PCAonGPU

Name	PCAonGPU JSON
Version	0.1 JSON
	download
home_page	https://github.com/dnhkng/PCAonGPU
Summary	A GPU-based Incremental PCA implementation.
upload_time	2023-10-30 18:47:36
maintainer
docs_url	None
author	Your Name
requires_python	>=3.10
license
keywords
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # 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

```python
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.
```

            

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