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#!/bin/bash | |
install_conda(){ | |
_USAGE="Usage | |
===== | |
./install_conda.sh <--gpu> <--prefix|-p DIR> | |
Install conda with miniforge on the system. |
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import numpy as np | |
import matplotlib.pyplot as plt | |
from matplotlib import rc | |
# activate latex text rendering | |
rc('text', usetex=True) | |
if __name__ == "__main__": |
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#include <mpi.h> | |
#include <iostream> | |
using namespace MPI; | |
using namespace std; | |
int main(int argc, char**argv) { | |
//Initiate the MPI API |
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""" | |
This example highlights the fact that the ProcessPoolExecutor implementation | |
from concurrent.futures is not robust to unpickling error for the result | |
(at least in versions 3.6 and lower). | |
""" | |
from pickle import UnpicklingError | |
from concurrent.futures import ProcessPoolExecutor | |
def return_instance(cls): |
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$ python -c "if 1: | |
import numpy as np | |
from sklearn.manifold.t_sne import TSNE, trustworthiness | |
from sklearn.utils import check_random_state | |
random_state = check_random_state(0) | |
X = random_state.randn(50, 2).astype(np.float32) | |
n_iter = 250 | |
tsne = TSNE(n_components=2, init='pca', random_state=0, | |
method='barnes_hut', verbose=20, n_iter=n_iter) |
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from time import sleep | |
def func0(): | |
print("run func0") | |
sleep(1) | |
print("ran func0") | |
def func1(): |
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import multiprocessing as mp | |
from multiprocessing import Manager | |
def test(idx, list_out): | |
list_out.append(idx) | |
if __name__ == '__main__': | |
mgr = Manager() |