![]() ![]() ![]() Once you have a good estimate of how memory usage varies based on input size, you can think about cost estimates for hardware, and therefore the need for optimization. In many cases peak memory requirements scale linearly with input size.īut that’s not always the case: make sure your model isn’t making false assumptions, and underestimating memory usage for large inputs. In other words, if the model says you need 800MB RAM, make sure there’s 900MB free. You’ll want to add another 10% or more to the estimate as a fudge factor, because real memory usage might vary somewhat. While the model will often give you a reasonable estimate, don’t assume it’s exactly right. You will however need to do some polling in a thread or other process as your program runs, since this doesn’t give you the peak value.Īlternatively, just make sure you gather your estimates on a computer with more than enough RAM. You can use psutil to get more extensive current memory usage, including swap. So be careful if you start seeing peak resident memory usage plateau, as this may be a sign of swapping. If your program starts swapping, offloading memory to disk, peak memory usage might be higher than resident memory. What we’re measuring above is how much memory is stored in RAM at peak. Practical considerations Peak resident memory is not the same as peak memory usage Now you can estimate memory usage for any input size, from tiny to huge. polyfit (,, 1 ) array () > def expected_memory_usage ( image_pixels ). On Linux and macOS you can use the standard Python library module resource: How do you measure peak memory of a process? Unlike CPU, if you run out of memory your program won’t run slower-it’ll crash. If your process uses 100MB of RAM 99.9% of the time, and 8GB of RAM 0.1% of the time, you still must ensure 8GB of RAM are available. When you’re investigating memory requirements, to a first approximation the number that matters is peak memory usage. What you really need then is model of how much memory your program will need for different input sizes. In the first case above, you can’t actually measure peak memory usage because your process is running out memory.Īnd in the remaining cases, you might be running with differents inputs at different times, resulting in different memory requirements. If you’re scaling up to multiple runs, you’ll want to estimate the costs, whether hardware or cloud resources.If you’re running a parallelized computation, you will want to know how much memory each individual task takes, so you know how many tasks to run in parallel.If you’re running out of memory, it’s good to know whether you just need to upgrade your laptop from 8GB to 16GB RAM, or whether your process wants 200GB RAM and it’s time to do some optimization.Whether it’s a data processing pipeline or a scientific computation, you will often want to figure out how much memory your process is going to need: ![]()
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