vastai-performance-tuning

What this skill does

# Vast.ai Performance Tuning

## Overview

Optimize GPU instance selection, startup time, and training throughput on Vast.ai. Key levers: Docker image caching, GPU selection by dlperf score, data pipeline optimization, and multi-GPU scaling.

## Prerequisites

- Vast.ai account with active or planned instances
- Understanding of GPU compute bottlenecks
- Profiling tools (nvidia-smi, torch.profiler)

## Instructions

### Step 1: Optimize Instance Selection by Performance

```bash
# Sort by dlperf (deep learning performance benchmark) instead of price
vastai search offers 'num_gpus=1 gpu_ram>=24 reliability>0.95' \
  --order 'dlperf-' --limit 10

# The dlperf field measures actual GPU compute throughput
# Higher dlperf = faster training even at same GPU model
# Variance within same GPU model can be 20-30%
```

```python
def select_by_performance_per_dollar(offers):
    """Select the offer with best performance per dollar."""
    for o in offers:
        o["perf_per_dollar"] = o.get("dlperf", 0) / max(o["dph_total"], 0.01)
    return max(offers, key=lambda o: o["perf_per_dollar"])
```

### Step 2: Reduce Instance Startup Time

```bash
# Use smaller, pre-cached Docker images
# FAST: nvidia/cuda:12.1.1-runtime-ubuntu22.04 (~2GB, widely cached)
# MEDIUM: pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime (~4GB)
# SLOW: custom-image:latest with pip install at build (~10GB+)

# Pre-install deps in the image, not in onstart
# BAD (slow startup):
vastai create instance $ID --image pytorch/pytorch:latest \
  --onstart-cmd "pip install transformers datasets wandb"

# GOOD (fast startup):
# Build custom image with all deps pre-installed
```

### Step 3: Data Pipeline Optimization

```python
# Profile GPU utilization on the instance
# SSH into instance and run:
"""
watch -n 1 nvidia-smi  # Check if GPU util is <80% → data bottleneck

# Common fixes for low GPU utilization:
# 1. Increase DataLoader num_workers
# 2. Use pin_memory=True
# 3. Pre-fetch data to local SSD (not NFS)
# 4. Use WebDataset or FFCV for streaming datasets
"""

# Optimize PyTorch DataLoader
from torch.utils.data import DataLoader

loader = DataLoader(
    dataset,
    batch_size=32,
    num_workers=4,       # Match CPU cores on instance
    pin_memory=True,     # Faster GPU transfer
    prefetch_factor=2,   # Pre-load 2 batches per worker
    persistent_workers=True,  # Don't respawn workers each epoch
)
```

### Step 4: GPU Memory Optimization

```python
# Check available VRAM before selecting batch size
import torch

def optimal_batch_size(model, sample_input, gpu_memory_gb):
    """Binary search for largest batch size that fits in VRAM."""
    lo, hi, best = 1, 512, 1
    while lo <= hi:
        mid = (lo + hi) // 2
        try:
            torch.cuda.empty_cache()
            batch = sample_input.repeat(mid, *([1] * (sample_input.dim() - 1)))
            _ = model(batch.cuda())
            best = mid
            lo = mid + 1
        except torch.cuda.OutOfMemoryError:
            hi = mid - 1
        torch.cuda.empty_cache()
    return best
```

### Step 5: Multi-GPU Scaling

```bash
# Search for multi-GPU offers (NVLink preferred for training)
vastai search offers 'num_gpus>=4 gpu_name=A100 total_flops>=100' \
  --order 'dph_total' --limit 5

# Use torchrun for distributed training
ssh -p $PORT root@$HOST "torchrun --nproc_per_node=4 train.py --batch-size 128"
```

## GPU Performance Reference

| GPU | VRAM | FP16 TFLOPS | Typical $/hr | Best For |
|-----|------|-------------|-------------|----------|
| RTX 4090 | 24GB | 82.6 | $0.15-0.30 | Fine-tuning, inference |
| A100 40GB | 40GB | 77.97 | $0.80-1.50 | Training medium models |
| A100 80GB | 80GB | 77.97 | $1.00-2.00 | Training large models |
| H100 SXM | 80GB | 267 | $2.50-4.00 | High-throughput training |

## Output

- Performance-per-dollar offer selection
- Optimized Docker image for fast startup
- Data pipeline tuning (DataLoader, pin_memory, workers)
- GPU memory optimization with auto batch sizing
- Multi-GPU scaling with torchrun

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Low GPU utilization (<50%) | Data pipeline bottleneck | Increase `num_workers`, use `pin_memory` |
| OOM during training | Batch size too large | Use `optimal_batch_size()` or gradient accumulation |
| Slow instance startup | Large Docker image | Pre-install deps in image, not onstart |
| Poor multi-GPU scaling | Communication bottleneck | Use NVLink-connected GPUs, reduce sync frequency |

## Resources

- [Vast.ai Search Filtering](https://docs.vast.ai/search-and-filter-gpu-offers)
- [PyTorch Performance Guide](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html)

## Next Steps

For cost optimization, see `vastai-cost-tuning`.

## Examples

**Profile first**: SSH into instance, run `watch nvidia-smi` during training. If GPU-Util < 80%, the bottleneck is data loading, not compute.

**Best value GPU**: Use `perf_per_dollar` scoring to find hosts where the same GPU model runs faster due to better cooling or fewer co-tenants.