08-28, 13:55–14:45 (Europe/Berlin), Room 5
Goals:
- Share tips, tricks and best practices for configuring the build backend of a Python package with compiled (Cython/C/C++/Rust/Fortran) code
- Identify shared needs between packages, and discuss gaps in current build backends, documentation, or shared infrastructure
Topics:
- Goals to aim for in your build config (and how to achieve them):
- Faster builds and relevant tooling like profiling,
- Build logs that actually help when diagnosing issues,
- How to debug build failures effectively,
- How to check for and visualize build dependencies,
- Ensuring builds are reproducible,
- Approaches to reducing binary size,
- CI config ideas to guard against regressions
- Recent build-related developments & a post-distutils world
- What are the most pressing pain points for maintainers?
Goals:
- Share tips, tricks and best practices for configuring the build backend of a Python package with compiled (Cython/C/C++/Rust/Fortran) code
- Identify shared needs between packages, and discuss gaps in current build backends, documentation, or shared infrastructure
Topics:
- Goals to aim for in your build config (and how to achieve them):
- Faster builds and relevant tooling like profiling,
- Build logs that actually help when diagnosing issues,
- How to debug build failures effectively,
- How to check for and visualize build dependencies,
- Ensuring builds are reproducible,
- Approaches to reducing binary size,
- CI config ideas to guard against regressions
- Recent build-related developments & a post-distutils world
- What are the most pressing pain points for maintainers?
Parallel Computing
Category [Community, Education, and Outreach] –Learning and Teaching Scientific Python
Category [Machine and Deep Learning] –Supervised Learning
Category [Scientific Applications] –Other
Category [Data Science and Visualization] –Data Analysis and Data Engineering
Expected audience expertise: Domain –some
Expected audience expertise: Python –none
Abstract as a tweet –Improving the build configuration of your Python package
Ralf has been deeply involved in the SciPy, PyData and Python packaging communities for 15 years. He is a maintainer of NumPy, SciPy, the Array API standard, meson-python and pypackaging-native. Ralf is the current SciPy Steering Council Chair, and he served on the NumFOCUS Board of Directors from 2012-2018.
Ralf co-directs Quansight Labs, which consists of developers, community managers, designers, and documentation writers who build open-source technology and grow open-source communities around data science and scientific computing projects. Previously Ralf has worked in industrial R&D, on topics as diverse as MRI, lithography and forestry.