PCAonGPU 0.1

A GPU-based Incremental PCA implementation.

PCAonGPU

A powerful implementation of Incremental Principal Components Analysis that runs on GPU, built on top of PyTorch.

Installation

1. Clone the repository:

git clone https://github.com/YourUsername/PCAonGPU.git

2. Navigate to the cloned directory:

cd PCAonGPU

3. Install the required dependencies:

pip install -r requirements.txt

Usage

from gpu_pca import PCAonGPU

# Create an instance
model = PCAonGPU(n_components=5)

# Fit the model (either using `fit` or `partial_fit`)
model.fit(your_data)

# Transform the data
transformed_data = model.transform(your_data)

Benchmark

SKlearn on an AMD Ryzen 9 5900X 12-Core Processor vs PCAonGPU on an Nvidia 4090

Data size: 5000 samples of 5000 dimensional data:

> python tests/benchmark_gpu_pca.py 
test_sklearn_pca took 21.78324556350708 seconds to complete its execution.
test_gpu_pca took 6.523377895355225 seconds to complete its execution.

Data size: 50000 samples of 5000 dimensional data.

> python tests/benchmark_gpu_pca.py 
test_sklearn_pca took 65.70944833755493 seconds to complete its execution.
test_gpu_pca took 11.030456304550171 seconds to complete its execution.