Imagine you have a segmentation map, where each object is identified by a unique index, e.g. looking similar to this: For each object, I would like to save which pixels it covers, but I could only come up with the standard for loop so far. Unfortunately, for larger images with thousands of individual objects, this turns out to be very
Tag: parallel-processing
Correct usage of numpy.vstack with python’s numba
I am currently trying to speed up some python code using numba. According to the documentation of numba, numpy.meshgrid is not supported but numpy.vstack is. So I replaced the meshgrid call by vstack which works fine when not using numba. It does not work, however, when using numba. Here is the code: And here is the error message: It sounds
Python Multiprocesssing: When i submit a list of objects to ProcessPoolExecutor, is a copy or a reference submitted?
I try to parallelize a part of a huge project. I got a lattice consistent of points and I perform a calculation at each individual point of the lattice. To speed up the calculation I want to subdivide the points of the lattice into different sublists and perform the calculations on individual processes via a ProcessPoolExecutor. However, if I pass
Is there a way to use torch.nn.DataParallel with CPU?
I’m trying to change some PyTorch code so that it can run on the CPU. The model was trained with torch.nn.DataParallel() so when I load the pre-trained model and try using it I must use nn.DataParallel() which I am currently doing like this: However after I switched my torch device to cpu like this: I got this error: I’m assuming
Find minimum difference between two vectors with numba
I’ve tried to optimize searching for minimum value between two numpy vectors with numba. There is speed up and result is correct until I use prange and parallel=True option. I understand that the issue is in sharing variables min_val, tmp, min_val_idx_a, min_val_idx_b during parallel execution (maybe with parallel threads). Is there way to overcome the problem and use numba in
Am I parallelizing this right?
I basically have to obtain a certain probability distribution by sampling from a sample of 5000 matrices. So I just need to count how many times element X occurs in the position (i,j) of these 5000 matrices. Then, I save these [values and counts] in a dictionary. That said, I thought it could be a good idea to parallelize my
how do POST API requests use parallel processing in python? requests.exceptions.ConnectionError:
I have code like this: in file data.json contains 500 records, for example: in BASE_URL there is data like this: expected output after POST API: with my code above, the data that enters the url is only 420 records, even though my data.json is 500 records. how do I solve this so that I post 500 records to url. I
Packages that are imported are not recognized during parallel computing?
I’m running the function get_content in parallel setting with multiprocess.Pool. Then it throws out an error NameError: name ‘session’ is not defined. Clearly, I defined it with session = requests.Session() . Could you please elaborate on this issue? Answer First of all, your import statement is incorrect and should be: (You had from multiprocess …, so I am not sure
Predicting in parallel using concurrent.futures of tensorflow.keras models
I am trying to implement some parallel jobs using concurrent.futures. Each worker requires a copy of a TensorFlow model and some data. I implemented it in the following way (MWE) simple_model() creates the model. clone_model clones a TensorFlow model. work represents an MWE of possible work. worker assigns the work in parallel. This is not working, it just stuck and
Numpy: Optimal way to count indexs occurrence in an array
I have an array indexs. It’s very long (>10k), and each int value is rather small (<100). e.g. Now I want to count occurrence of each index value (e.g. 0 for 3 times, 1 for 2 times…), and get counts as np.array([3, 2, 1, 1, 1]). I have tested 4 methods as follows: UPDATE: _test4 is @Ch3steR’s sol: Run for