How to upload an R package to Azure Machine Learning

 

Introduction -

In the ever-evolving landscape of data science and machine learning, harnessing the power of cloud platforms has become essential. Azure Machine Learning (ML) is a Microsoft's cloud based machine learning service which provides a robust environment for developing, deploying, and managing machine learning models. If you are working with R and have developed a custom package then integrating it into Azure Machine Learning can streamline your workflow and enhance collaboration. In this article we will walk through the process of uploading an R package to Azure Machine Learning, that will enable you to leverage the platform's scalable computing resources and collaborative features.

Concepts -

Following terminologies are crucial for uploading package into Azure ML. So, let's first understand the key concepts related to uploading an R package to Azure ML:

  • Azure Machine Learning Workspace: It is the central hub for your machine learning operations in Azure which also provides a collaborative environment for the teams.
  • Azure ML SDK for R: It is a package that enables R users to interact with Azure ML services. It also facilitates workspace connection, experiment management, and resource provisioning.
  • R Package Preparation: Ensure that your R package is well-documented, includes dependencies and is ready for the deployment.
  • Compute Targets: They are virtual machines or clusters where computations are executed. AzureML supports various compute targets, enabling scalable and efficient processing.
  • Uploading to Azure ML Workspace: Use "upload_to_workspace" to transfer your R package to the AzureML workspace. Files are stored centrally for easy access during experiments.
  • AzureML Experiment: It represents a collection of trials or runs. It can also be useful in organizing and managing different iterations of your machine learning work.
  • Script Execution: We can run our R script on AzureML using "submit_experiment" by specifying the experiment details, compute target, script location, and required packages.

Advantages -

  • Integration with Azure Cloud: Leverage the scalability and resources of Azure for machine learning tasks. With integrating to azure we will get benefit of cloud-based infrastructure and services.
  • Dependency Management: Specify package dependencies in your R script or experiment configuration. Ensures a consistent environment for script execution.
  • Scaling with AzureML: Easily scale your computations based on workload demands. Choose appropriate compute resources for efficient processing.
  • Monitoring and Logging: AzureML provides monitoring tools to track experiment runs and resource utilization. Access logs and metrics to analyze and optimize your machine learning workflows.

Steps -

By following these general steps, you will successfully upload your R package to Azure Machine Learning, enabling seamless integration of your R-based machine learning workflows with the power of Azure's cloud infrastructure. Whether you're a data scientist, researcher, student or developer, this step-by-step tutorial will help you seamlessly integrate your R package into the Azure Machine Learning ecosystem. So, let's start:

  1. Prepare your R Package - First, ensure that your R package is well-documented, has the necessary dependencies listed, and includes all the required files.
  2. Create an Azure Machine Learning Workspace - Second, set up an Azure ML workspace in the Azure portal if you haven't already.
  3. Install AzureML SDK in R - Third step is to install Software Development Kit (SDK) of Azure ML.
  4. Connect to your Workspace - Next, connect with the specific workspace according to your particular azure configuration.
  5. Create or Retrieve AzureML Compute - Then comes Azure ML Compute which we must create or retrieve for running our code.
  6. Upload the R package - Finally, upload an R package to Azure.
  7. Create an Experiment
  8. Run the experiment
  9. Monitor the Run
  10. Review Results

Example -

Let's walk through a simplified example of uploading an R package to Azure ML. Assume you have a package named "myMLpackage" containing a predictive model. Now we will follow the above steps for this example. This example covers the entire process, from connecting to your AzureML workspace to submitting an experiment run that uses your uploaded R package.

Note - You should replace placeholders such as workspace_name, subscription_id, resource_group, compute_name, path_to_package, experiment_name, script_name and others with your actual values according to what you have written during particular Azure configuration. Also, adjust file paths based on your local structure.

This simple walkthrough example demonstrates the core steps of uploading an R package to Azure Machine Learning.

1. Prepare your R package:

Ensure your myMLpackage (R package) is well-documented and includes all necessary files. Also verify that dependencies are listed appropriately.

Package structure should be:

myMLpackage/
├── R/
│   ├── model_script.R
└── DESCRIPTION


2. Create an Azure ML Workspace:

Set up an Azure Machine Learning workspace by clicking on "Create" for creating a new workspace in the Azure portal.



creating-workspace-screenshot

Create Azure Machine Learning Workspace for Uploading an R package



3. Install Azure ML Software Development Kit (SDK) in R:

Install the "azuremlsdk" package in your R environment using "install.packages("azuremlsdk")".

You can do this step using the following command. The output will be the successful installation of the AzureML SDK package.

An error was thrown.



4. Connect to your Workspace:

Use the "workspace" function to connect to your Azure ML workspace, providing the necessary details like the workspace name, subscription ID, and resource group.

Use the following code to connect to your Azure ML workspace. Remember to replace the placeholders (workspace_name, subscription_id, and resource_group) with your defined actual values. The output here will be the connection to your AzureML workspace.

An error was thrown.



5. Create or Retrieve AzureML Compute:

Define a compute target using the "compute" function, specifying the compute name and other relevant details.

You will need a compute target to run your R script. You can create a new compute instance or use an existing one. Again, replace compute_name with the your appropriate compute target.

An error was thrown.



6. Upload the R package:

Utilize "upload_to_workspace" to upload your R package to the AzureML workspace. Specify the file path and set the "overwrite" parameter if needed.

Upload your R package to the Azure ML workspace using the following code. Make sure to specify the path to your R package tar.gz file. The output will be the successful upload of your R package to the workspace.

An error was thrown.



7. Create an Experiment:

Use the "experiment" function to create a new experiment in your workspace, providing a descriptive name.

Create an experiment using the below code. The output of this step will be the creation of the experiment.

An error was thrown.



8. Run the Experiment:

Submit an experiment using "submit_experiment", specifying the experiment, R script, compute target, source directory, and any required packages.

The output for this step will be the configuration for running the script and submission of the experiment run.

An error was thrown.



9. Monitor the Run:

In this step you have to just keep an eye on the Azure portal to monitor the progress of your experiment run.

10. Review Results:

After the run is complete, if your R script produces metrics or outputs, they should be visible in the run details. You can review the results, logs, and output generated by your R script.

Below are the additional features available in Azure ML which we can utilize after uploading an R package:

  • Run Details: In the Azure portal, navigate to your experiment. Click on a specific run to see detailed information.
  • Run Logs: Look for logs generated by your R script. These may include print statements, warnings, or errors. Logs help troubleshoot and understand the execution flow.
  • Metrics and Outputs: If your R script produces metrics or outputs, they should be visible in the run details. This could include model performance metrics, data visualizations, or any other relevant results.
  • Artifact Store: Check for any artifacts saved during the run. This might include model files, plots, or other artifacts specified in your R script.
  • Compute Metrics: Monitor metrics related to the compute target, such as resource utilization. This information helps optimize performance and resource allocation.

Summary -

Uploading an R package to Azure Machine Learning is a process that lets you use your R code and packages in the cloud. Follow these simple steps:

  • 1. Make sure your R package is well-prepared with all the needed files.
  • 2. Create an Azure Machine Learning workspace in the Azure portal.
  • 3. Install the "azuremlsdk" package in R.
  • 4. Use R to connect to your Azure Machine Learning workspace.
  • 5. Decide on the machine in Azure where your R code will run. (Choose Compute)
  • 6. Upload your R package to your Azure workspace.
  • 7. Create an experiment, run it, and your R code will now work in Azure.

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