This event is a part of the "Best Practices for HPC Software Developers" webinar series, produced by the IDEAS Productivity Project. The HPC Best Practices webinars address issues faced by developers of computational science and engineering (CSE) software on high-performance computers (HPC) and occur approximately monthly.
|Webinar Title||Simplifying Scientific Python Package Installation and Usage|
|Date and Time||2023-09-13 01:00 pm EDT|
|Presenter||Amiya Maji (Purdue University)|
|Registration, Information, and Archives||https://ideas-productivity.org/events/hpc-best-practices-webinars/#webinar078|
Webinars are free and open to the public, but advance registration is required through the Event website. Archives (recording, slides, Q&A) will be posted at the same link soon after the event.
With the growing popularity of Python, installation and management of Python packages in HPC environments is emerging as a critical problem for researchers; the problem is exacerbated by the need to provide consistency across traditional batch workloads and interactive notebooks. This webinar will discuss how to simplify scientific Python package installation by streamlining environment management, dependency tracking, and runtime customizations through easy-to-use tools. The webinar will discuss challenges for installing Python packages in HPC environments and present the best practices suggested by various HPC centers. Many of these best practices have been incorporated into a tool, conda-env-mod, developed by the speaker and his collaborators. HPC centers can further customize the tool and its module templates to incorporate additional software dependencies and provide descriptive help messages. The deployment of the tool has significantly reduced errors and enabled sharing of Python package installations among users. The webinar will give an overview of installing Python packages with
Amiya Maji is a Lead Computational Scientist at Rosen Center for Advanced Computing (RCAC) at Purdue University, where he collaborates with faculty and researchers from various scientific domains to optimize their computational and data analysis workflows. Being an avid advocate for software reliability and security, Amiya has developed several algorithms and tools for software testing both during his graduate studies at Purdue ECE and then at RCAC. He co-invented the “Testpilot” regression testing framework at Purdue (HUST17) and also developed the “conda-env-mod” tool for easy deployment of scientific Python applications (HUST20). Amiya currently leads the software build automation project for Purdue’s community clusters. Amiya’s contributions to the Community Cluster program were recognized by the Bravo Award (2020) given to Purdue employees for outstanding achievement. Amiya also served as a fellow of Trusted CI (2021) where he promoted best practices for secure computing.