Address additional CRAN release warnings/notes (#133)

* Fix additional warnings from devtools check.

* address the cran release issues

* fix the code sytle issue

* fix code style issue

* Fix the code sytle issues

DESCRIPTION

@@ -11,7 +11,7 @@ Authors@R: c(
     )
 URL: https://github.com/azure/azureml-sdk-for-r
 BugReports: https://github.com/azure/azureml-sdk-for-r/issues
-Description: An R interface to the Azure Machine Learning service to build and run machine learning workflows.
+Description: An R interface to the Azure Machine Learning service to build and run machine learning workloads.
 Encoding: UTF-8
 License: MIT + file LICENSE
 RoxygenNote: 6.1.1

R/model.R

@@ -48,8 +48,8 @@ get_model <- function(workspace,
 #' you have a model that's stored in multiple files, you can register them
 #' as a single model in your workspace. After registration, you can then
 #' download or deploy the registered model and receive all the files that
-#' were registered.\cr
-#' \cr
+#' were registered.
+#'
 #' Models are identified by name and version. Each time you register a
 #' model with the same name as an existing one, your workspace's model
 #' registry assumes that it's a new version. The version is incremented,
@@ -230,8 +230,8 @@ deploy_model <- function(workspace,
 #' the model (for example, if you plan to deploy to Azure App Service). Or
 #' you might want to download the image and run it on a local Docker installation.
 #' You might even want to download the files used to build the image, inspect
-#' them, modify them, and build the image manually.\cr
-#' \cr
+#' them, modify them, and build the image manually.
+#'
 #' Model packaging enables you to do these things. `package_model()` packages all
 #' the assets needed to host a model as a web service and allows you to download
 #' either a fully built Docker image or the files needed to build one. There are
@@ -241,7 +241,7 @@ deploy_model <- function(workspace,
 #' * **Generate a Dockerfile**: Download the Dockerfile, model, entry script, and
 #' other assets needed to build a Docker image. You can then inspect the files or
 #' make changes before you build the image locally. To use this method, make sure
-#' to set `generate_dockerfile = TRUE`.\cr
+#' to set `generate_dockerfile = TRUE`.
 #' With either scenario, you will need to have Docker installed in your
 #' development environment.
 #' @param workspace The `Workspace` object.
@@ -300,7 +300,7 @@ package_model <- function(workspace,
 #' username <- container_registry$username
 #' password <- container_registry$password
 #' ```
-#' \cr
+#'
 #' To then authenticate Docker with the Azure container registry from
 #' a shell or command-line session, use the following command, replacing
 #' `<address>`, `<username>`, and `<password>` with the values retrieved
@@ -338,11 +338,11 @@ get_model_package_creation_logs <- function(package,
 #' Pull the Docker image from a created `ModelPackage` to your
 #' local Docker environment. The output of this call will
 #' display the name of the image. For example:
-#' `Status: Downloaded newer image for myworkspacef78fd10.azurecr.io/package:20190822181338`.\cr
-#' \cr
+#' `Status: Downloaded newer image for myworkspacef78fd10.azurecr.io/package:20190822181338`.
+#'
 #' This can only be used with a Docker image `ModelPackage` (where
