A Python package for stress testing Raspberry Pi CPUs and generating temperature plots to compare cooling solutions.
stressberry is a Python tool designed to test Raspberry Pi core temperatures under varying CPU loads and produce comparative plots. It helps users evaluate the effectiveness of different cooling methods, from passive heat sinks to active fan cases, by visualizing thermal performance during stress tests.
Raspberry Pi users and developers who need to assess and compare thermal management solutions, such as hobbyists, hardware tinkerers, and system integrators working on cooling optimization.
It provides a straightforward, reproducible method to generate empirical data and visual plots for thermal performance, enabling informed decisions about cooling hardware based on actual stress test results.
Stress tests for the Raspberry Pi :sweat_smile:
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Provides easy-to-use commands like stressberry-run and stressberry-plot for automated data collection and plotting, as shown in the installation and usage examples.
Standardizes testing with idle, full load (5 minutes), and cooldown phases over 10 minutes, ensuring consistent thermal performance evaluation across setups.
Generates PNG plots that allow direct visual comparison of temperature data, supported by a community gallery of user-submitted setups for real-world insights.
Includes a gallery of user contributions via GitHub issues, enabling collaborative analysis and empirical data sharing for various cooling solutions.
Only supports Raspberry Pi boards; it cannot be used for other hardware, limiting its applicability in mixed or non-Raspberry Pi environments.
Relies on the external 'stress' tool being installed separately (e.g., via apt), adding an extra step and potential compatibility hurdles.
Stress test duration is fixed at 5 minutes load with no built-in customization options, reducing flexibility for users needing tailored testing scenarios.