I had a similar problem in reading a graph from a file. The processing included the computation of a 200 000x200 000 float matrix (one line at a time) that did not fit into memory. Trying to free the memory between computations using gc.collect() fixed the memory-related aspect of the problem but it resulted in performance issues: I don't know why but even though the amount of used memory remained constant, each new call to gc.collect() took some more time than the previous one. So quite quickly the garbage collecting took most of the computation time.
To fix both the memory and performance issues I switched to the use of a multithreading trick I read once somewhere (I'm sorry, I cannot find the related post anymore). Before I was reading each line of the file in a big for loop, processing it, and running gc.collect() every once and a while to free memory space. Now I call a function that reads and processes a chunk of the file in a new thread. Once the thread ends, the memory is automatically freed without the strange performance issue.
Practically it works like this:
from dask import delayed # this module wraps the multithreading def f(storage, index, chunk_size): # the processing function # read the chunk of size chunk_size starting at index in the file # process it using data in storage if needed # append data needed for further computations to storage return storage partial_result = delayed([]) # put into the delayed() the constructor for your data structure # I personally use "delayed(nx.Graph())" since I am creating a networkx Graph chunk_size = 100 # ideally you want this as big as possible while still enabling the computations to fit in memory for index in range(0, len(file), chunk_size): # we indicates to dask that we will want to apply f to the parameters partial_result, index, chunk_size partial_result = delayed(f)(partial_result, index, chunk_size) # no computations are done yet ! # dask will spawn a thread to run f(partial_result, index, chunk_size) once we call partial_result.compute() # passing the previous "partial_result" variable in the parameters assures a chunk will only be processed after the previous one is done # it also allows you to use the results of the processing of the previous chunks in the file if needed # this launches all the computations result = partial_result.compute() # one thread is spawned for each "delayed" one at a time to compute its result # dask then closes the tread, which solves the memory freeing issue # the strange performance issue with gc.collect() is also avoidedHướng dẫn vẽ pikachu bằng python
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