# Compare your current GPU costs against Vast.ai marketplace prices
PROVIDER_COSTS = {
    "aws_p4d.24xlarge":      {"gpu": "A100 40GB", "gpus": 8, "hourly": 32.77},
    "aws_p3.2xlarge":        {"gpu": "V100 16GB", "gpus": 1, "hourly": 3.06},
    "gcp_a2-highgpu-1g":     {"gpu": "A100 40GB", "gpus": 1, "hourly": 3.67},
    "azure_NC24ads_A100_v4": {"gpu": "A100 80GB", "gpus": 1, "hourly": 3.67},
    "lambda_1xA100":         {"gpu": "A100",      "gpus": 1, "hourly": 1.25},
}

VASTAI_TYPICAL = {
    "RTX_4090":  0.20,
    "A100":      1.50,
    "H100_SXM":  3.00,
}

def savings_analysis(current_provider, current_hourly, vastai_gpu, vastai_hourly):
    monthly_current = current_hourly * 730  # hours/month
    monthly_vastai = vastai_hourly * 730
    savings = monthly_current - monthly_vastai
    pct = (savings / monthly_current) * 100
    print(f"Current ({current_provider}): ${monthly_current:,.0f}/mo")
    print(f"Vast.ai ({vastai_gpu}): ${monthly_vastai:,.0f}/mo")
    print(f"Savings: ${savings:,.0f}/mo ({pct:.0f}%)")

savings_analysis("AWS p3.2xlarge", 3.06, "RTX_4090", 0.20)
# Output: Savings: $2,088/mo (93%)

# Most Docker images work unchanged on Vast.ai
# Key differences:
# - Vast.ai instances run as root
# - /workspace is the default working directory
# - SSH access (not IAM roles) for authentication

# Adapt your existing Dockerfile
cat << 'DOCKERFILE' > Dockerfile.vastai
FROM your-existing-image:latest

# Vast.ai instances use /workspace by default
WORKDIR /workspace

# Install any Vast.ai-specific tools
RUN pip install boto3  # for S3 checkpoint uploads

# Copy training code
COPY src/ /workspace/src/
COPY configs/ /workspace/configs/

CMD ["python", "src/train.py"]
DOCKERFILE

docker build -t ghcr.io/org/training:vastai -f Dockerfile.vastai .
docker push ghcr.io/org/training:vastai

# On AWS/GCP: IAM roles provide automatic credentials
# On Vast.ai: Pass credentials explicitly via environment variables

# Create instance with env vars for cloud storage access
vastai create instance $OFFER_ID \
  --image ghcr.io/org/training:vastai \
  --disk 100 \
  --env "AWS_ACCESS_KEY_ID=AKIA... AWS_SECRET_ACCESS_KEY=... AWS_DEFAULT_REGION=us-east-1"

#!/bin/bash
set -euo pipefail
echo "Migration Validation Checklist"

# 1. Docker image runs on Vast.ai
vastai create instance $OFFER_ID --image ghcr.io/org/training:vastai --disk 50
# Wait for running...

# 2. GPU access works
ssh -p $PORT root@$HOST "nvidia-smi && python -c 'import torch; print(torch.cuda.is_available())'"

# 3. Cloud storage works
ssh -p $PORT root@$HOST "aws s3 ls s3://your-bucket/ | head -5"

# 4. Training runs and saves checkpoints
ssh -p $PORT root@$HOST "cd /workspace && python src/train.py --epochs 1 --checkpoint-dir /workspace/ckpt"

# 5. Checkpoints uploaded to cloud storage
ssh -p $PORT root@$HOST "aws s3 sync /workspace/ckpt/ s3://your-bucket/ckpt/"

# 6. Clean up
vastai destroy instance $INSTANCE_ID
echo "Migration validation complete"

## Rollback Procedure
1. Stop all Vast.ai instances: `vastai show instances` → `vastai destroy instance ID`
2. Re-provision on original cloud provider
3. Resume training from cloud-stored checkpoint
4. Vast.ai Docker image remains available for future retry