-#' `package_model()` was called with `generate_dockerfile = FALSE`).\cr
-#' \cr
+#' `package_model()` was called with `generate_dockerfile = FALSE`).
+#'
 #' After you've pulled the image, you can start a local container based
 #' on this image using Docker commands.
 #' @param package The `ModelPackage` object.
@@ -358,12 +358,12 @@ pull_model_package_image <- function(package) {
 #' your local file system
 #' @description
 #' Download the Dockerfile, model, and other assets needed to build
-#' an image locally from a created `ModelPackage`.\cr
-#' \cr
+#' an image locally from a created `ModelPackage`.
+#'
 #' This can only be used with a Dockerfile `ModelPackage` (where
 #' `package_model()` was called with `generate_dockerfile = TRUE` to
-#' indicated that you wanted only the files and not a fully built image).\cr
-#' \cr
+#' indicated that you wanted only the files and not a fully built image).
+#'
 #' `save_model_package_files()` downloads the files needed to build the
 #' image to the `output_directory`. The Dockerfile included in the saved
 #' files references a base image stored in an Azure container registry.
@@ -423,8 +423,8 @@ wait_for_model_package_creation <- function(package, show_output = FALSE) {
 #' and the format of the data returned to clients. If the request data is in a
 #' format that is not usable by your model, the script can transform it into
 #' an acceptable format. It can also transform the response before returning
-#' it to the client.\cr
-#' \cr
+#' it to the client.
+#'
 #' The entry script must contain an `init()` method that loads your model and
 #' then returns a function that uses the model to make a prediction based on
 #' the input data passed to the function. Azure ML runs the `init()` method
@@ -432,25 +432,18 @@ wait_for_model_package_creation <- function(package, show_output = FALSE) {
 #' prediction function returned by `init()` will be run every time the service
 #' is invoked to make a prediction on some input data. The inputs and outputs
 #' of this prediction function typically use JSON for serialization and
-#' deserialization.\cr
-#' \cr
+#' deserialization.
+#'
 #' To locate the model in your entry script (when you load the model in the
 #' script's `init()` method), use `AZUREML_MODEL_DIR`, an environment variable
 #' containing the path to the model location. The environment variable is
 #' created during service deployment, and you can use it to find the location
-#' of your deployed model(s).\cr
-#' \cr
-#' The following table describes the value of `AZUREML_MODEL_DIR` depending
-#' on the number of models deployed:
-#' \tabular{rr}{
-#' **Deployment** \tab **Environment variable value**\cr
-#' Single model \tab The path to the folder containing the model\cr
-#' Multiple models \tab The path to the folder containing all models. Models are located by name and version in this folder (`$MODEL_NAME/$VERSION`)
-#' }
+#' of your deployed model(s).
+#'
 #' To get the path to a file in a model, combine the environment variable
 #' with the filename you're looking for. The filenames of the model files
-#' are preserved during registration and deployment.\cr
-#' \cr
+#' are preserved during registration and deployment.
+#'
 #' Single model example:
 #' ```
 #' model_path <- file.path(Sys.getenv("AZUREML_MODEL_DIR"), "my_model.rds")

