vaultlayer 0.1.31

AI compute arbitrage CLI — move GPU training jobs between clouds automatically

VaultLayer

Run AI training jobs on managed GPU capacity with checkpointing, log streaming, and provider failover.

pip install -U vaultlayer
vl init
vl run train.py

Job submitted
Training on vast_ai
Training output:
...
Training completed successfully.

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What It Does

VaultLayer sits between your training script and the cloud. It:

• Checkpoints automatically — syncs model weights + optimizer state to a zero-egress R2 Vault on every save
• Detects interruptions — intercepts AWS/GCP/Azure termination signals before your job dies
• Migrates instantly — provisions a replacement node on the cheapest available provider and resumes from last checkpoint
• Tracks savings — shows real-time cost vs what you would have paid on AWS On-Demand

No changes to your PyTorch or JAX code. No YAML configs. No PhD-level infra knowledge required.

Commands

# Training
vl run train.py
vl ps
vl logs <job-id> --follow
vl stop <job-id>

# Dataset storage (no S3 required)
vl sync ./data --dataset-id my-dataset
vl run --data r2://my-dataset train.py
vl datasets

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Supported Providers

Provider	Type	Status
Vast.ai	Marketplace	Production-included
RunPod	Neocloud	Production-included
Lambda Labs	Neocloud	Production-included
AWS Spot	Hyperscaler	Production-included for validated failover paths
AWS On-Demand	Hyperscaler	Internal testing
GCP, CoreWeave, Crusoe, Nebius, Voltage Park, Hyperstack, Azure	Mixed	Pending validation

Current provider status lives in docs/provider_test_matrix.md and docs/provider_testing_matrix.md.

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Model Size Support

Model Size	Method	Checkpoint Size	Status
1B	QLoRA	small	Validated smoke path
3B	QLoRA	small	Validated matrix path
7B	QLoRA	medium	Validated matrix path
72B	QLoRA	large	Validated on RunPod H100-class capacity
Full fine-tune / multi-GPU	varies	varies	Future work

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Tech Stack

Layer	Technology	Cost
Code + Docs	GitHub (this repo)	Free
CI/CD	GitHub Actions	Free (2k min/mo)
Vault / Storage	Cloudflare R2	Free up to 10GB
Agent Runtime	Railway	Free $5/mo credit
Webhooks	Cloudflare Workers	Free 100k req/day
Agent Message Queue	Upstash Redis	Free 10k cmd/day

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Repository Structure

vaultlayer/
├── README.md
├── docs/
│   ├── PRD.md              # Full product requirements
│   ├── ARCHITECTURE.md     # System design + agent topology
│   └── AGENTS.md           # Agent specs + build order
├── dashboard/
│   └── index.html          # Savings dashboard prototype
└── src/
    ├── cli/
    │   ├── main.py
    │   ├── run.py
    │   ├── checkpoint_template.py
    │   └── init.py
    ├── vaultlayer/
    │   └── _resume_hook.py
    ├── agents/
    │   ├── orchestration/
    │   ├── pricing/
    │   ├── watchdog/
    │   │   └── signals.py
    │   ├── vault/
    │   ├── broker/
    │   ├── finops/
    │   └── namespace/
    └── shared/

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SLA

VaultLayer tracks job completion, checkpoint persistence, and resume behavior. Public SLA numbers are not committed during beta; see docs/SLA_SLI.md for definitions.

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Dataset Storage (No S3 Required)

VaultLayer's Neutral Zone (Cloudflare R2) is a first-class storage provider. Users with no AWS or cloud storage account can upload training data directly and train from it on any provider.

# Upload from your laptop / on-prem server
vl sync ./training-data --dataset-id my-dataset

# Train — data is mounted at /mnt/vaultlayer on every provisioned node
vl run --data r2://my-dataset train.py

# See what you're storing and the monthly cost
vl datasets

Pricing:

Action	Cost
Upload (local → R2)	Free
Storage	$0.020 / GB / month ($0.0195 — 30% markup over Cloudflare R2 base rate)
Read (R2 → training node)	$0.00 (zero egress within Cloudflare network)
S3 mirror (one-time)	AWS egress charge (~$0.09/GB, first 100 GB/month free)

Storage quotas by plan:

Plan	Storage limit
Free	10 GB
Pro	500 GB
Enterprise	Unlimited

Datasets are soft-deleted with vl datasets --delete <id> — billing stops immediately, R2 objects are purged within 24 hours.

Getting Started

pip install -U vaultlayer
vl init
vl run train.py

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License

Private — © 2026 VaultLayer