tensorscout 1.1.0

A Python library for tensor operations powered by parallel processing.

TensorScout

A Python library for tensor operations powered by parallel processing.

Installation

pip install tensorscout

Contributors

• Andrew Garcia - creator and maintainer

Contributing

1. Fork it (https://github.com/your-github-user/tensorscout/fork)
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

Usage Example

import tensorscout as scout
import matplotlib.pyplot as plt

'''Performance comparison of a perforation algorithm using a single core (CPU) v. four cores in parallel'''  
def perforate(tensor):

    for i in range(10000):
        cds = np.argwhere(tensor!=0)
        tensor[tuple(cds[np.random.randint(cds.shape[0])])] = 0 

    return tensor

def perforateMP(tensor):
    cores = 4
    for i in range(int(10000/cores)):
        cds = np.argwhere(tensor!=0)
        tensor[tuple(cds[np.random.randint(cds.shape[0])])] = 0 

    return tensor


'''single processor (CPU)'''
A = np.ones((240,240))
A = perforate(A)
plt.imshow(A)

#%timeit perforate(np.ones((240,240)))
#5.23 s ± 9.99 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)


'''parallel processing (4 CPUs)'''
A_MP = np.ones((240,240))
A_MP =  scout.rebuild(scout.multi_core(A_MP, perforateMP, cores = 4))
plt.imshow(A_MP)

#%timeit scout.rebuild(scout.multi_core(np.ones((240,240)), perforateMP, cores = 4))
#745 ms ± 8.86 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)