vastai-core-workflow-b

# Vast.ai Core Workflow B: Multi-Instance & Cost Optimization ## Overview Secondary workflow for Vast.ai: orchestrate multiple GPU instances for distributed training, implement automatic spot interruption recovery with checkpoint-based resume, and analyze spending to reduce per-job cost. ## Prerequisites - Completed `vastai-core-workflow-a` - Understanding of distributed training (PyTorch DDP, DeepSpeed) - Checkpoint-based training pipeline ## Instructions ### Step 1: Multi-Instance Provisioning ```python import subprocess, json, time from concurrent.futures import ThreadPoolExecutor def provision_cluster(num_nodes, gpu_name="A100", min_vram=80, image=""): """Provision multiple GPU instances for distributed training.""" # Search for matching offers query = (f"num_gpus=1 gpu_name={gpu_name} gpu_ram>={min_vram} " f"reliability>0.98 inet_down>500 rentable=true") result = subprocess.run( ["vastai", "search", "offers", query, "--order", "dph_total", "--raw", "--limit", str(num_nodes * 3)], capture_output=True, text=True, check=True, ) offers = json.loads(result.stdout) if len(offers) < num_nodes: raise RuntimeError(f"Only {len(offers)} offers, need {num_nodes}") # Provision nodes in parallel instances = [] for i, offer in enumerate(offers[:num_nodes]): inst_id = provision_single(offer["id"], image, rank=i) instances.append({"id": inst_id, "rank": i, "offer": offer}) # ...