Getting Around the GIL: Parallelizing Python for Better Performance
summary
Covers concrete methods to parallelize CPU-bound Python workloads to reduce data processing time, addressing limitations imposed by the Global Interpreter Lock (GIL). Discusses multiple approaches tailored to different program needs and types of parallelism, with the goal of speeding data collection, parsing, feature engineering and other pipeline steps.
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