
vastai-sdk
Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/vast-ai/vast-cli --skill vastai-sdk- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads vastai-sdk automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Vast.ai Python SDK (vastai / vastai_sdk)
The vastai package provides a Python SDK for managing GPU instances, volumes, serverless endpoints, and billing on Vast.ai. The vastai_sdk package is a backward-compatibility shim that re-exports vastai.
Installation
pip install vastai
For serverless and async support:
pip install "vastai[serverless]"
Authentication
The SDK reads the API key from ~/.vast_api_key by default. You can also pass it explicitly:
from vastai import VastAI
vast = VastAI() # reads ~/.vast_api_key
vast = VastAI(api_key="YOUR_API_KEY") # explicit key
Get your API key from https://console.vast.ai/manage-keys/
Backward Compatibility
The old vastai_sdk import still works:
from vastai_sdk import VastAI # equivalent to: from vastai import VastAI
VastAI Class (High-Level SDK)
from vastai import VastAI
vast = VastAI(api_key=None, server_url=None, retry=3, raw=False, quiet=False)
Instance Management
# List all your instances
instances = vast.show_instances()
# Get a single instance
instance = vast.show_instance(id=12345)
# Search GPU offers
offers = vast.search_offers(query='gpu_name=RTX_4090 num_gpus>=4 reliability>0.99')
# Create an instance from an offer
result = vast.create_instance(id=<offer_id>, image="pytorch/pytorch:latest", disk=50)
# ...as a jupyter instance on a direct connection
result = vast.create_instance(id=<offer_id>, image="pytorch/pytorch:latest", disk=50,
jupyter=True, direct=True, jupyter_lab=True)
# Lifecycle
vast.start_instance(id=12345)
vast.stop_instance(id=12345)
vast.reboot_instance(id=12345)
vast.destroy_instance(id=12345)
# Label an instance
vast.label_instance(id=12345, label="my-training-run")
# Get SSH connection string
ssh_url = vast.ssh_url(id=12345) # returns "ssh -p PORT user@host"
scp_url = vast.scp_url(id=12345) # returns scp-compatible URL
Interruptible (spot) rentals
Interruptible (spot) instances are priced below on-demand instances, but can be interrupted at any time by another user with a lower bid. Note: vast.search_offers(type='bid', ...) exposes min_bid, but vast.create_instance(...) defaults to on-demand at dph_total unless you pass bid_price=<floor>. Always pass bid_price after a type='bid' search, otherwise the instance will be rented as an on-demand instance/price instead of as an interruptible.
When outbid, the instance moves to stopped (not destroyed) and storage charges continue. Resume by raising the bid via vast.change_bid(id=..., price=...).
Search
# Search GPU offers (use help(vast.search_offers) for full query syntax)
offers = vast.search_offers(query='gpu_name=RTX_3090 num_gpus>=2')
# Search volume offers
volumes = vast.search_volumes(query='...')
# Search network volumes
net_vols = vast.search_network_volumes()
# Search templates
templates = vast.search_templates()
# Search invoices
invoices = vast.search_invoices()
Data Transfer
# copy() takes vast URLs: "[C.|V.]id:path", "cloud_service[.id]:path", or "local:path"
vast.copy("local:./data/", "C.12345:/workspace/data/") # Local → instance
vast.copy("C.12345:/workspace/results/", "local:./out/") # Instance → local
vast.copy("12345:/workspace/", "67890:/workspace/") # Instance → instance (legacy format)
vast.copy("s3.101:/data/", "C.12345:/workspace/") # Cloud service → instance
vast.copy("V.1234:/file", "C.5678:/workspace/") # Volume → instance
vast.copy("V.1234:/file", "s3.101:/workspace/") # Volume → cloud service
vast.cancel_copy(dst_id=12345) # Cancel an in-progress copy
# Cloud sync via a saved cloud connection (see the UI settings page for connection IDs)
vast.cloud_copy(src="./data", dst="s3://bucket/path", instance=12345,
connection=<conn_id>, transfer="Instance To Cloud")
vast.cancel_sync(dst_id=12345)
Volume copy is currently only supported for copying to other volumes, instances, or cloud services, not local. Do not use /root or / as a destination directory — it breaks ssh permissions on the instance and future copies fail. See https://vast.ai/docs/gpu-instances/data-movement#constraints.