R/webservice-aci.R

@@ -5,8 +5,8 @@
 #' @description
 #' Deploy a web service to Azure Container Instances for testing or
 #' debugging. Use ACI for low-scale CPU-based workloads that
-#' require less than 48 GB of RAM.\cr
-#' \cr
+#' require less than 48 GB of RAM.
+#'
 #' Deploy to ACI if one of the following conditions is true:
 #' * You need to quickly deploy and validate your model. You do not need
 #' to create ACI containers ahead of time. They are created as part of
@@ -80,8 +80,8 @@ aci_webservice_deployment_config <- function(cpu_cores = NULL,
 #' @description
 #' Update an ACI web service with the provided properties. You can update the
 #' web service to use a new model, a new entry script, or new dependencies
-#' that can be specified in an inference configuration.\cr
-#' \cr
+#' that can be specified in an inference configuration.
+#'
 #' Values left as `NULL` will remain unchanged in the web service.
 #' @param webservice The `AciWebservice` object.
 #' @param tags A named list of key-value tags for the web service,

R/webservice-aks.R

@@ -6,8 +6,8 @@
 #' Deploy a web service to Azure Kubernetes Service for high-scale
 #' prodution deployments. Provides fast response time and autoscaling
 #' of the deployed service. Using GPU for inference when deployed as a
-#' web service is only supported on AKS.\cr
-#' \cr
+#' web service is only supported on AKS.
+#'
 #' Deploy to AKS if you need one or more of the following capabilities:
 #' * Fast response time
 #' * Autoscaling of the deployed service
@@ -79,8 +79,8 @@
 #' When deploying to AKS, you deploy to an AKS cluster that is connected to your workspace.
 #' There are two ways to connect an AKS cluster to your workspace:
 #' * Create the AKS cluster using Azure ML (see `create_aks_compute()`).
-#' * Attach an existing AKS cluster to your workspace (see `attach_aks_compute()`).\cr
-#' \cr
+#' * Attach an existing AKS cluster to your workspace (see `attach_aks_compute()`).
+#'
 #' Pass the `AksCompute` object to the `deployment_target` parameter of `deploy_model()`.
 #' }
 #' \subsection{Token-based authentication}{
@@ -161,8 +161,8 @@ aks_webservice_deployment_config <- function(
 #' @description
 #' Update an AKS web service with the provided properties. You can update the
 #' web service to use a new model, a new entry script, or new dependencies
-#' that can be specified in an inference configuration.\cr
-#' \cr
+#' that can be specified in an inference configuration.
+#'
 #' Values left as `NULL` will remain unchanged in the web service.
 #' @param webservice The `AksWebservice` object.
 #' @param autoscale_enabled If `TRUE` enable autoscaling for the web service.

R/webservice-local.R

@@ -4,8 +4,8 @@
 #' Create a deployment config for deploying a local web service
 #' @description
 #' You can deploy a model locally for limited testing and troubleshooting.
-#' To do so, you will need to have Docker installed on your local machine.\cr
-#' \cr
+#' To do so, you will need to have Docker installed on your local machine.
+#'
 #' If you are using an Azure Machine Learning Compute Instance for
 #' development, you can also deploy locally on your compute instance.
 #' @param port An int of the local port on which to expose the service's
@@ -26,8 +26,8 @@ local_webservice_deployment_config <- function(port = NULL) {
 #' @description
 #' Update a local web service with the provided properties. You can update the
 #' web service to use a new model, a new entry script, or new dependencies
-#' that can be specified in an inference configuration.\cr
-#' \cr
+#' that can be specified in an inference configuration.
+#'
 #' Values left as `NULL` will remain unchanged in the service.
 #' @param webservice The `LocalWebservice` object.
 #' @param models A list of `Model` objects to package into the updated service.

