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00-azure-introduction.md | ||
10-vm-sizes.md | ||
11-autoscale.md | ||
12-quota-limitations.md | ||
20-package-management.md | ||
21-distributing-data.md | ||
22-parallelizing-cores.md | ||
23-persistent-storage.md | ||
30-customize-cluster.md | ||
31-long-running-job.md | ||
32-building-containers.md | ||
33-programmatically-generate-config.md | ||
40-troubleshooting.md | ||
41-managing-storage-via-R.md | ||
42-faq.md | ||
README.md |
README.md
doAzureParallel Guide
This section will provide information about how Azure works, how best to take advantage of Azure, and best practices when using the doAzureParallel package.
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Azure Introduction (link)
Using the Data Science Virtual Machine (DSVM) & Azure Batch
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Virtual Machine Sizes (link)
How do you choose the best VM type/size for your workload?
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Autoscale (link)
Automatically scale up/down your cluster to save time and/or money.
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Azure Limitations (link)
Learn about the limitations around the size of your cluster and the number of foreach jobs you can run in Azure.
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Package Management (link)
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.
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Distributing your Data (link)
Best practices and limitations for working with distributed data.
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Parallelizing on each VM Core (link)
Best practices and limitations for parallelizing your R code to each core in each VM in your pool
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Persistent Storage (link)
Taking advantage of persistent storage for long-running jobs
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Customize Cluster (link)
Setting up your cluster to user's specific needs
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Long Running Job (link)
Best practices for managing long running jobs
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Programmatically generated config (linik)
Generate credentials and cluster config at runtime programmatically
Additional Documentation
Take a look at our Troubleshooting Guide for information on how to diagnose common issues.
Read our FAQ for known issues and common questions.