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# Using Quiver for PyG Examples
**[Quiver](https://github.com/quiver-team/torch-quiver)** is a **GPU-optimized distributed library** for PyG.
It can speed up graph sampling and feature aggregation through GPU when running PyG examples.
## Installation
Assuming you have installed PyTorch and PyG, you can install Quiver as follows:
```bash
pip install torch-quiver>=0.1.1
```
## Usage
The API and design documentation of Quiver can be found [here](https://github.com/quiver-team/torch-quiver).
## Examples
We provide several examples to showcase the usage of Quiver within PyG:
### Single-GPU Training
The single-GPU example leverages Quiver's ability of **(i)** GPU-based graph sampling and feature aggregation, and **(ii)** GNN data caching algorithm (which cache hot data in GPU memory) while enabling fast access to CPU data using a Quiver shared tensor implementation:
```bash
python single_gpu_quiver.py
```
### Multi-GPU Training
The multi-GPU example further leverages Quiver's ability of **(i)** distributing sampling and feature aggregation to multiple GPUs, and **(ii)** using multi-GPU memories to cache and replicate hot GNN data:
```bash
python multi_gpu_quiver.py
```
### Distributed Training
A Quiver-based distributed PyG example is coming soon.
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