R/webservice.R

@@ -19,8 +19,8 @@ get_webservice <- function(workspace, name) {
 #' @description
 #' Automatically poll on the running web service deployment and
 #' wait for the web service to reach a terminal state. Will throw
-#' an exception if it reaches a non-successful terminal state.\cr
-#' \cr
+#' an exception if it reaches a non-successful terminal state.
+#'
 #' Typically called after running `deploy_model()`.
 #' @param webservice The `LocalWebservice`, `AciWebservice`, or
 #' `AksWebservice` object.
@@ -40,8 +40,8 @@ wait_for_deployment <- function(webservice, show_output = FALSE) {
 #' @description
 #' You can get the detailed Docker engine log messages from your
 #' web service deployment. You can view the logs for local, ACI,
-#' and AKS deployments.\cr
-#' \cr
+#' and AKS deployments.
+#'
 #' For example, if your web service deployment fails, you can
 #' inspect the logs to help troubleshoot.
 #' @param webservice The `LocalWebservice`, `AciWebservice`, or
@@ -65,12 +65,12 @@ get_webservice_logs <- function(webservice, num_lines = 5000L) {
 #' `aks_webservice_deployment_config()` for creation and
 #' `update_aci_webservice()` or `update_aks_webservice()` for updating).
 #' Note that key-based auth is enabled by default for `AksWebservice`
-#' but not for `AciWebservice`.\cr
-#' \cr
+#' but not for `AciWebservice`.
+#'
 #' To check if a web service has key-based auth enabled, you can
 #' access the following boolean property from the Webservice object:
-#' `service$auth_enabled`\cr
-#' \cr
+#' `service$auth_enabled`
+#'
 #' Not supported for `LocalWebservice` deployments.
 #' @param webservice The `AciWebservice` or `AksWebservice` object.
 #' @return A list of two strings corresponding to the primary and
@@ -87,8 +87,8 @@ get_webservice_keys <- function(webservice) {
 #' @description
 #' Delete a deployed ACI or AKS web service from the given workspace.
 #' This function call is not asynchronous; it runs until the resource is
-#' deleted.\cr
-#' \cr
+#' deleted.
+#'
 #' To delete a `LocalWebservice` see `delete_local_webservice()`.
 #' @param webservice The `AciWebservice` or `AksWebservice` object.
 #' @export
@@ -119,8 +119,8 @@ delete_webservice <- function(webservice) {
 #' a service key as a token in your request header
 #' (see `get_webservice_keys()`). If you've enabled token-based
 #' authentication, you will need to provide an JWT token as a bearer
-#' token in your request header (see `get_webservice_token()`).\cr
-#' \cr
+#' token in your request header (see `get_webservice_token()`).
+#'
 #' To get the REST API address for the service's scoring endpoint, you can
 #' access the following property from the Webservice object:
 #' `service$scoring_uri`
@@ -133,8 +133,8 @@ invoke_webservice <- function(webservice, input_data) {
 #' @description
 #' Regenerate either the primary or secondary authentication key for
 #' an `AciWebservice` or `AksWebservice`.The web service must have
-#' been deployed with key-based authentication enabled.\cr
-#' \cr
+#' been deployed with key-based authentication enabled.
+#'
 #' Not supported for `LocalWebservice` deployments.
 #' @param webservice The `AciWebservice` or `AksWebservice` object.
 #' @param key_type A string of which key to regenerate. Options are
@@ -153,15 +153,15 @@ generate_new_webservice_key <- function(webservice, key_type) {
 #' enabled. Token-based authentication requires clients to use an Azure
 #' Active Directory account to request an authentication token, which is
 #' used to make requests to the deployed service. Only available for
-#' AKS deployments.\cr
-#' \cr
+#' AKS deployments.
+#'
 #' In order to enable token-based authentication, set the
 #' `token_auth_enabled = TRUE` parameter when you are creating or
 #' updating a deployment (`aks_webservice_deployment_config()` for creation
 #' or `update_aks_webservice()` for updating). Note that you cannot have both
 #' key-based authentication and token-based authentication enabled.
-#' Token-based authentication is not enabled by default.\cr
-#' \cr
+#' Token-based authentication is not enabled by default.
+#'
 #' To check if a web service has token-based auth enabled, you can
 #' access the following boolean property from the Webservice object:
 #' `service$token_auth_enabled`

