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Docker Configuration

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What Docker options can I use?

Add Docker run arguments in the template configuration: 
# Port mapping
-p 8080:8080 -p 8081:8081

# Environment variables  
-e TZ=UTC -e CUDA_VISIBLE_DEVICES=0

# Shared memory (for PyTorch)
--shm-size=32gb

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Can I use my own Docker images?

Yes! When creating a template:
  1. Specify your Docker image URL
  2. Ensure it’s publicly accessible or provide auth
  3. Use standard Docker Hub, GHCR, or other registries
  4. Include all dependencies in the image

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Why can’t I run Docker inside my instance?

Docker-in-Docker is disabled for security. Alternatives:
  • Use separate instances for different containers
  • Build multi-service images
  • Use process managers like supervisord

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Performance Optimization

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How can I maximize GPU utilization?

  1. Batch size optimization:
    • Increase until GPU memory is nearly full
    • Monitor with nvidia-smi
  2. Data pipeline:
    • Pre-process data
    • Use multiple data loader workers
    • Cache datasets locally
  3. Mixed precision training: 
    # PyTorch example
from torch.cuda.amp import autocast
with autocast():
    output = model(input)

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Why is my training slower than expected?

Common issues:
  • CPU bottleneck - Check data loading
  • Network I/O - Download data to local storage first
  • Wrong GPU mode - Ensure CUDA is enabled
  • Thermal throttling - Some consumer GPUs throttle
  • PCIe bandwidth - Multi-GPU setups may be limited

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Storage and Volumes

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What’s the difference between instance storage and volumes?

Instance Storage:
  • Included with every instance
  • Deleted when instance is destroyed
  • Size set at creation (cannot change)
  • Faster performance
Volumes:
  • Persistent across instances
  • Can be attached/detached
  • Additional cost
  • Good for datasets and checkpoints

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How do I use volumes?

  1. Create a volume in the Volumes section
  2. Attach when creating an instance
  3. Mount point specified in template
  4. Data persists after instance destruction
See Volumes Guide for details.

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Environment Setup

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How do I install additional packages?

In Jupyter terminal or SSH: 
# System packages
apt-get update && apt-get install -y package-name

# Python packages
pip install package-name

# Conda (if available)
conda install package-name
Add to /root/onstart.sh for persistence across restarts.

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How do I use specific CUDA versions?

CUDA version depends on the Docker image. To check: 
nvcc --version
nvidia-smi
To use specific versions, choose appropriate templates or create custom images with your required CUDA version.

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Debugging

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How do I view instance logs?

  • Through web console: Click “Logs” on instance card
  • Via CLI: vastai logs INSTANCE_ID
  • Inside instance: Check /var/log/ directory

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My instance won’t start - how do I debug?

  1. Check instance logs for errors
  2. Verify Docker image exists and is accessible
  3. Check if ports are already in use
  4. Ensure sufficient disk space requested
  5. Try a different provider
  6. Contact support with instance ID

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How do I monitor resource usage?

# GPU monitoring
watch -n 1 nvidia-smi

# CPU and memory
htop

# Disk usage
df -h

# Network
iftop or nethogs

# All resources
gpustat -cp