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Deployments

Learn how to use Vast Deployments, the quickest way to run GPU code and setup endpoints in the Vast Cloud.

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How It Works

Deployments define everything required to create a Vast Serverless endpoint running your code. This includes:
  • @remote decorated Python functions
  • The image name and tag
  • GPU search filters
  • Any pip install or apt get install reuqirements
  • Environment variables and secrets
  • Any custom start-up scripts
  • Endpoint autoscaling settings
You define your deployment in a single .py file, like my-deployment.py. Then, to run code one a Vast GPU, you can import and call your @remote functions. This will:
  • Automatically package and upload your code
  • Create a Serverless endpoint
  • Create worker GPU instances with your image
  • Install requirements and run the on_start.sh
  • Execute any @remote function calls.

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Architecture

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Deploy Mode

When you import and call a function from your deployment file, the SDK will automatically handle uploading all of the files, secrets, and scripts associated with your deployment to the cloud. It will also automatically create a managed Serverless endpoint and workergroup according to your autoscaling settings configuration. When your Serverless workers start up for the first time, they will download your code, load your secrets in the environment, and run any pip installs, apt installs, and your startup scripts before entering “serve” mode.

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Serve Mode

When in serve mode, your workers will load all of the @context classes that you have defined, ensuring that their aenter() functions have completed before marking your worker as ready. Once your context is setup, the worker will run a benchmark based on to determine a performance score for that worker, which is used by the Serverless engine to determine how much capacity is needed to serve your endpoint. Once benchmarked, the worker will enter a “ready” state, meaning that it will begin to execute any remote functions that you invoke.

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Calling @remote Functions

When you call a @remote function, it will first wait for your deployment to be set up, and for your workers to load and enter a ready state. As soon as a worker is available to execute your function, the Vast.ai SDK will automatically package the parameters for the function call and route to the quickest ready worker. The worker then receives those parameters, executes the function with access to the context and the GPU on the instance, and then packages and sends the return value back to the original function call you made on your local machine. A full round-trip HTTP request to a load-balanced, distributed GPU worker endpoint is abstracted into a single function call.

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Updating your Deployment

Whenever you make changes to your deployment, the SDK will automatically handle the minimal update required to get your latest code, settings, secrets, and configuration onto your live endpoint. This is generally broken down into several tiers of updates:

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Tier 0: No changes

If your deployment is identical to the last time that you ran it, no changes are needed and you can connect to your endpoint right away.

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Tier 1: Autoscaling changes

If the deployed code and settings are the same, but you are just tweaking the autoscaling parameters, then the SDK updates your endpoint and workergroup settings without needing to re-upload the code and restart your workers.

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Tier 2: Code changes

If you change the contents of the code, scripts, or package requirements, but don’t make any changes to the image, environment variables, search filters, or secrets, then we can issue a “soft-update” to your workers. This involves updating the latest state of your code to the cloud, and signaling to your endpoint to enter a “soft-update” that automatically pulls the latest code and reinstalls all other requirements.

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Tier 3: Image changes

If your Docker image has changed, or you need to run fresh with new environment variables configured, then the SDK will issue a “hard-update” to the endpoint, which re-uses the same workers but updates their image. This is similar to a soft-update, but generally takes a bit longer since it requires pulling a new Docker image potentially, and requires re-populating the contents of your workers storage.

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Tier 4: Forced Redeploy

This is used for making changes to your deployment that are not backwards compatible. It takes place whenever the “tag” of your Deployment changes, and it creates an entirely new Serverless endpoint and workergroup for separate routing. This is useful for when you need to ensure that clients running the new version of a deployment don’t route to workers serving an older version of the same deployment that isn’t backwards compatible. It requires recruiting entirely new workers.

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Managing Deployment Lifecycles

By default, Deployments and their endpoints will exist indefinitely after a client invokes a @remote function call and sets up a Deployment for the first time. However, you can instead configure your Deployment to automatically tear down after a specified number of seconds since the last client connection, which can be configured with the ttl parameter in the Deployment object.

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Search Queries

The SDK provides a Pythonic query builder for specifying GPU and hardware requirements for your deployment. Queries are built using Column objects and standard Python comparison operators, then passed to image.require().

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Basic Usage

from vastai.data.query import gpu_name, gpu_ram, cpu_cores, RTX_4090, RTX_5090, H100_SXM

# Require a specific GPU
image.require(gpu_name == H100_SXM)

# Require one of several GPUs
image.require(gpu_name.in_([RTX_4090, RTX_5090]))

# Require minimum GPU RAM (in GB)
image.require(gpu_ram >= 48)

# Combine multiple requirements
image.require(gpu_ram >= 24, cpu_cores >= 16)

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Supported Operators

OperatorExampleDescription
==gpu_name == RTX_4090Equals
!=gpu_name != RTX_3090Not equals
<dph_total < 2.0Less than
<=gpu_ram <= 24Less than or equal
>inet_down > 500Greater than
>=cpu_cores >= 16Greater than or equal
.in_()gpu_name.in_([RTX_4090, H100_SXM])Value in list
.notin_()gpu_name.notin_([RTX_3060])Value not in list