README.md

@@ -87,14 +87,14 @@ To begin running experiments with Azure Machine Learning, you must establish a c
 	```
 Once you've accessed your workspace, you can begin running and tracking your own experiments with Azure Machine Learning SDK for R.
 
-Take a look at our [code samples](samples/) and [end-to-end vignettes](vignettes/) for examples of what's possible with the SDK!
+Take a look at our [code samples](https://github.com/Azure/azureml-sdk-for-r/tree/master/samples) and [end-to-end vignettes](https://github.com/Azure/azureml-sdk-for-r/tree/master/vignettes) for examples of what's possible with the SDK!
  
 ## Resources
 * R SDK package documentation: https://azure.github.io/azureml-sdk-for-r/reference/index.html
 * Azure Machine Learning service: https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml
 
 ## Contribute
-We welcome contributions from the community. If you would like to contribute to the repository, please refer to the [contribution guide](CONTRIBUTING.md).
+We welcome contributions from the community. If you would like to contribute to the repository, please refer to the [contribution guide](https://github.com/Azure/azureml-sdk-for-r/blob/master/CONTRIBUTING.md).
 
 ## Code of Conduct
 This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

man/aci_webservice_deployment_config.Rd

@@ -56,8 +56,8 @@ The \code{AciServiceDeploymentConfiguration} object.
 \description{
 Deploy a web service to Azure Container Instances for testing or
 debugging. Use ACI for low-scale CPU-based workloads that
-require less than 48 GB of RAM.\cr
-\cr
+require less than 48 GB of RAM.
+
 Deploy to ACI if one of the following conditions is true:
 \itemize{
 \item You need to quickly deploy and validate your model. You do not need

man/aks_webservice_deployment_config.Rd

@@ -112,8 +112,8 @@ The \code{AksServiceDeploymentConfiguration} object.
 Deploy a web service to Azure Kubernetes Service for high-scale
 prodution deployments. Provides fast response time and autoscaling
 of the deployed service. Using GPU for inference when deployed as a
-web service is only supported on AKS.\cr
-\cr
+web service is only supported on AKS.
+
 Deploy to AKS if you need one or more of the following capabilities:
 \itemize{
 \item Fast response time
@@ -127,8 +127,9 @@ When deploying to AKS, you deploy to an AKS cluster that is connected to your wo
 There are two ways to connect an AKS cluster to your workspace:
 \itemize{
 \item Create the AKS cluster using Azure ML (see \code{create_aks_compute()}).
-\item Attach an existing AKS cluster to your workspace (see \code{attach_aks_compute()}).\cr
-\cr
+\item Attach an existing AKS cluster to your workspace (see \code{attach_aks_compute()}).
+}
+
 Pass the \code{AksCompute} object to the \code{deployment_target} parameter of \code{deploy_model()}.
 }
 \subsection{Token-based authentication}{
@@ -141,7 +142,6 @@ workspace's region is available again. In addition, the greater the distance bet
 cluster's region and your workspace's region, the longer it will take to fetch a token.
 }
 }
-}
 \section{Examples}{
 \preformatted{deployment_config <- aks_webservice_deployment_config(cpu_cores = 1, memory_gb = 1)
 }

man/delete_webservice.Rd

@@ -12,7 +12,7 @@ delete_webservice(webservice)
 \description{
 Delete a deployed ACI or AKS web service from the given workspace.
 This function call is not asynchronous; it runs until the resource is
-deleted.\cr
-\cr
+deleted.
+
 To delete a \code{LocalWebservice} see \code{delete_local_webservice()}.
 }

man/generate_new_webservice_key.Rd

@@ -15,7 +15,7 @@ generate_new_webservice_key(webservice, key_type)
 \description{
 Regenerate either the primary or secondary authentication key for
 an \code{AciWebservice} or \code{AksWebservice}.The web service must have
-been deployed with key-based authentication enabled.\cr
-\cr
+been deployed with key-based authentication enabled.
+
 Not supported for \code{LocalWebservice} deployments.
 }

man/get_model_package_container_registry.Rd

@@ -26,7 +26,6 @@ username <- container_registry$username
 password <- container_registry$password
 }
 
-\cr
 To then authenticate Docker with the Azure container registry from
 a shell or command-line session, use the following command, replacing
 \code{<address>}, \code{<username>}, and \code{<password>} with the values retrieved

man/get_webservice_keys.Rd

@@ -22,12 +22,12 @@ when you are creating or updating a deployment (either
 \code{aks_webservice_deployment_config()} for creation and
 \code{update_aci_webservice()} or \code{update_aks_webservice()} for updating).
 Note that key-based auth is enabled by default for \code{AksWebservice}
-but not for \code{AciWebservice}.\cr
-\cr
+but not for \code{AciWebservice}.
+
 To check if a web service has key-based auth enabled, you can
 access the following boolean property from the Webservice object:
-\code{service$auth_enabled}\cr
-\cr
+\code{service$auth_enabled}
+
