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Python does a lot of allocations and deallocations. Then I was curious, why would Python choose to never free memory? Many hours later, I now know far more than I ever wanted to about how Python deals with memory. People work around it by spawning multiple processes or using data structures on disk. It turns out that this is a flaw with the Python interpreter. I turned up a mailing list thread about Python never returning memory to the operating system. Finally, I turned to Google to see if anyone else has had a similar problem. Python still consumed a gigabyte of memory. Next, I added calls to force Python's cyclic garbage collector to run, in case I had circular references that were keeping the objects from being deleted. I deleted all my variables and still my program's size did not change. Next, I added " del reference" statements one variable at a time, trying to find which one was holding on to memory. I began looking for my memory leak by creating a minimal test that ran a big search. The solution is to find this reference and get rid of it. In Python or Java, a memory leak is created when you accidentally leave a reference to some massive data structure hanging around, so the entire thing cannot be freed. A leak in C or C++ is created when you allocate memory using malloc or new, and never deallocate it. Įven though some high level languages handle memory automatically doesn't mean that memory leaks are impossible. I thought I had a memory leak, but the actual problem was much more interesting. This computation needs very little memory, but for some reason, my program was not releasing any memory, which slowed down the whole system. The problem is that after each search I do a few hours of computation on the result. It is not unusual to see my Python process eating up a gigabyte of RAM. With some graphs, there can be millions of paths, which eats up a ton of memory. It places objects in a queue to track each path through the search space.
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I'm working with a program that does lengthy searches through a graph. This week, I broke Python's automatic memory management. Unfortunately, there are times when abstractions break, leaving you wondering what the heck is actually going on under the covers. One benefit is that it automatically manages memory, as any high level language worth the name should. It returns how much memory the ESP32 has left.I really enjoy working with Python. Heap_caps_get_largest_free_block() has nothing to do with micropython. If you call malloc with a bigger number than heap_caps_get_largest_free_block(), you're gonna get NULL and if you pass NULL into gc_init(), of course there's gonna be a fatal error.
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heap_caps_get_largest_free_block() = 100000 means "you can only call malloc with an argument no larger than 100000" Therefore, if heap_caps_get_largest_free_block() returns 100KB, it makes no sense to call malloc(150*1024).
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The biggest block of RAM available on ESP32 is, according to the ESP32 documentation, obtained by heap_caps_get_largest_free_block(). There's two things you have to keep separate: ESP32 and micropython. Void *mp_task_heap = malloc(mp_task_heap_size) # change to malloc(150*1024) -> a fatal error at startup. Size_t mp_task_heap_size = heap_caps_get_largest_free_block(MALLOC_CAP_8BIT) Allocate the uPy heap using malloc and get the largest available region In other words, 100 KB is known as the heap maximum size.īy the way, if we forcibly designate a heap size of 150 KB, restart was repeated due to a fatal error at startup.ĭo you know how to reduce the micopython system for getting big size of heap memory ? That is, It's specify the remaining memory size of micropython. Heap size : heap_caps_get_largest_free_block(MALLOC_CAP_8BIT)