azureml-sdk-for-r | Azure Machine Learning SDK for R | Azure library

by Azure R Version: v1.10.0 License: Non-SPDX

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kandi X-RAY | azureml-sdk-for-r Summary

azureml-sdk-for-r is a R library typically used in Cloud, Azure applications. azureml-sdk-for-r has no bugs, it has no vulnerabilities and it has low support. However azureml-sdk-for-r has a Non-SPDX License. You can download it from GitHub.

Azure Machine Learning SDK for R

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Support

azureml-sdk-for-r has a low active ecosystem.

It has 90 star(s) with 32 fork(s). There are 16 watchers for this library.

It had no major release in the last 12 months.

There are 32 open issues and 125 have been closed. On average issues are closed in 26 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of azureml-sdk-for-r is v1.10.0

Quality

azureml-sdk-for-r has 0 bugs and 0 code smells.

Security

azureml-sdk-for-r has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

azureml-sdk-for-r code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

azureml-sdk-for-r has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

azureml-sdk-for-r releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 34859 lines of code, 0 functions and 194 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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azureml-sdk-for-r Key Features

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azureml-sdk-for-r Examples and Code Snippets

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Community Discussions

Trending Discussions on azureml-sdk-for-r

deploy model and expose model as web service via azure machine learning + azuremlsdk in R

conda dependencies file r model in azure machine learning

storing azure credentials best practice for azuremlsdk

QUESTION

deploy model and expose model as web service via azure machine learning + azuremlsdk in R

Asked 2021-May-14 at 16:02

I am trying to follow this post to deploy a "model" in Azure.

A code snipet is as follows and the model, which is simply a function adding 2 numbers, seems to register fine. I don't even use the model to isolate the problem after 1000s of attempts as this scoring code shows:

...

ANSWER

Answered 2021-May-14 at 15:53

Great to see people putting the R SDK through it's paces!

The vignette you're using is obviously a great way to get started. It seems you're almost all the way through without a hitch.

Deployment is always tricky, and I'm not expert myself. I'd point you to this guide on troubleshooting deployment locally. Similar functionality exists for the R SDK, namely: local_webservice_deployment_config().

So I think you change your example to this:

Source https://stackoverflow.com/questions/67535014

QUESTION

conda dependencies file r model in azure machine learning

Asked 2021-May-14 at 15:45

I understand what the entry script/scoring script is and does. See here as an example. As I struggle to expose my deployed model via code as described here (see also here), I am trying to use the UI ml.azure.com instead. I am a bit puzzled by the mandatory dependency: conda dependencies file:

I have an R model but clearly this is a Python thing. What shall I use in this case?

...

ANSWER

Answered 2021-May-14 at 15:45

conda is actually not just a Python thing, you might be thinking of pip?

Conda is a package & environment manager for nearly any kind of package, provided that it has been uploaded to anaconda. So you can use anaconda (and conda environment files) for R projects.

The trouble is that the azuremlsdk CRAN package is not hosted as an anaconda package, but is probably needed for the scoring service. Worth using a file like below to see what it works.

If it doesn't work, then I agree that this UI needs to generalized to better support R model deployment scenarios.

It is also possible to add the azuremlsdk CRAN package to anaconda, but that requires some extra work, but ideally you shouldn't have to require this much manual effort.

environment.yml

Here's an example conda dependencies file for R.

Source https://stackoverflow.com/questions/67536581

QUESTION

storing azure credentials best practice for azuremlsdk

Asked 2021-May-08 at 15:36

Looking at this the following creates a config.json file (I think):

...

ANSWER

Answered 2021-May-08 at 15:36

First, sorry if anything here seams inappropriate for your question since I do not know R.

If this is a project that won't be distributed (i.e to customers and be downloaded) I would save this data on an Environment Variable on you localhost or server and have all developers create a var as well. This will allow you to store all credentials and parameters without committing them.

This approach basically requires you to change the code which loads the credentials from the config file so it queries you localhost variables for the credentials. I found a nice guide on how to do that in R, check it here!

If this is a software that'll be distributed into production I would take a look at Azure Key Vault. This will allow you to safely store your secrets and get them when needed, authenticating with the user's account on an Azure AD or AD B2C. There's a nice guide here.

Best,

Felipe

Source https://stackoverflow.com/questions/67448564

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install azureml-sdk-for-r

Install Conda if not already installed. Choose Python 3.5 or later.
To begin running experiments with Azure Machine Learning, you must establish a connection to your Azure Machine Learning workspace. Once you've accessed your workspace, you can begin running and tracking your own experiments with Azure Machine Learning SDK for R.
If you don't already have a workspace created, you can create one by doing: # If you haven't already set up a resource group, set `create_resource_group = TRUE` # and set `resource_group` to your desired resource group name in order to create the resource group # in the same step. new_ws <- create_workspace(name = <workspace_name>, subscription_id = <subscription_id>, resource_group = <resource_group_name>, location = location, create_resource_group = FALSE) After the workspace is created, you can save it to a configuration file to the local machine. write_workspace_config(new_ws)
If you have an existing workspace associated with your subscription, you can retrieve it from the server by doing: existing_ws <- get_workspace(name = <workspace_name>, subscription_id = <subscription_id>, resource_group = <resource_group_name>) Or, if you have the workspace config.json file on your local machine, you can load the workspace by doing: loaded_ws <- load_workspace_from_config()