Production architecture for GPU compute workflows on Vast.ai. Covers the three-tier pattern (orchestrator, GPU workers, artifact storage), job queue design, and fault-tolerant training pipelines.
┌─────────────────────────────────────────────────┐
│ ORCHESTRATOR (your server / CI / cloud function) │
│ - Job queue management │
│ - Instance provisioning via Vast.ai API │
│ - Status monitoring and auto-recovery │
│ - Cost tracking and budget enforcement │
└───────────────┬─────────────────────────────────┘
│ Vast.ai REST API
┌───────────────▼─────────────────────────────────┐
│ GPU WORKERS (Vast.ai rented instances) │
│ - Training / inference execution │
│ - Checkpoint saving to cloud storage │
│ - Health reporting back to orchestrator │
│ - Graceful shutdown on SIGTERM (spot preemption)│
└───────────────┬─────────────────────────────────┘
│ S3 / GCS / MinIO
┌───────────────▼─────────────────────────────────┐
│ ARTIFACT STORAGE (persistent) │
│ - Model checkpoints │
│ - Training logs and metrics │
│ - Dataset cache │
│ - Final model artifacts │
└─────────────────────────────────────────────────┘
ml-pipeline/
orchestrator/
job_queue.py # Job definition and scheduling
provisioner.py # Vast.ai instance lifecycle
monitor.py # Status polling and auto-recovery
cost_tracker.py # Budget enforcement
worker/
Dockerfile # GPU worker image
train.py # Training entry point
checkpoint.py # Cloud storage checkpoint manager
health.py # Report status back to orchestrator
config/
gpu_profiles.yaml # GPU selection criteria per job type
budgets.yaml # Cost limits per team/project
scripts/
deploy.py # CLI for launching jobs
cost_report.py # Spending analysis
# config/gpu_profiles.yaml
profiles:
dev-test:
gpu_name: RTX_4090
num_gpus: 1
max_dph: 0.25
reliability_min: 0.90
max_duration_hours: 2
training-standard:
gpu_name: A100
num_gpus: 1
max_dph: 2.00
reliability_min: 0.98
max_duration_hours: 24
training-distributed:
gpu_name: H100_SXM
num_gpus: 4
max_dph: 4.00
reliability_min: 0.99
max_duration_hours: 48
inference-batch:
gpu_name: RTX_4090
num_gpus: 1
max_dph: 0.15
reliability_min: 0.95
max_duration_hours: 4
import boto3, os, json, time
class CheckpointManager:
def __init__(self, bucket, prefix, interval_steps=500):
self.s3 = boto3.client("s3")
self.bucket = bucket
self.prefix = prefix
self.interval = interval_steps
def save(self, model, optimizer, step, metrics):
if step % self.interval != 0:
return
checkpoint = {
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"step": step, "metrics": metrics,
"timestamp": time.time(),
}
path = f"{self.prefix}/checkpoint-{step}.pt"
torch.save(checkpoint, f"/tmp/checkpoint-{step}.pt")
self.s3.upload_file(f"/tmp/checkpoint-{step}.pt", self.bucket, path)
def load_latest(self):
objects = self.s3.list_objects_v2(Bucket=self.bucket, Prefix=self.prefix)
if not objects.get("Contents"):
return None
latest = max(objects["Contents"], key=lambda o: o["LastModified"])
self.s3.download_file(self.bucket, latest["Key"], "/tmp/latest.pt")
return torch.load("/tmp/latest.pt")
| Error | Cause | Solution |
|---|---|---|
| Orchestrator loses track of instance | API timeout | Implement heartbeat from worker |
| Checkpoint upload fails | S3 permissions | Verify credentials on GPU instance |
| Worker can't reach orchestrator | No public IP | Use polling model (worker pulls jobs) |
| Budget exceeded | No cost controls | Implement profile-based max_duration_hours |
For multi-environment configuration, see vastai-multi-env-setup.
Simple pipeline: Orchestrator searches for offers matching training-standard profile, provisions instance, uploads data via SCP, runs training, saves checkpoints to S3, destroys instance.
Fault-tolerant training: Worker saves checkpoint every 500 steps to S3. On preemption, orchestrator provisions replacement and worker resumes from latest checkpoint.