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Queryable Columns

GPU gpu_name, gpu_ram, gpu_total_ram, gpu_max_power, gpu_max_temp, gpu_arch, gpu_mem_bw, gpu_lanes, gpu_frac, gpu_display_active, num_gpus, compute_cap, cuda_max_good, bw_nvlink, total_flops CPU cpu_name, cpu_cores, cpu_cores_effective, cpu_ghz, cpu_ram, cpu_arch Storage & Disk disk_space, disk_bw, disk_name, allocated_storage Network inet_up, inet_down, inet_up_cost, inet_down_cost, direct_port_count, pcie_bw, pci_gen Pricing dph_base, dph_total, storage_cost, storage_total_cost, vram_costperhour, min_bid, credit_discount_max, flops_per_dphtotal, dlperf_per_dphtotal Machine & Host host_id, machine_id, hostname, public_ipaddr, reliability, expected_reliability, os_version, driver_vers, mobo_name, has_avx, static_ip, external, verification, hosting_type, vms_enabled, resource_type, cluster_id Virtual Columns (convenience aliases resolved by the API) geolocation, datacenter, duration, verified, allocated_storage, target_reliability

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GPU Name Constants

The SDK exports constants for all known GPU models. A selection of commonly used ones: NVIDIA Data Center: A100_PCIE, A100_SXM4, H100_PCIE, H100_SXM, H100_NVL, H200, H200_NVL, B200, GH200_SXM, L4, L40, L40S, A10, A30, A40, Tesla_T4, Tesla_V100 NVIDIA Consumer: RTX_5090, RTX_5080, RTX_5070_Ti, RTX_5070, RTX_4090, RTX_4080S, RTX_4080, RTX_4070_Ti, RTX_4070S, RTX_3090, RTX_3090_Ti, RTX_3080_Ti, RTX_3080 NVIDIA Professional: RTX_A6000, RTX_6000Ada, RTX_5880Ada, RTX_5000Ada, RTX_PRO_6000 AMD: InstinctMI250X, InstinctMI210, InstinctMI100, RX_7900_XTX, PRO_W7900, PRO_W7800 Import them from vastai.data.query: 
from vastai.data.query import gpu_name, RTX_4090, H100_SXM, gpu_ram, cpu_cores

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Creating and Configuring a Deployment

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The Deployment Object

from vastai import Deployment

app = Deployment(
    name="my-deployment",       # Deployment name (auto-detected from module if omitted)
    tag="default",              # Version tag for routing (changing this triggers a Tier 4 redeploy)
    version_label=None,         # Optional semantic version label
    api_key=...,                # Vast API key (uses $VAST_API_KEY env var if omitted)
    ttl=None,                   # Auto-teardown after N seconds of no client connections (None = live forever)
)

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Configuring the Image

app.image(from_image, storage) returns an Image object for configuring the Docker image, packages, environment, and hardware requirements. All methods return self for chaining. 
image = app.image("vastai/pytorch:@vastai-automatic-tag", storage=16)
MethodDescription
image.require(*queries)Set GPU/hardware search requirements
image.pip_install(*packages)Install pip packages on worker startup
image.apt_get(*packages)Install apt packages on worker startup
image.env(**kwargs)Set environment variables
image.run_script(script_str)Run a shell script on startup
image.run_cmd(*args)Run a command on startup (args as tuple)
image.copy(src, dst)Copy local files into the deployment bundle
image.venv(path)Use an existing venv at the given path instead of the SDK-managed one
image.use_system_python()Use the image’s system Python instead of a venv
image.publish_port(number, type_="tcp")Publish additional ports on the worker
image = app.image("vastai/base-image:@vastai-automatic-tag", 16)
image.pip_install("torch==2.0.0", "transformers")
image.apt_get("ffmpeg")
image.env(MODEL_NAME="my-model", DEBUG="true")
image.require(gpu_name.in_([RTX_4090, RTX_5090]))
image.venv("/venv/main")

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Configuring Autoscaling

app.configure_autoscaling(
    cold_workers=2,          # Number of idle workers to keep ready
    max_workers=10,          # Maximum concurrent workers
    min_load=100,            # Minimum load threshold to trigger scaling
    min_cold_load=50,        # Load threshold for cold workers
    target_util=0.8,         # Target utilization ratio (0-1)
    cold_mult=2,             # Cold worker multiplier
    max_queue_time=30.0,     # Maximum seconds a request can wait in queue
    target_queue_time=5.0,   # Target queue wait time in seconds
    inactivity_timeout=300,  # Seconds of inactivity before scaling down
)
All autoscaling parameters are optional. You can call configure_autoscaling() multiple times; later calls update (not replace) previously set values.

