Execute Vast.ai primary workflow: GPU instance provisioning and job execution.
Use when renting GPUs for training, searching offers by price and specs,
or managing the full instance lifecycle from search to teardown.
Trigger with phrases like "vastai rent gpu", "vastai training job",
"vastai provision instance", "run job on vastai".
Instrucciones de origen · Vista previa de solo lectura
name
vastai-core-workflow-a
description
Execute Vast.ai primary workflow: GPU instance provisioning and job execution.
Use when renting GPUs for training, searching offers by price and specs,
or managing the full instance lifecycle from search to teardown.
Trigger with phrases like "vastai rent gpu", "vastai training job",
"vastai provision instance", "run job on vastai".
Primary workflow for Vast.ai: search for GPU offers, provision an instance, transfer data, execute a training or inference job, collect artifacts, and destroy the instance to stop billing. This is the money-path operation for every Vast.ai user.
Prerequisites
Completed vastai-install-auth setup
Docker image published to a registry (Docker Hub, GHCR, etc.)
SSH key uploaded to Vast.ai
Training data accessible via URL or local path
Instructions
Step 1: Search Offers with Filters
import subprocess, json
defsearch_offers(gpu_name="RTX_4090", min_vram=24, min_reliability=0.95,
max_price=0.50, num_gpus=1):
"""Search Vast.ai marketplace with specific filters."""
query = (
f"num_gpus={num_gpus} gpu_name={gpu_name} "f"gpu_ram>={min_vram} reliability>{min_reliability} "f"inet_down>200 dph_total<={max_price} rentable=true"
)
result = subprocess.run(
["vastai", "search", "offers", query, "--order", "dph_total", "--raw"],
capture_output=True, text=True, check=True,
)
offers = json.loads(result.stdout)
print(f"Found {len(offers)} offers matching criteria")
for o in offers[:5]:
print(
)
offers
f" ID {o['id']}: {o['gpu_name']}{o['gpu_ram']}GB "
defprovision_instance(offer_id, image, disk_gb=50, onstart_cmd=""):
"""Create an instance from the best offer."""
cmd = [
"vastai", "create", "instance", str(offer_id),
"--image", image,
"--disk", str(disk_gb),
]
if onstart_cmd:
cmd.extend(["--onstart-cmd", onstart_cmd])
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
instance_info = json.loads(result.stdout)
instance_id = instance_info.get("new_contract")
print(f"Instance {instance_id} provisioning...")
return instance_id
Step 3: Wait for Instance Ready
import time
defwait_for_instance(instance_id, timeout=300):
"""Poll until instance status is 'running'."""
start = time.time()
while time.time() - start < timeout:
result = subprocess.run(
["vastai", "show", "instance", str(instance_id), "--raw"],
capture_output=True, text=True,
)
info = json.loads(result.stdout)
status = info.get("actual_status", "unknown")
print(f" Status: {status}")
if status == "running":
ssh_host = info.get("ssh_host")
ssh_port = info.get("ssh_port")
print(f" SSH: ssh -p {ssh_port} root@{ssh_host}")
return info
time.sleep(15)
raise TimeoutError(f"Instance {instance_id} did not start within {timeout}s")
Step 4: Transfer Data and Execute Job
# Upload training data to instance
scp -P $SSH_PORT ./data/training.tar.gz root@$SSH_HOST:/workspace/
# Execute training job remotely
ssh -p $SSH_PORT root@$SSH_HOST << 'REMOTE'cd /workspace
tar xzf training.tar.gz
python train.py --epochs 10 --batch-size 32 --output /workspace/checkpoints/
REMOTE
Step 5: Collect Artifacts and Destroy
defcleanup_instance(instance_id, ssh_host, ssh_port, output_dir="./results"):
"""Download results and destroy instance."""import os
os.makedirs(output_dir, exist_ok=True)
# Download model checkpoints
subprocess.run([
"scp", "-P", str(ssh_port), "-r",
f"root@{ssh_host}:/workspace/checkpoints/",
output_dir,
], check=True)
print(f"Artifacts saved to {output_dir}")
# CRITICAL: Destroy instance to stop billing
subprocess.run(["vastai", "destroy", "instance", str(instance_id)], check=True)
print(f"Instance {instance_id} destroyed — billing stopped")
For multi-instance orchestration and cost optimization, see vastai-core-workflow-b.
Examples
Fine-tune LLM: Search for A100 80GB offers, provision with the huggingface/transformers image, upload a LoRA config, run fine-tuning for 3 epochs, download the adapter weights, destroy the instance.
Batch inference: Provision 4 cheap RTX 4090 instances in parallel, distribute an inference dataset across them, collect results, and destroy all instances in a loop.
