CRC Director Jarek Nabrzyski co-chairs third international workshop on the future of computing
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Memory Monitoring: A Guide for R and Python Users |
Efficient memory management is crucial for optimal performance in data science and programming. Whether you're working with R or Python, understanding how to monitor memory usage within your code empowers you to write more robust and resource-efficient programs. This guide will walk you through the steps to monitor memory usage in both R and Python, helping you gain insights and optimize your code.
Memory Monitoring in R:
R provides a handy memory profiler called profmem that allows you to track memory usage during the execution of your code. If you haven't already, install the profmem package by running the following command in your R console:
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install.packages("profmem")
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Using profmem in Your Code:
Integrate profmem into your R script or RStudio session by wrapping your code with the profmem() function.
For example:
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library(profmem)
profmem({
# Your R code here
})
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After running your code with profmem, you'll receive a detailed breakdown of memory usage, including the functions and objects consuming the most memory. Use this information to identify potential memory bottlenecks and optimize your code.
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Memory Monitoring in Python
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Python offers the memory_profiler module, which allows you to monitor memory usage line by line in your Python scripts. If you haven't installed the memory_profiler module yet, use the following command:
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pip install memory_profiler
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Using memory_profiler Decorator:
Apply the @profile decorator to the functions or code blocks you want to monitor. For example:
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from memory_profiler import profile
@profile
def your_function():
# Your Python code here
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Running the Code with mprof:
Execute your Python script, and memory_profiler will generate a memory usage profile. To visualize the results, use the mprof tool:
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To view the memory profile, run:
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The generated plot provides a clear visualization of memory usage over time. Look for peaks and trends to identify areas where memory optimization may be needed.
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Best Practices for Memory Monitoring
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- Focus on profiling specific functions or code blocks where you suspect high memory usage. This allows you to pinpoint and address potential issues more efficiently.
- Use memory monitoring as an iterative process. Make adjustments to your code based on profiling results, rerun the profiler, and continue refining until you achieve optimal memory usage.
- Both R and Python offer specialized tools for memory monitoring. Familiarize yourself with profmem for R and memory_profiler for Python to take full advantage of language-specific capabilities.
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By incorporating memory monitoring into your coding practices, you gain valuable insights into how your code utilizes system resources. This knowledge not only helps you identify and fix potential issues but also enables you to write more efficient and scalable programs in R and Python.
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| Big Data and Machine Learning Workshop
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Monday, January 29, and Wednesday, January 31, 2024
11:00 a.m. – 5:00 p.m.
837 Flanner Hall (map)
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The CRC will be hosting this hands-on workshop, presented by ACCESS, where researchers will be able to learn about the latest technologies in high performance computing (HPC).
Please note that this two-day workshop will run Monday and Wednesday. Registration is required, contact CRC User Support for more information (crcsupport@nd.edu).
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| Every Wednesday and Thursday in January
2:00 – 3:00 p.m.
via Zoom
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This training is available to new users and current users interested in a refresher course on how to use CRC resources. Attendees learn the basics of accessing CRC resources and submitting jobs on the CRC clusters. This course is a co-requisite when receiving a CRC account. Learn more.
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| Top 10 Computation Users (December 2023)
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Chemistry & Biochemistry
448,819 CPU hours
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Chemical & Biomolecular Engineering
441,520 CPU hours
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Chemical & Biomolecular Engineering
417,848 CPU hours
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Physics
409,388 CPU hours
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Civil & Environmental Engineering & Earth Sciences
322,883 CPU hours
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| Chemical & Biomolecular Engineering
279,381 CPU hours
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Chemistry & Biochemistry
258,828 CPU hours
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Chemical & Biomolecular Engineering
257,177 CPU hours
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Civil & Environmental Engineering & Earth Sciences
239,997 CPU hours
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Chemical & Biomolecular Engineering
234,126 CPU hours
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