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Running PyTorch on Vast.ai: A Complete Guide

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Introduction

This guide walks you through setting up and running PyTorch workloads on Vast.ai, a marketplace for renting GPU compute power. Whether you’re training large models or running inference, this guide will help you get started efficiently.

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Prerequisites

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Setting Up Your Environment

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1. Selecting PyTorch Template

Navigate to the Templates tab to view available templates. Before choosing a specific instance, you’ll need to select the appropriate PyTorch template for your needs:
  • Choose recommended PyTorch template:
    • A container is built on the Vast.ai base image, inheriting its core functionality
    • It provides a flexible development environment with pre-configured libraries
    • PyTorch is pre-installed at /venv/main/ for immediate use
    • Supports for both AMD64 and ARM64(Grace) architectures, especially on CUDA 12.4+
    • You can select specific PyTorch versions via the Version Tag selector
PyTorch

PyTorch

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2. Choosing an Instance

Click the play button to select the template and see GPUs you can rent. For PyTorch workloads, consider:
  • GPU Memory: Minimum 8GB for most models
  • CUDA Version: PyTorch 2.0+ works best with CUDA 11.7 or newer
  • Disk Space: Minimum 50GB for datasets and checkpoints
  • Internet Speed: Look for instances with >100 Mbps for dataset downloads
Rent the GPU of your choice.

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3. Connecting to Your Instance

Click blue button on instance card in Instances tab when it says “Open” to access Jupyter.

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Setting Up Your PyTorch Environment

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1. Basic Environment Check

Open Python’s Interactive Shell in the jupyter terminal Verify your setup by executing these commands in Python’s Interactive Shell in a Jupyter terminal:
Python Python
import torch
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU device: {torch.cuda.get_device_name(0)}")

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2. Data Management

For efficient data handling: a) Fast local storage: 
mkdir /workspace/data
cd /workspace/data
b) Dataset downloads: 
# Using wget
wget your_dataset_url

# Using git lfs for larger files: https://git-lfs.com/
sudo apt-get install git-lfs
git lfs install
git clone your_dataset_repo

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Training Best Practices

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Checkpoint Management

Always save checkpoints to prevent data loss:
Python
checkpoint_dir = '/workspace/checkpoints'
os.makedirs(checkpoint_dir, exist_ok=True)

checkpoint = {
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'loss': loss,
}
torch.save(checkpoint, f'{checkpoint_dir}/checkpoint_{epoch}.pt')

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Resource Monitoring

Monitor GPU usage: 
watch -n 1 nvidia-smi
Or in Python:
Python
def print_gpu_utilization():
    print(torch.cuda.memory_allocated() / 1024**2, "MB Allocated")
    print(torch.cuda.memory_reserved() / 1024**2, "MB Reserved")

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Cost Optimization

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Instance Selection

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Resource Utilization

  • Use appropriate batch sizes to maximize GPU utilization
  • Enable gradient checkpointing for large models
  • Implement early stopping to avoid unnecessary compute time

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Troubleshooting

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Common Issues and Solutions

  • Out of Memory (OOM) Errors
    • Reduce batch size
    • Enable gradient checkpointing
    • Use mixed precision training
Python
from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()
with autocast():
    outputs = model(inputs)
    loss = criterion(outputs, labels)
scaler.scale(loss).backward()
  • Slow Training
    • Check GPU utilization
    • Verify data loading pipeline
    • Consider using torch.compile() for PyTorch 2.0+
Python
model = torch.compile(model)
  • Connection Issues
    • Use tmux or screen for persistent sessions
    • Set up automatic reconnection in your SSH config

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Best Practices

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Environment Management

  • Document your setup and requirements
  • Keep track of software versions

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Data Management

  • Use data versioning tools
  • Implement proper data validation
  • Set up efficient data loading pipelines

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Training Management

  • Implement logging (e.g., WandB, TensorBoard)
  • Set up experiment tracking
  • Use configuration files for hyperparameters

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Advanced Topics

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Multi-GPU Training

For distributed training:
Python
model = torch.nn.DataParallel(model)

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Mixed Precision Training

Enable AMP for faster training:
Python
from torch.cuda.amp import autocast

with autocast():
    outputs = model(inputs)

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Custom Docker Images

Create a custom Docker image from your own Dockerfile and create your own template as needed: 
FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime

# Install additional dependencies
RUN pip install wandb tensorboard

# Add your custom requirements
COPY requirements.txt .
RUN pip install -r requirements.txt

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Conclusion

Running PyTorch on Vast.ai provides a cost-effective way to rent cheap GPUs and accelerate deep learning workloads. By following this guide and best practices, you can efficiently set up and manage your PyTorch workloads while optimizing costs and performance.

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Additional Resources

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