"Hello! My name is Britney Whittington, and I had the honor of interning at Quansight for the past three months. During this time, I worked with Nathan Goldbaum and Lysandros Nikolaou to improve the thread safety of NumPy's test suite. This project involved working with various parts of NumPy and other libraries and taught me a lot about OSS practices."
"The Python docs defines the GIL as: The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. A free-threaded build disables the GIL, allowing Python to use all available CPU cores and run code concurrently. This can be helpful in the field of scientific computing and AI/ML, with the GIL often being difficult to work around."
"With the release of free-threaded builds of Python, it's more important than ever to ensure Python code is thread-safe. This blog post details my journey improving the thread safety of NumPy's test suite. If you ever decide to tackle making your own code and test suite more thread-safe, hopefully my experience is helpful! So, feel free to kick back as I describe how I messed with NumPy's test suite, from my first one-line commit to updating the CI jobs."
An internship at Quansight focused on improving NumPy's test suite thread safety over three months. The work involved collaboration with maintainers and contributors to identify and fix race conditions and nondeterministic tests across NumPy and related libraries. Free-threaded Python builds disable the GIL, enabling concurrent execution across CPU cores and increasing the need for thread-safe code, particularly for scientific computing and AI/ML workloads. The project required modifications to tests, occasional code changes, and updates to continuous-integration jobs to validate thread safety. The effort emphasized open-source software practices and cross-project coordination to ensure reliable testing under free-threaded environments.
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