed cloud service that serves TensorFlow models using a REST interface.

  • RStudio 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 and embedded 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 https://tensorflow.rstudio.com/tools/tfdeploy/.