# Condensed: Faiss GPU

Summary: This tutorial provides implementation guidance for Faiss GPU, focusing on vector similarity search acceleration using GPUs. It demonstrates three key implementation approaches: single GPU, all available GPUs, and specific multiple GPUs configuration. The tutorial covers essential code patterns for index creation, GPU resource management, and data transfer between CPU and GPU. Key functionalities include proper installation, basic index setup, GPU resource allocation (with 18% VRAM allocation), and result verification. This guide is particularly useful for tasks involving large-scale vector similarity search and nearest neighbor computations that need GPU acceleration.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on essential implementation details for Faiss GPU:

# Faiss GPU Implementation Guide

## Installation
```bash
conda install -c pytorch -c nvidia faiss-gpu=1.8.0
```
**Note**: Only supports Linux x86_64

## Key Implementation Details

### 1. Basic Setup
```python
import faiss
import numpy as np

# Create sample data
dim = 768
corpus_size = 1000
corpus = np.random.random((corpus_size, dim)).astype('float32')
```

### 2. Index Creation Methods
```python
# Option 1: Direct initialization
index = faiss.IndexFlatIP(dim)

# Option 2: Using index_factory
index = faiss.index_factory(dim, "Flat", faiss.METRIC_L2)
```

### 3. GPU Implementation Options

#### Single GPU
```python
rs = faiss.StandardGpuResources()
co = faiss.GpuClonerOptions()
index_gpu = faiss.index_cpu_to_gpu(provider=rs, device=0, index=index, options=co)
```

#### All Available GPUs
```python
co = faiss.GpuMultipleClonerOptions()
index_gpu = faiss.index_cpu_to_all_gpus(index=index, co=co)
```

#### Specific Multiple GPUs
```python
ngpu = 4
resources = [faiss.StandardGpuResources() for _ in range(ngpu)]

vres = faiss.GpuResourcesVector()
vdev = faiss.Int32Vector()
for i, res in zip(range(ngpu), resources):
    vdev.push_back(i)
    vres.push_back(res)
index_gpu = faiss.index_cpu_to_gpu_multiple(vres, vdev, index)
```

## Important Notes

1. `StandardGpuResources` allocates 18% of total VRAM as scratch space by default
2. Use `GpuClonerOptions` and `GpuMultipleClonerOptions` to customize GPU storage behavior
3. All GPU implementation methods should yield identical results
4. Verify results with:
```python
# Sanity check
assert np.all(corpus[:] == corpus[I[:, 0]])
```

This condensed version maintains all critical implementation details while removing redundant explanations and keeping the focus on practical usage.