Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/azure/doazureparallel

A R package that allows users to submit parallel workloads in Azure
https://github.com/azure/doazureparallel

azure-batch cluster dsvm foreach mran parallel r

Last synced: 3 months ago
JSON representation

A R package that allows users to submit parallel workloads in Azure

Awesome Lists containing this project

README 

        
[![Build Status](https://travis-ci.org/Azure/doAzureParallel.svg?branch=master)](https://travis-ci.org/Azure/doAzureParallel)



# This repo is no longer maintained and no new features will be added.



## doAzureParallel



## Introduction



The *doAzureParallel* package is a parallel backend for the widely popular *foreach* package. With *doAzureParallel*, each iteration of the *foreach* loop runs in parallel on an Azure Virtual Machine (VM), allowing users to scale up their R jobs to tens or hundreds of machines.



*doAzureParallel* is built to support the *foreach* parallel computing package. The *foreach* package supports parallel execution - it can execute multiple processes across some parallel backend. With just a few lines of code, the *doAzureParallel* package helps create a cluster in Azure, register it as a parallel backend, and seamlessly connects to the *foreach* package.



NOTE: The terms *pool* and *cluster* are used interchangably throughout this document.



## Notable Features

- Ability to use low-priority VMs for an 80% discount [(link)](./docs/31-vm-sizes.md#low-priority-vms)

- Users can bring their own Docker Image

- AAD and VNets Support

- Built in support for Azure Blob Storage



## Dependencies



- R (>= 3.3.1)

- httr (>= 1.2.1)

- rjson (>= 0.2.15)

- RCurl (>= 1.95-4.8)

- digest (>= 0.6.9)

- foreach (>= 1.4.3)

- iterators (>= 1.0.8)

- bitops (>= 1.0.5)



## Setup 



1) Install doAzureParallel directly from Github.



```R

# install the package devtools

install.packages("devtools")



# install the doAzureParallel and rAzureBatch package

devtools::install_github("Azure/rAzureBatch")

devtools::install_github("Azure/doAzureParallel")

```



2) Create an doAzureParallel's credentials file

``` R

library(doAzureParallel)

generateCredentialsConfig("credentials.json")

```



3) Login or register for an Azure Account, navigate to [Azure Cloud Shell](https://shell.azure.com)



``` sh 

wget -q https://raw.githubusercontent.com/Azure/doAzureParallel/master/account_setup.sh &&

chmod 755 account_setup.sh &&

/bin/bash account_setup.sh

```

4) Follow the on screen prompts to create the necessary Azure resources and copy the output into your credentials file. For more information, see [Getting Started Scripts](./docs/02-getting-started-script.md).



To Learn More:

- [Azure Account Requirements for doAzureParallel](./docs/04-azure-requirements.md)



## Getting Started



Import the package

```R

library(doAzureParallel)

```



Set up your parallel backend with Azure. This is your set of Azure VMs.

```R

# 1. Generate your credential and cluster configuration files.  

generateClusterConfig("cluster.json")

generateCredentialsConfig("credentials.json")



# 2. Fill out your credential config and cluster config files.

# Enter your Azure Batch Account & Azure Storage keys/account-info into your credential config ("credentials.json") and configure your cluster in your cluster config ("cluster.json")



# 3. Set your credentials - you need to give the R session your credentials to interact with Azure

setCredentials("credentials.json")



# 4. Register the pool. This will create a new pool if your pool hasn't already been provisioned.

cluster <- makeCluster("cluster.json")



# 5. Register the pool as your parallel backend

registerDoAzureParallel(cluster)



# 6. Check that your parallel backend has been registered

getDoParWorkers()

```



Run your parallel *foreach* loop with the *%dopar%* keyword. The *foreach* function will return the results of your parallel code.



```R

number_of_iterations <- 10

results <- foreach(i = 1:number_of_iterations) %dopar% {

  # This code is executed, in parallel, across your cluster.

  myAlgorithm()

}

```



After you finish running your R code in Azure, you may want to shut down your cluster of VMs to make sure that you are not being charged anymore.



```R

# shut down your pool

stopCluster(cluster)

```



## Table of Contents 

This section will provide information about how Azure works, how best to take advantage of Azure, and best practices when using the doAzureParallel package.



1. **Azure Introduction** [(link)](./docs/00-azure-introduction.md)



   Using *Azure Batch*



2. **Getting Started** [(link)](./docs/01-getting-started.md)



    Using the *Getting Started* to create credentials

    

    i. **Generate Credentials Script** [(link)](./docs/02-getting-started-script.md)



    - Pre-built bash script for getting Azure credentials without Azure Portal



    ii. **National Cloud Support** [(link)](./docs/03-national-clouds.md)



    - How to run workload in Azure national clouds



3. **Customize Cluster** [(link)](./docs/30-customize-cluster.md)



    Setting up your cluster to user's specific needs



    i. **Virtual Machine Sizes** [(link)](./docs/31-vm-sizes.md)

    

    - How do you choose the best VM type/size for your workload?



    ii. **Autoscale** [(link)](./docs/32-autoscale.md)

  

    - Automatically scale up/down your cluster to save time and/or money.

  

    iii. **Building Containers** [(link)](./docs/33-building-containers.md)

    

      - Creating your own Docker containers for reproducibility



4. **Managing Cluster** [(link)](./docs/40-clusters.md)



    Managing your cluster's lifespan



5. **Customize Job**



    Setting up your job to user's specific needs

    

    i. **Asynchronous Jobs** [(link)](./docs/51-long-running-job.md)

    

    - Best practices for managing long running jobs

  

    ii. **Foreach Azure Options** [(link)](./docs/52-azure-foreach-options.md)

        

    - Use Azure package-defined foreach options to improve performance and user experience

  

    iii. **Error Handling** [(link)](./docs/53-error-handling.md)

    

    - How Azure handles errors in your Foreach loop? 

    

6. **Package Management** [(link)](./docs/20-package-management.md)



    Best practices for managing your R packages in code. This includes installation at the cluster or job level as well as how to use different package providers.



7. **Storage Management**

    

    i. **Distributing your Data** [(link)](./docs/71-distributing-data.md)

    

    - Best practices and limitations for working with distributed data.



    ii. **Persistent Storage** [(link)](./docs/72-persistent-storage.md)



    - Taking advantage of persistent storage for long-running jobs

   

    iii. **Accessing Azure Storage through R** [(link)](./docs/73-managing-storage.md)

    

    - Manage your Azure Storage files via R 



8. **Performance Tuning** [(link)](./docs/80-performance-tuning.md)



    Best practices on optimizing your Foreach loop



9. **Debugging and Troubleshooting** [(link)](./docs/90-troubleshooting.md)

    

    Best practices on diagnosing common issues



10. **Azure Limitations** [(link)](./docs/91-quota-limitations.md)



    Learn about the limitations around the size of your cluster and the number of foreach jobs you can run in Azure.

   

## Additional Documentation

Read our [**FAQ**](./docs/92-faq.md) for known issues and common questions.



## Next Steps



For more information, please visit [our documentation](./docs/README.md).