vastai-migration-deep-dive

# Vast.ai Migration Deep Dive ## Current State !`vastai --version 2>/dev/null || echo 'vastai CLI not installed'` !`pip show vastai 2>/dev/null | grep Version || echo 'N/A'` ## Overview Migrate GPU workloads to Vast.ai from hyperscaler providers (AWS, GCP, Azure) or other GPU clouds (Lambda, RunPod, CoreWeave). Also covers migrating between GPU types on Vast.ai and the reverse migration away from Vast.ai. ## Prerequisites - Existing GPU workload with Docker image - Understanding of current GPU costs and utilization - Checkpoint-based training pipeline (for training migrations) ## Instructions ### Step 1: Cost Comparison Analysis ```python # 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 ...