AzureML | R interface to AzureML | Azure library

we don’t have a good way to extend the handling for generic Python class objects. However, we are planning to add support for that, basically by providing more information on the necessary hooks, and allowing users to extend a base class to implement the hook to match the desired class structure. These types are currently supported:

pandas numpy pyspark Standard Python object

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-advanced-entry-script#automatically-generate-a-swagger-schema

Try this :

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:

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.

Here's an example conda dependencies file for R.

The ScriptRunConfig class now accepts a docker_runtime_config argument, which is where you pass the DockerConfiguration object.

So, the code would look something like this:

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

Apparently you need to specify a local mount path to use side_inputs in more than one node:

Datasets definitely support multiple files, so your problem is almost certainly in the permissions given when creating "mydatastore" datastore (I suspect you have used SAS token to create this datastore). In order to be able to access anything but individual files you need to give list permissions to the datastore. This would not be a problem if you register datastore with account key, but could be a limitation of the access token. The second part of the provided path is not valid or the files could not be accessed refers to potential permission issues. You can also verify that folder/** syntax works by creating dataset from defaultblobstore that was provisioned for you with your ml workspace.

To consume this from the AZ ML CLI we use the following syntax: