PyCUDACov - A PyCuda Covariance Matrix Parallel Implementation
Usage and Installation
Requires CUDA enviroment.
Installation:
$ pip install pycudacov
Basic Usage
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
from pandas import DataFrame
import numpy as np
from pycudacov import get_cov
# Generate test dataset
rows, cols = 2048, 2048 # samples, features
X, y = make_blobs(n_samples = rows, centers = 2, n_features = cols)
X_std = StandardScaler().fit_transform(X) # Optional
df = DataFrame(X_std)
df = df.astype(np.float32)
blocks = 512 # Size of kernel blocks
threads = 256 # Size of threads per block
# Call to PyCUDA Kernel, return the cov. matrix and
# GPU execution time in milliseconds
covariance_matrix, gpu_exec_time = get_cov(df.values, blocks, threads)
License
1 maintainer
Metadata
1 maintainer
Release files for pycudacov 0.1.2
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Source distribution (sdist)
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| pycudacov-0.1.2.tar.gz | 3.4 kB | Details |
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|---|---|---|---|---|
| pycudacov-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.6 kB
Release files / pycudacov-0.1.2.tar.gz
| Download URL | pycudacov-0.1.2.tar.gz |
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| Size | 3.4 kB |
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Release files / pycudacov-0.1.2-py3-none-any.whl
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