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 |
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# 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.
```
Raw data
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