# Jeremylongshore Claude Code Plugins Plus Skills Vastai Hello World > Vast.ai Hello World - Skill: `tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-hello` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-hello` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-hello/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-hello --- # Vast.ai Hello World ## Overview Rent your first GPU instance on Vast.ai, run a PyTorch workload, and destroy the instance when done. Demonstrates the full lifecycle: search offers, create instance, connect via SSH, run a job, and tear down. ## Prerequisites - Completed `vastai-install-auth` setup - Vast.ai account with credits ($1+ recommended for testing) - SSH key uploaded to Vast.ai (cloud.vast.ai > Account > SSH Keys) ## Instructions ### Step 1: Search for Available GPUs (CLI) ```bash # Find cheap single-GPU offers sorted by price vastai search offers 'num_gpus=1 gpu_ram>=8 inet_down>100 reliability>0.95' \ --order 'dph_total' --limit 5 # Output columns: ID, GPU, VRAM, $/hr, DLPerf, Reliability, Location ``` ### Step 2: Search for Available GPUs (REST API) ```bash curl -s -H "Authorization: Bearer $VASTAI_API_KEY" \ "https://cloud.vast.ai/api/v0/bundles/?q=%7B%22num_gpus%22%3A%7B%22eq%22%3A1%7D%2C%22gpu_ram%22%3A%7B%22gte%22%3A8%7D%2C%22reliability2%22%3A%7B%22gte%22%3A0.95%7D%2C%22rentable%22%3A%7B%22eq%22%3Atrue%7D%7D&order=dph_total&limit=5" \ | jq '.offers[:3] | .[] | {id, gpu_name, num_gpus, gpu_ram, dph_total, reliability2}' ``` ### Step 3: Create an Instance (CLI) ```bash # Replace OFFER_ID with the ID from search results vastai create instance OFFER_ID \ --image pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime \ --disk 20 \ --onstart-cmd "echo 'Instance ready'" ``` ### Step 4: Create an Instance (Python) ```python from vastai_client import VastClient client = VastClient() # Search for affordable RTX 4090 offers offers = client.search_offers({ "num_gpus": {"eq": 1}, "gpu_name": {"eq": "RTX_4090"}, "reliability2": {"gte": 0.95}, "rentable": {"eq": True}, }) # Pick the cheapest offer best = sorted(offers["offers"], key=lambda o: o["dph_total"])[0] print(f"Best offer: {best['gpu_name']} at ${best['dph_total']:.3f}/hr (ID: {best['id']})") # Create instance with PyTorch image instance = client.create_instance( offer_id=best["id"], image="pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime", disk_gb=20, onstart="nvidia-smi && python -c 'import torch; print(torch.cuda.is_available())'", ) print(f"Instance created: {instance}") ``` ### Step 5: Monitor and Connect ```bash # Check instance status (wait for 'running') vastai show instances --raw | jq '.[] | {id, actual_status, ssh_host, ssh_port}' # Connect via SSH once running ssh -p SSH_PORT root@SSH_HOST # On the instance: verify GPU access nvidia-smi python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')" ``` ### Step 6: Run a Test Workload ```python # test_gpu.py — run this ON the rented instance import torch import time device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Device: {device} ({torch.cuda.get_device_name(0)})") # Simple matrix multiplication benchmark size = 4096 a = torch.randn(size, size, device=device) b = torch.randn(size, size, device=device) torch.cuda.synchronize() start = time.time() c = torch.matmul(a, b) torch.cuda.synchronize() elapsed = time.time() - start tflops = (2 * size**3) / elapsed / 1e12 print(f"Matrix multiply {size}x{size}: {elapsed:.3f}s ({tflops:.2f} TFLOPS)") print("Hello World from Vast.ai!") ``` ### Step 7: Destroy the Instance ```bash # IMPORTANT: Destroy to stop billing vastai destroy instance INSTANCE_ID # Verify it's gone vastai show instances ``` ## Output - GPU instance rented and running on Vast.ai - SSH connection established to the remote GPU machine - PyTorch workload executed successfully with GPU acceleration - Instance destroyed (billing stopped) ## Error Handling | Error | Cause | Solution | |-------|-------|----------| | `No offers found` | Filters too strict | Relax GPU or reliability filters | | `Insufficient funds` | Account balance too low | Add credits at cloud.vast.ai | | `Instance failed to start` | Docker image pull failed | Use a smaller or more common image | | `SSH connection refused` | Instance still loading | Wait 1-2 min for status `running` | | `CUDA not available` | Driver mismatch | Use a CUDA-compatible Docker image | ## Resources - [Vast.ai Search & Filter](https://docs.vast.ai/search-and-filter-gpu-offers) - [Creating Instances](https://docs.vast.ai/api-reference/instances/create-instance) - [CLI Reference](https://docs.vast.ai/cli/get-started) - [REST API Quickstart](https://docs.vast.ai/api/overview-and-quickstart) ## Next Steps Proceed to `vastai-local-dev-loop` for development workflow setup. ## Examples **Cheapest GPU test**: Search with `vastai search offers 'num_gpus=1' --order 'dph_total' --limit 1`, create an instance with the ubuntu image, SSH in, run `nvidia-smi`, then destroy. **Specific GPU model**: Filter for H100 with `gpu_name=H100_SXM` and `reliability>0.99` for production-grade hardware. Expect $2.50-4.00/hr. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/jeremylongshore) — claim your Tome and manage your conversions.