Scaling Stochastic Modeling with R-POMP on Azure Batch

This article demonstrates how to distribute and parallelize POMP workflows on Azure Batch, enabling efficient large-scale parameter estimation. It covers two key approaches: trajectory mapping, which optimizes parameters using likelihood-based trajectory matching, and iterative filtering, which refines estimates using a sequential Monte Carlo framework.

Just pretend it says POMP, not POME

High Level Overview of the Workflow

To follow this workflow, first set up the necessary Azure resources and a Python environment to run the relevant Jupyter notebook. The Jupyter notebook will do most of the work. It will build and push the container image to Azure Container Registry, generate the required R RDS objects, and configure the Python batch client. It will then upload the Python scripts and R RDS objects to Azure Storage, set up and dispatch the Azure Batch job, and monitor the tasks until completion. Once the tasks finish, it will retrieve the result artifacts from Azure Storage and perform basic analysis on them.

All implementation details, including the full codebase and documentation, are available in the **r_pomp_azure_batch GitHub repository**.

Considerations

Pros

• Estimation Speed: Horizontally distribute and parallelize across Azure Batch compute for quicker results, and larger parameter searches.
• Dynamic Scaling: Automatically scale resources pools up and down, reducing cost.
• Cost Efficiency: Pay only for the compute time, avoiding the expense and administrative cost of a long-lived cluster.
• Reproducibility: Reproducibility of the compute environment using Docker.
• Experiment Tracking: Setup, preprocessing, execution, and results-gathering via a Jupyter notebook for experiment reproducibility.

Cons

• Learning Curve: Requires familiarity with Azure, Docker, Python, and R.
• Cloud Setup: Requires resource provisioning in Azure.
• Cloud Costs: Pay Azure Batch Pricing

README

To continue, checkout the **r_pomp_azure_batch GitHub repository** or reference it’s README, which is embedded below.

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