multiprocessing

问题 I built a scraping module "scraper.py" that also has the ability to download file and I imported this module into django views. Issue is that in the scraper.py, this " __name__='__main__ " is included where the multiprocessing pool is, so when I import the module and try to run it, it doesn't work because it isn't the main . This is the script(scraper.py) that uses the pool method. def download(self, url): response = self._is_downloadable(url) if response: name = response.headers.get('content

问题 I have two codes. One is pooled (multiprocessing) version of the other. However, the parallel version with even 1 processor is taking a long time whereas the serial version finishes in ~15 sec. Can someone help to accelerate the second version. Serial import numpy as np, time def mapTo(d): global tree for idx, item in enumerate(list(d), start=1): tree[str(item)].append(idx) data=np.random.randint(1,4, 20000000) tree = dict({"1":[],"2":[],"3":[]}) s= time.perf_counter() mapTo(data) e = time

问题 Trying to understand how to correctly program with ray . The results below do not seem to agree with the performance improvement of ray as explained here. Environment: Python version: 3.6.10 ray version: 0.7.4 Here are the machine specs: >>> import psutil >>> psutil.cpu_count(logical=False) 4 >>> psutil.cpu_count(logical=True) 8 >>> mem = psutil.virtual_memory() >>> mem.total 33707012096 # 32 GB First, the traditional python multiprocessing with Queue (multiproc_function.py): import time from

问题 I'm trying to update a shared object (a dict ) using the following code. But it does not work. It gives me the input dict as an output. Edit : Exxentially, What I'm trying to achieve here is to append items in the data (a list) to the dict's list. Data items give indices in the dict. Expected output : {'2': [2], '1': [1, 4, 6], '3': [3, 5]} Note: Approach 2 raise error TypeError: 'int' object is not iterable Approach 1 from multiprocessing import * def mapTo(d,tree): for idx, item in

问题 I'm trying to update a shared object (a dict ) using the following code. But it does not work. It gives me the input dict as an output. Edit : Exxentially, What I'm trying to achieve here is to append items in the data (a list) to the dict's list. Data items give indices in the dict. Expected output : {'2': [2], '1': [1, 4, 6], '3': [3, 5]} Note: Approach 2 raise error TypeError: 'int' object is not iterable Approach 1 from multiprocessing import * def mapTo(d,tree): for idx, item in

问题 I'm trying to update a shared object (a dict ) using the following code. But it does not work. It gives me the input dict as an output. Edit : Exxentially, What I'm trying to achieve here is to append items in the data (a list) to the dict's list. Data items give indices in the dict. Expected output : {'2': [2], '1': [1, 4, 6], '3': [3, 5]} Note: Approach 2 raise error TypeError: 'int' object is not iterable Approach 1 from multiprocessing import * def mapTo(d,tree): for idx, item in

问题 I built a scraping module "scraper.py" that also has the ability to download file and I imported this module into django views. Issue is that in the scraper.py, this " __name__='__main__ " is included where the multiprocessing pool is, so when I import the module and try to run it, it doesn't work because it isn't the main . This is the script(scraper.py) that uses the pool method. def download(self, url): response = self._is_downloadable(url) if response: name = response.headers.get('content

问题 I built a scraping module "scraper.py" that also has the ability to download file and I imported this module into django views. Issue is that in the scraper.py, this " __name__='__main__ " is included where the multiprocessing pool is, so when I import the module and try to run it, it doesn't work because it isn't the main . This is the script(scraper.py) that uses the pool method. def download(self, url): response = self._is_downloadable(url) if response: name = response.headers.get('content

问题 This question is a followup on Jason R's comment to Robert Crovellas answer on this original question ("Multiple CUDA contexts for one device - any sense?"): When you say that multiple contexts cannot run concurrently, is this limited to kernel launches only, or does it refer to memory transfers as well? I have been considering a multiprocess design all on the same GPU that uses the IPC API to transfer buffers from process to process. Does this mean that effectively, only one process at a

问题 I want to search for pre-defined list of keywords in a given article and increment the score by 1 if keyword is found in article. I want to use multiprocessing since pre-defined list of keyword is very large - 10k keywords and number of article is 100k. I came across this question but it does not address my question. I tried this implementation but getting None as result. keywords = ["threading", "package", "parallelize"] def search_worker(keyword): score = 0 article = """ The multiprocessing