 Not supported for \code{LocalWebservice} deployments.
 }
 \seealso{

man/get_webservice_logs.Rd

@@ -19,8 +19,8 @@ A string of the logs for the web service.
 \description{
 You can get the detailed Docker engine log messages from your
 web service deployment. You can view the logs for local, ACI,
-and AKS deployments.\cr
-\cr
+and AKS deployments.
+
 For example, if your web service deployment fails, you can
 inspect the logs to help troubleshoot.
 }

man/get_webservice_token.Rd

@@ -21,15 +21,15 @@ for a web service that was deployed with token-based authentication
 enabled. Token-based authentication requires clients to use an Azure
 Active Directory account to request an authentication token, which is
 used to make requests to the deployed service. Only available for
-AKS deployments.\cr
-\cr
+AKS deployments.
+
 In order to enable token-based authentication, set the
 \code{token_auth_enabled = TRUE} parameter when you are creating or
 updating a deployment (\code{aks_webservice_deployment_config()} for creation
 or \code{update_aks_webservice()} for updating). Note that you cannot have both
 key-based authentication and token-based authentication enabled.
-Token-based authentication is not enabled by default.\cr
-\cr
+Token-based authentication is not enabled by default.
+
 To check if a web service has token-based auth enabled, you can
 access the following boolean property from the Webservice object:
 \code{service$token_auth_enabled}

man/inference_config.Rd

@@ -41,8 +41,8 @@ the incoming request data, the format of the data expected by your model,
 and the format of the data returned to clients. If the request data is in a
 format that is not usable by your model, the script can transform it into
 an acceptable format. It can also transform the response before returning
-it to the client.\cr
-\cr
+it to the client.
+
 The entry script must contain an \code{init()} method that loads your model and
 then returns a function that uses the model to make a prediction based on
 the input data passed to the function. Azure ML runs the \code{init()} method
@@ -50,25 +50,18 @@ once, when the Docker container for your web service is started. The
 prediction function returned by \code{init()} will be run every time the service
 is invoked to make a prediction on some input data. The inputs and outputs
 of this prediction function typically use JSON for serialization and
-deserialization.\cr
-\cr
+deserialization.
+
 To locate the model in your entry script (when you load the model in the
 script's \code{init()} method), use \code{AZUREML_MODEL_DIR}, an environment variable
 containing the path to the model location. The environment variable is
 created during service deployment, and you can use it to find the location
-of your deployed model(s).\cr
-\cr
-The following table describes the value of \code{AZUREML_MODEL_DIR} depending
-on the number of models deployed:
-\tabular{rr}{
-\strong{Deployment} \tab \strong{Environment variable value}\cr
-Single model \tab The path to the folder containing the model\cr
-Multiple models \tab The path to the folder containing all models. Models are located by name and version in this folder (\code{$MODEL_NAME/$VERSION})
-}
+of your deployed model(s).
+
 To get the path to a file in a model, combine the environment variable
 with the filename you're looking for. The filenames of the model files
-are preserved during registration and deployment.\cr
-\cr
+are preserved during registration and deployment.
+
 Single model example:\preformatted{model_path <- file.path(Sys.getenv("AZUREML_MODEL_DIR"), "my_model.rds")
 }
 

man/invoke_webservice.Rd

@@ -31,8 +31,8 @@ enabled key-based authentication for your service, you will need to provide
 a service key as a token in your request header
 (see \code{get_webservice_keys()}). If you've enabled token-based
 authentication, you will need to provide an JWT token as a bearer
-token in your request header (see \code{get_webservice_token()}).\cr
-\cr
+token in your request header (see \code{get_webservice_token()}).
+
 To get the REST API address for the service's scoring endpoint, you can
 access the following property from the Webservice object:
 \code{service$scoring_uri}

man/local_webservice_deployment_config.Rd

@@ -15,8 +15,8 @@ The \code{LocalWebserviceDeploymentConfiguration} object.
 }
 \description{
 You can deploy a model locally for limited testing and troubleshooting.
-To do so, you will need to have Docker installed on your local machine.\cr
-\cr
+To do so, you will need to have Docker installed on your local machine.
+
 If you are using an Azure Machine Learning Compute Instance for
 development, you can also deploy locally on your compute instance.
 }

man/package_model.Rd

@@ -28,8 +28,8 @@ In some cases, you might want to create a Docker image without deploying
 the model (for example, if you plan to deploy to Azure App Service). Or
 you might want to download the image and run it on a local Docker installation.
