I thought that SharedMemory would keep values of target arrays, but when I actually tried it, it seems it doesn’t. In the code above, the 2 processes can share one array(a) and access to it. But the value that was given before sharing(a[‘value’] = 100) is missing. Is it just natural or is there any way to keep the value
Tag: multiprocessing
How to get multiprocessing for subtask work with tensorflow-gpu?
Basically I use tf-gpu 2.3rc0 to perform image detection on a video stream. For each loop, there’s a subtask runs separately. So I try to use Pool from multiprocessing The structure is like: It runs without error, but the tf seems to re-initiate for each frame, resulting to a extremely slow speed… Specifically, the same as follows shows for each
One Queue for each Consumer – Python
I’m in a single-producer/multiple-consumers scenario. Consider that each job is independent and the consumers do not communicate among them. Could it be a good idea to create a different queue for each consumer? In that way, the producer adds jobs in each queue in a round-robin fashion and there are no delays in accessing a single queue. Or is it
set a time limit on the Pool Map operation when using multiprocessing?
Is it possible to set a time limit on the pool map() operation when using multiprocessing in Python. When the time limit is reached, all child processes stop and return the results they already have. In the example above, I have a very large list vs. Ideally, all elements in the list vs will be sent to function task(), and
When spawn processes, Does Lock have different id?
I’m trying to figure out how Lock works under the hood. I run this code on MacOS which using “spawn” as default method to start new process. Output: The Lock works in my code. However, the ids of lock make me confused. Since idare different, are they still same one lock or there are multiple locks and they somehow communicate
Fast shaping of multiprocessing return values in Python
I have a function with list valued return values that I’m multiprocessing in Python and I need to concatenate them to 1D lists at the end. The following is a sample code for demonstration: The output for illustration is: The problem is that the list L that I’m processing is pretty huge and that the concatenations at the end take
Create a new list of dictionary from the index in dataframe Python with the fastest way
I have a ~200mil data in dictionary index_data: Key is a value in CustId and Value is an index of CustID in df_data: I have a DataFrame df_data: NOTE: If CustID is duplicate, only column Score have different data in each row I want to create a new list of dict(Total_Score is an avg Score of each CustID, Number is
Process a lot of data without waiting for a chunk to finish
I am confused with map, imap, apply_async, apply, Process etc from the multiprocessing python package. What I would like to do: I have 100 simulation script files that need to be run through a simulation program. I would like python to run as many as it can in parallel, then as soon as one is finished, grab a new script
Return value from function within a class using multiprocessing
I have following piece of codes, which I want to run through multiprocessing, I wonder how can I get return values after parallel processing is finished. I prefer not to make any change to getdata function. Output: Answer The Calculation objects you create in your main process are copied into the spawned process, so there is no way to extract
Occasional deadlock in multiprocessing.Pool
I have N independent tasks that are executed in a multiprocessing.Pool of size os.cpu_count() (8 in my case), with maxtasksperchild=1 (i.e. a fresh worker process is created for each new task). The main script can be simplified to: The pool sometimes gets stuck. The traceback when I do a KeyboardInterrupt is here. It indicates that the pool won’t fetch new