Technology Laboratories: Facilitating Instruction for Cyberinfrastructure Infused Data Sciences

Volume 13, Issue 1 (April 2022), pp. 44–49

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@article{jocse-13-1-8,
  author={Zhenhua He and Jian Tao and Lisa M. Perez and Dhruva K. Chakravorty},
  title={Technology Laboratories: Facilitating Instruction for Cyberinfrastructure Infused Data Sciences},
  journal={The Journal of Computational Science Education},
  year=2022,
  month=apr,
  volume=13,
  issue=1,
  pages={44--49},
  doi={https://doi.org/10.22369/issn.2153-4136/13/1/8}
}

While artificial intelligence and machine learning (AI/ML) frameworks gain prominence in science and engineering, most researchers face significant challenges in adopting complex AI/ML workflows to campus and national cyberinfrastructure (CI) environments. Data from the Texas A&M High Performance Computing (HPRC) researcher training program indicate that researchers increasingly want to learn how to migrate and work with their pre-existing AI/ML frameworks on large scale computing environments. Building on the continuing success of our work in developing innovative pedagogical approaches for CI-training approaches, we expand CI-infused pedagogical approaches to teach technology-based AI and data sciences. We revisit the pedagogical approaches used in the decades-old tradition of laboratories in the Physical Sciences that taught concepts via experiential learning. Here, we structure a series of exercises on interactive computing environments that give researchers immediate hands-on experience in AI/ML and data science technologies that they will use as they work on larger CI resources. These exercises, called "tech-labs," assume that participating researchers are familiar with AI/ML approaches and focus on hands-on exercises that teach researchers how to use these approaches on large-scale CI. The tech-labs offer four consecutive sessions, each introducing a learner to specific technologies offered in CI environments for AI/ML and data workflows. We report on our tech-lab offered for Python-based AI/ML approaches during which learners are introduced to Jupyter Notebooks followed by exercises using Pandas, Matplotlib, Scikit-learn, and Keras. The program includes a series of enhancements such as container support and easy launch of virtual environments in our Web-based computing interface. The approach is scalable to programs using a command line interface (CLI) as well. In all, the program offers a shift in focus from teaching AI/ML toward increasing adoption of AI/ML in large-scale CI.