 You might even want to download the files used to build the image, inspect
-them, modify them, and build the image manually.\cr
-\cr
+them, modify them, and build the image manually.
+
 Model packaging enables you to do these things. \code{package_model()} packages all
 the assets needed to host a model as a web service and allows you to download
 either a fully built Docker image or the files needed to build one. There are
@@ -40,7 +40,7 @@ and other files needed to host it as a web service.
 \item \strong{Generate a Dockerfile}: Download the Dockerfile, model, entry script, and
 other assets needed to build a Docker image. You can then inspect the files or
 make changes before you build the image locally. To use this method, make sure
-to set \code{generate_dockerfile = TRUE}.\cr
+to set \code{generate_dockerfile = TRUE}.
 With either scenario, you will need to have Docker installed in your
 development environment.
 }

man/pull_model_package_image.Rd

@@ -14,11 +14,11 @@ pull_model_package_image(package)
 Pull the Docker image from a created \code{ModelPackage} to your
 local Docker environment. The output of this call will
 display the name of the image. For example:
-\code{Status: Downloaded newer image for myworkspacef78fd10.azurecr.io/package:20190822181338}.\cr
-\cr
+\code{Status: Downloaded newer image for myworkspacef78fd10.azurecr.io/package:20190822181338}.
+
 This can only be used with a Docker image \code{ModelPackage} (where
-\code{package_model()} was called with \code{generate_dockerfile = FALSE}).\cr
-\cr
+\code{package_model()} was called with \code{generate_dockerfile = FALSE}).
+
 After you've pulled the image, you can start a local container based
 on this image using Docker commands.
 }

man/register_model.Rd

@@ -40,8 +40,8 @@ container for one or more files that make up your model. For example, if
 you have a model that's stored in multiple files, you can register them
 as a single model in your workspace. After registration, you can then
 download or deploy the registered model and receive all the files that
-were registered.\cr
-\cr
+were registered.
+
 Models are identified by name and version. Each time you register a
 model with the same name as an existing one, your workspace's model
 registry assumes that it's a new version. The version is incremented,

man/save_model_package_files.Rd

@@ -15,12 +15,12 @@ will be created to contain the contents of the package.}
 }
 \description{
 Download the Dockerfile, model, and other assets needed to build
-an image locally from a created \code{ModelPackage}.\cr
-\cr
+an image locally from a created \code{ModelPackage}.
+
 This can only be used with a Dockerfile \code{ModelPackage} (where
 \code{package_model()} was called with \code{generate_dockerfile = TRUE} to
-indicated that you wanted only the files and not a fully built image).\cr
-\cr
+indicated that you wanted only the files and not a fully built image).
+
 \code{save_model_package_files()} downloads the files needed to build the
 image to the \code{output_directory}. The Dockerfile included in the saved
 files references a base image stored in an Azure container registry.

man/update_aci_webservice.Rd

@@ -41,8 +41,8 @@ web service.}
 \description{
 Update an ACI web service with the provided properties. You can update the
 web service to use a new model, a new entry script, or new dependencies
-that can be specified in an inference configuration.\cr
-\cr
+that can be specified in an inference configuration.
+
 Values left as \code{NULL} will remain unchanged in the web service.
 }
 \section{Examples}{

man/update_aks_webservice.Rd

@@ -104,7 +104,7 @@ Both \code{token_auth_enabled} and \code{auth_enabled} cannot be set to \code{TR
 \description{
 Update an AKS web service with the provided properties. You can update the
 web service to use a new model, a new entry script, or new dependencies
-that can be specified in an inference configuration.\cr
-\cr
+that can be specified in an inference configuration.
+
 Values left as \code{NULL} will remain unchanged in the web service.
 }

man/update_local_webservice.Rd

@@ -23,7 +23,7 @@ healthy state. Defaults to \code{FALSE}.}
 \description{
 Update a local web service with the provided properties. You can update the
 web service to use a new model, a new entry script, or new dependencies
-that can be specified in an inference configuration.\cr
-\cr
+that can be specified in an inference configuration.
+
 Values left as \code{NULL} will remain unchanged in the service.
 }

man/wait_for_deployment.Rd

@@ -16,8 +16,8 @@ to \code{FALSE}.}
 \description{
 Automatically poll on the running web service deployment and
 wait for the web service to reach a terminal state. Will throw
-an exception if it reaches a non-successful terminal state.\cr
-\cr
+an exception if it reaches a non-successful terminal state.
+
 Typically called after running \code{deploy_model()}.
 }
 \section{Examples}{