Tag: performance

A decision map for when Python teams should keep asyncio, use multiprocessing, try subinterpreters, or pilot free-threaded CPython without importing new race conditions.

Outcome: Separated Python concurrency choices by workload, state sharing, dependency readiness, and failure mode so free-threaded builds become a measured pilot instead of a default switch.

A Python performance playbook for choosing data structures, profiling tools, vectorized libraries, JIT experiments, and concurrency changes from evidence instead of taste.

Outcome: Provided a performance triage workflow that asks for a measured bottleneck before changing algorithms, data structures, runtime flags, concurrency models, or native libraries.

A repeatable triage routine — the three-run baseline, DataLoader diagnosis, five profiler signatures, and a copy-paste scaffold — for finding where training time actually goes before touching the model.

Outcome: Identified and resolved training bottlenecks in under an hour by running the three-run baseline and reading profiler signatures before changing any model code.

torch.compile, mixed precision, gradient accumulation, DDP vs FSDP, and the profiler — the five levers I reach for before rethinking the model architecture.

Outcome: Cut training wall-clock time and GPU memory pressure by applying compile, AMP, and accumulation patterns in sequence before ever touching model architecture.

A practical way to use algorithm complexity in product engineering, from choosing data structures to designing recommendation features that do not collapse as data grows.

Outcome: Explained how algorithm complexity shows up in everyday product work, then walked through an e-commerce recommendation feature from naive loops to indexed lookup.