Vastai Data Handling

Vast.ai Data Handling

Overview

Manage training data and model artifacts securely on Vast.ai GPU instances. Covers data transfer, encryption, checkpoint management, and cleanup. Critical consideration: Vast.ai instances run on shared hardware operated by third-party hosts.

Prerequisites

• Vast.ai instance with SSH access
• Cloud storage (S3, GCS) for persistent artifacts
• Understanding of data sensitivity classification

Instructions

Step 1: Data Transfer Patterns

# Small datasets (<5GB): Direct SCP
scp -P $PORT -r ./data/ root@$HOST:/workspace/data/

# Large datasets (5-50GB): Compressed transfer
tar czf - ./data/ | ssh -p $PORT root@$HOST "tar xzf - -C /workspace/"

# Very large datasets (>50GB): Cloud storage staging
# Upload to S3/GCS first, then download on instance
ssh -p $PORT root@$HOST "aws s3 sync s3://bucket/dataset/ /workspace/data/"

Step 2: Encrypted Data Transfer

import subprocess, os

def encrypt_and_upload(local_path, host, port, remote_path, passphrase):
    """Encrypt data before transferring to Vast.ai instance."""
    encrypted = f"{local_path}.enc"
    # Encrypt with AES-256
    subprocess.run([
        "openssl", "enc", "-aes-256-cbc", "-salt", "-pbkdf2",
        "-in", local_path, "-out", encrypted,
        "-pass", f"pass:{passphrase}",
    ], check=True)

    # Transfer encrypted file
    subprocess.run([
        "scp", "-P", str(port), encrypted,
        f"root@{host}:{remote_path}.enc",
    ], check=True)

    # Decrypt on instance
    subprocess.run([
        "ssh", "-p", str(port), f"root@{host}",
        f"openssl enc -aes-256-cbc -d -pbkdf2 "
        f"-in {remote_path}.enc -out {remote_path} "
        f"-pass pass:{passphrase} && rm {remote_path}.enc"
    ], check=True)

    os.remove(encrypted)

Step 3: Checkpoint to Cloud Storage

import torch, boto3, os

class CloudCheckpointManager:
    def __init__(self, s3_bucket, prefix, save_every=500):
        self.s3 = boto3.client("s3")
        self.bucket = s3_bucket
        self.prefix = prefix
        self.save_every = save_every

    def save(self, model, optimizer, step, loss):
        if step % self.save_every != 0:
            return
        local_path = f"/tmp/ckpt-{step}.pt"
        torch.save({
            "step": step, "loss": loss,
            "model": model.state_dict(),
            "optimizer": optimizer.state_dict(),
        }, local_path)
        self.s3.upload_file(local_path, self.bucket,
                           f"{self.prefix}/ckpt-{step}.pt")
        os.remove(local_path)
        print(f"Checkpoint saved: step {step}, loss {loss:.4f}")

    def load_latest(self):
        resp = self.s3.list_objects_v2(Bucket=self.bucket, Prefix=self.prefix)
        if not resp.get("Contents"):
            return None
        latest = sorted(resp["Contents"], key=lambda o: o["Key"])[-1]
        self.s3.download_file(self.bucket, latest["Key"], "/tmp/latest.pt")
        return torch.load("/tmp/latest.pt")

Step 4: Secure Cleanup Before Destroy

# ALWAYS clean sensitive data before destroying an instance
ssh -p $PORT root@$HOST << 'CLEANUP'
# Remove training data and checkpoints
rm -rf /workspace/data /workspace/checkpoints /workspace/*.pt

# Clear command history
history -c && rm -f ~/.bash_history

# Overwrite sensitive files (optional, for high-security)
find /workspace -name "*.env" -exec shred -u {} \;

echo "Cleanup complete"
CLEANUP

# Then destroy
vastai destroy instance $INSTANCE_ID

Step 5: Data Lifecycle Policy

Output

• Data transfer patterns (SCP, compressed, cloud-staged)
• Encrypted transfer for sensitive datasets
• Cloud checkpoint manager with S3 integration
• Secure cleanup script before instance destruction
• Data lifecycle policy

Error Handling

Resources

• Vast.ai Instance Management
• AWS S3 CLI

Next Steps

For enterprise access control, see vastai-enterprise-rbac.

Examples

Sensitive data workflow: Encrypt dataset locally, SCP encrypted file to instance, decrypt on-instance, train, save checkpoints to S3, clean and destroy.

Resume after preemption: Load latest checkpoint from S3 on new instance, continue training from last saved step.