This is a focused 6-hour lab to master Python multithreading with practical coverage of multiprocessing, asyncio/coroutines, multicore ETL patterns, and CPU→GPU coordination you can map onto Knative.
Why: In Kubernetes-native, GPU-heavy platforms, threads excel at I/O multiplexing, processes give true CPU parallelism, and asyncio scales network fan-out. Coordinating CPUs to keep GPUs fed efficiently is critical for cost and performance.
How: Small, measurable scripts using threading, Lock, Queue, ThreadPoolExecutor, multiprocessing, and asyncio. We compare I/O vs CPU-bound behavior, apply bounded concurrency and backpressure, and build interview-ready talking points.
conda env create -f environment.yml
conda activate py-mt-lab
python -m pip install -U pip
python -m pip install -r requirements.txt
pytest