Serverless Deployments
# List all deployments
deployments = vast.show_deployments()
# Get a deployment
deployment = vast.show_deployment(id=42)
# Delete a deployment
vast.delete_deployment(id=42)
Machine Management (Hosting)
machines = vast.show_machines()
machine = vast.show_machine(id=10)
vast.list_machine(id=10, price_gpu=0.30)
vast.unlist_machine(id=10)
SSH Keys
keys = vast.show_ssh_keys()
vast.create_ssh_key(ssh_key="ssh-rsa AAAA...")
vast.delete_ssh_key(id=5)
Team Management
members = vast.show_members()
vast.invite_member(email="user@example.com", role="developer")
vast.remove_member(id=7)
SyncClient (Low-Level Sync)
SyncClient provides typed, synchronous access to GPU offers and instances.
from vastai import SyncClient
client = SyncClient(api_key="YOUR_API_KEY") # or reads ~/.vast_api_key
# Search offers with structured filters
offers = client.search(
num_gpus=2,
gpu_name="RTX_4090",
min_reliability=0.99,
max_dph_total=2.0,
)
# Create an instance (SyncClient takes an InstanceConfig, not loose kwargs)
from vastai.data.instance import InstanceConfig
instance = client.create_instance(
offer_id=<id>,
config=InstanceConfig(image="pytorch/pytorch:latest", disk=50),
)
# List your instances
instances = client.show_instances() # returns list[SyncInstance]
# Destroy an instance
client.destroy_instance(instance_or_id=12345)
AsyncClient (Low-Level Async)
AsyncClient provides async access to GPU offers and instances. Use as an async context manager.
import asyncio
from vastai import AsyncClient
from vastai.data.instance import InstanceConfig
async def main():
async with AsyncClient(api_key="YOUR_API_KEY") as client:
# Search offers
offers = await client.search(num_gpus=1, gpu_name="A100")
# Create instance
instance = await client.create_instance(
offer_id=<id>, config=InstanceConfig(image="ubuntu:22.04"))
# List instances
instances = await client.show_instances() # returns list[AsyncInstance]
# Destroy instance
await client.destroy_instance(instance_or_id=instance.id)
asyncio.run(main())
Serverless Client
For inference endpoints (requires pip install "vastai[serverless]"):
import asyncio
from vastai import Serverless
async def main():
serverless = Serverless() # reads ~/.vast_api_key
# Get an endpoint
endpoint = await serverless.get_endpoint("my-endpoint")
# Make a request
response = await serverless.request("/v1/completions", {
"model": "Qwen/Qwen3-8B",
"prompt": "Who are you?",
"max_tokens": 100,
"temperature": 0.7,
})
text = response["response"]["choices"][0]["text"]
print(text)
asyncio.run(main())
Common Patterns
# Find cheapest 4x RTX 4090 and launch a job
from vastai import VastAI
vast = VastAI()
offers = vast.search_offers(query='gpu_name=RTX_4090 num_gpus=4 reliability>0.99')
cheapest = min(offers, key=lambda o: o['dph_total'])
result = vast.create_instance(id=cheapest['id'], image="pytorch/pytorch:latest", disk=100)
print(f"Launched instance: {result['new_contract']}")
# Use help() to explore method signatures
help(vast.search_offers)
help(vast.create_instance)
FAQ
What does the vastai-sdk skill do?
Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.
How do I install vastai-sdk?
Run npx skills add https://github.com/vast-ai/vast-cli --skill vastai-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/vastai-sdk/ (personal) or .claude/skills/vastai-sdk/ (project).
Which AI agents can use vastai-sdk?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is vastai-sdk safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains vastai-sdk?
It is published by vast-ai in vast-ai/vast-cli. It has 337 recorded installs.
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