system-profile

Profiling a Python script requires identifying the target and selecting appropriate methods like cProfile or py-spy to measure CPU and memory performance. The process generates an instrumentation changelog and a summarized report with actionable optimization recommendations.

Profiling GPU and interconnect usage for a running process involves selecting appropriate profiling methods and utilizing tools like nvidia-smi to measure hardware performance. This generates a structured report detailing bottlenecks and actionable optimization recommendations.

Yes, you can profile a running process by targeting its PID using external tools like py-spy or nvidia-smi to measure CPU, memory, or GPU performance. This avoids code modification while still producing a summarized performance report with optimization recommendations.

CPU and memory profiling with tracemalloc may require manual code instrumentation when needed to capture actionable metrics accurately. The system automatically generates the necessary instrumentation code and provides a changelog alongside the summarized performance report.

You need to profile hardware components like interconnects instead of just CPU when diagnosing complex performance bottlenecks that involve data transfer limitations across GPUs. Selecting appropriate profiling methods ensures the final report accurately identifies these hardware-level bottlenecks.