AI News, How to publish and manage R web services in Machine Learning Server with mrsdeploy

How to publish and manage R web services in Machine Learning Server with mrsdeploy

Applies to: Machine Learning Server, Microsoft R Server 9.x This article details how you can publish and manage your analytic web services directly in R.

You can deploy your R models, scripts, and code as web services using the functions in the mrsdeploy R package.

Using the mrsdeploy R package, you can publish, update, and delete two kinds of R web services: standard R web services and realtime R web services.

Additionally, you can get a list of all services, retrieve a web service object for consumption, and share services with others.

You can also publish or interact with a web service outside of R using the RESTful APIs, which provide direct programmatic access to a service's lifecycle.

Before you can use the web service management functions in the mrsdeploy R package, you must: Any authenticated user can: While any authenticated user can also publish a web service by default, roles can be used to further control permissions.

Beginning in version 9.1, your administrator can also assign role-based authorization to further restrict the permissions around web services to give some users more control over web services than others.

We highly recommend a consistent and meaningful versioning convention across your organization or team such as semantic versioning.

Example of standard web service: For a full example, you can also follow the quickstart article "Deploying an R model as a web service."

However, unlike standard web services, you cannot specify the following for realtime web services: Realtime web services only accept model objects created with the supported functions from packages installed with the product.

The base path for files is set to your working directory, but you can change that using ServiceOption as follows: In this example, the code comes from an object (code = manualTransmission) and the model comes from a model object (model = carsModel).

In this example, the code comes from an object (code = manualTransmission) and the model comes from a model object (model = carsModel) as it was in example 1.

In this example, the local model object (model = kyphosisModel) is generated using the rxLogit modeling function in the RevoScaleR package.

To learn more about the supported model formats, supported product versions, and supported platforms for realtime web services, see here.

This Task View contains information about to use R and the world wide web together.

This task view focuses on packages for obtaining web-based data and information, frameworks for building web-based R applications, and online services that can be accessed from R.

provides further discussion of online data sources that can be accessed from R.

If you have any comments or suggestions for additions or improvements for this Task View, go to GitHub and

submit an issue

submit a pull request

send Thomas an email (thosjleeper at gmail dot com)

If you know of a web service, API, data source, or other online resource that is not yet supported by an R package, consider adding it to

the package development to do list on GitHub

There are two packages that should cover most use cases of interacting with the web from R. httr

provides a user-friendly interface for executing HTTP methods (GET, POST, PUT, HEAD, DELETE, etc.) and provides support for modern web authentication protocols (OAuth 1.0, OAuth 2.0).

is a lower-level package that provides a closer interface between R and the

libcurl C library

The vast majority of web-based data is structured as plain text, HTML, XML, or JSON (javascript object notation).

These functions can be used to interact directly with insecure webpages or can be used to parse locally stored or in-memory web files.

How to interact with and consume web services in R with mrsdeploy

Applies to: Machine Learning Server, Microsoft R Server 9.x After a web service has been published or updated, any authenticated user can list, examine, and consume that web service.

If you do not want to list, examine, or consume the web service in R, a set of RESTful APIs are also available to provide direct programmatic access to a service's lifecycle directly.

To list, examine, or consume the web service outside of R, use the RESTful APIs, which provide direct programmatic access to a service's lifecycle.

Any authenticated user can retrieve a list of web services using the listServices() function in the mrsdeploy package.

Use function arguments to return a specific web service or all labeled versions of a given web service.

To make it easy for others to find your service, provide them with the service name and version number (or they can use the listServices() function).

consumption approach, where users send as a single request to Machine Learning Server, which then makes multiple asynchronous API calls on your behalf.

Other data scientists may want to explore, test, and consume Web services directly in R using the functions in the mrsdeploy package.

You can share the name and version of a web service with fellow data scientists so they can call that service in R using the functions in the mrsdeploy package.

Application developers can call and integrate a web service into their applications using the service-specific Swagger-based JSON file and by providing any required inputs to that service.

Creating APIs in R with Plumber

The R Programming Language (R Core Team 2013) has become one of the most dominant programming languages for data analysis and visualization in recent years.

At the same time, web services have become a common language for allowing various systems to interact with one another.

The plumber R package (Trestle Technology, LLC 2017) allows users to expose existing R code as a service available to others on the Web.

How do I expose R code as a web service?

There are quite a few packages that will help you do this, but I can’t remember all their names off the top of my head (and I’m sure you’re just as good at googling as I am).

Here’s a snippet from the readme: You can then start up a server which will respond to json requests: That solves the easy part of the API problem: how to describe what parts of your R code should be exposed in the web API.

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