iBOTS
High Performance Python
It is becoming critical to make neuroscience analysis workflows robust, reproducible, and scalable. In this workshop, we will use Snakemake to develop complex data analysis pipelines while collaborating with other researchers, enabling your analysis to grow along with your data.
Modern neuroscience produces large, complex datasets that place heavy demands on analysis code. This three-day workshop will focus on practical ways to make Python analyses more efficient and scalable. Participants will learn how to identify performance bottlenecks, optimize numerical workflows, streamline data handling, and extend analyses beyond the limits of a single machine. The course emphasizes approaches that are both rigorous and usable in day-to-day research, with the aim of helping you write code that can keep pace with the scale of today’s neuroscience.
Learning and participation
Topics
- Measuring & Profiling Code for Process Duration and Peak Memory Consumption
- Improving Python Performance with NumPy, Numba, Cupy, and JAX
- Handling Large Datasets: Chunking, Compression, Caching, and Out-of-Core Processing
Intended participants
- Researchers and students from all universities are welcome.
- Participants of all educational backgrounds are welcome.
Useful resources for preparation
Certification
Students who attend at least 75% of the course will receive a participation certificate by email at the end of the course.