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memory-profiler
Python tool for measuring memory usage line by line or over time.
Our View
memory-profiler helps researchers see where an analysis allocates memory, which is often the first step toward making large-data workflows reliable.
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Teaching — Recorded in topic offerings; consult the dated event links for availability.
1 topics with recorded teaching · 1 past offerings · 0 upcoming offerings
Related themes: Scientific Computing
Related topics: Arrays and Numerical ComputingAutomating Research WorkflowsRobust and Reusable Software
Common use cases
- Measure memory use in analysis scripts
- Identify steps that unexpectedly copy large arrays or tables
- Compare memory behavior before and after optimization
Getting started
memory-profiler reports how much memory Python code uses while it runs. It is useful when an analysis becomes slow, crashes on large datasets, or behaves differently on machines with less RAM.
Why It Matters in Research Workflows
Performance problems are not always CPU problems. Scientific Python code can become limited by memory allocation, temporary copies, and file-loading choices. Measuring memory use makes those bottlenecks visible before changing the code.
In Our Teaching
We introduce memory profiling in high-performance Python as part of a measure-first approach to optimization. The high-performance materials include examples of measuring and profiling Python code before choosing an optimization strategy.