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https://github.com/dnhkng/pcaongpu
A GPU-based Incremental PCA implementation.
https://github.com/dnhkng/pcaongpu
Last synced: 12 days ago
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A GPU-based Incremental PCA implementation.
- Host: GitHub
- URL: https://github.com/dnhkng/pcaongpu
- Owner: dnhkng
- License: mit
- Created: 2023-10-30T12:35:57.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-05T16:46:28.000Z (12 months ago)
- Last Synced: 2024-11-01T04:05:04.743Z (18 days ago)
- Language: Python
- Size: 11.7 KB
- Stars: 22
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PCAonGPU
A powerful implementation of Incremental Principal Components Analysis that runs on GPU, built on top of PyTorch. Up to 20x speed-ups on gigabyte-scale PCA.
## Installation
### From PyPi
`pip install PCAonGPU`
### From Source
1. Clone the repository:
`git clone https://github.com/dnhkng/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 IncrementalPCAonGPU
# Create an instance
model = IncrementalPCAonGPU(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 10000 dimensional data.
```
> python tests/benchmark_gpu_pca.py
test_sklearn_pca took 314.3792634010315 seconds to complete its execution.
test_gpu_pca took 35.23140811920166 seconds to complete its execution.
```