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ensure_ready()

After defining your remote functions, image configuration, and autoscaling settings, call ensure_ready() to deploy everything: 
app.ensure_ready()
This is a synchronous, blocking call that:
  1. Packages your deployment code and configuration into a tarball
  2. Computes a content hash to determine if anything changed since the last deploy
  3. Registers the deployment with the Vast API
  4. Uploads the tarball to cloud storage (if the code has changed)
  5. Triggers the appropriate update tier (soft-update, hard-update, etc.) if workers are already running
Once ensure_ready() returns, your deployment is registered and workers will begin provisioning. You must call ensure_ready() before invoking any @remote functions.

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Defining @remote Functions

The @app.remote decorator marks an async function for remote execution on GPU workers: 
@app.remote(benchmark_dataset=[{"x": 2}])
async def square(x):
    return x * x
When called from a client, the SDK serializes the function arguments, routes the call to an available worker, executes the function on the GPU, and deserializes the return value back to the caller. 
# client.py
import asyncio
from deploy import app, square

async def main():
    result = await square(5)  # Returns 25, executed on a remote GPU worker
    print(result)

asyncio.run(main())

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Full Example

from vastai import Deployment
from vastai.data.query import gpu_name, RTX_4090, RTX_5090

app = Deployment(name="my-app")

@app.remote(benchmark_dataset=[{"x": 2}])
async def square(x):
    return x * x

image = app.image("vastai/base-image:@vastai-automatic-tag", 16)
image.require(gpu_name.in_([RTX_4090, RTX_5090]))
app.configure_autoscaling(min_load=1000)
app.ensure_ready()

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The @context Decorator

The @context decorator registers an async context manager class whose lifecycle is tied to the worker. Contexts are used to load heavy resources (models, database connections, engines) once at worker startup, making them available to all @remote function calls without reloading on every request.

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Defining a Context

A context class must implement the async context manager protocol (__aenter__ and __aexit__): 
@app.context()
class MyModel:
    async def __aenter__(self):
        # Runs once when the worker starts up, before it enters "ready" state.
        # Use this to load models, allocate GPU memory, warm up caches, etc.
        import torch
        self.model = torch.load("model.pt").cuda()
        return self

    async def __aexit__(self, *exc):
        # Runs on worker shutdown. Use for cleanup.
        del self.model
Key points:
  • __aenter__ must return self (or whatever object you want get_context to return)
  • __aexit__ receives exception info if the worker is shutting down due to an error
  • All registered contexts are entered in parallel at startup
  • All contexts are exited in parallel during shutdown
  • You can pass arguments to the context constructor via the decorator: @app.context(arg1, kwarg=val)

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Accessing Context in @remote Functions

Use app.get_context(ContextClass) inside a remote function to retrieve the initialized context: 
@app.context()
class VLLMEngine:
    async def __aenter__(self):
        from vllm import AsyncLLMEngine, AsyncEngineArgs
        args = AsyncEngineArgs(model="Qwen/Qwen3-0.6B", max_model_len=512)
        self.engine = AsyncLLMEngine.from_engine_args(args)
        return self

    async def __aexit__(self, *exc):
        self.engine.shutdown_background_loop()


@app.remote(benchmark_dataset=[{"prompt": "Hello"}])
async def generate(prompt: str, max_tokens: int = 128) -> str:
    engine = app.get_context(VLLMEngine)
    # Use engine.engine to generate text...

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Benchmarks

Exactly one @remote function should define a benchmark. Benchmarks run automatically before a worker first enters “ready” state and are used by the Serverless engine to measure each worker’s performance, which informs autoscaling capacity decisions.

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Defining a Benchmark

Benchmarks are configured via parameters on the @remote decorator: 
@app.remote(
    benchmark_dataset=[{"x": 2}, {"x": 100}],  # Static list of sample inputs
    benchmark_runs=10,                           # Number of benchmark iterations (default: 10)
)
async def square(x):
    return x * x
ParameterTypeDefaultDescription
benchmark_datasetlist[dict] | NoneNoneA list of sample input dicts. Keys must match the function’s parameter names. During benchmarking, inputs are randomly selected from this list.
benchmark_generatorCallable[[], dict] | NoneNoneA callable that returns a sample input dict. Use this instead of benchmark_dataset when you need dynamic or randomized test data.
benchmark_runsint10Number of iterations to run during the benchmark.
You must provide either benchmark_dataset or benchmark_generator. The benchmark dataset entries should be representative of real workloads. They don’t need to be exhaustive, but should exercise the same code paths that production requests will.

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How Benchmarks Work

  1. Warmup (optional, enabled by default): Before timing, the worker runs a warmup pass to ensure caches, JIT compilation, and GPU memory allocation are settled.
  2. Timed runs: The worker executes the remote function benchmark_runs times using inputs from the dataset or generator, with a default concurrency of 10 parallel requests.
  3. Scoring: The results produce a performance score for the worker, which the Serverless engine uses to determine how much capacity that worker provides relative to others.

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Workload Calculators

By default, each request counts as a fixed amount of workload (100 units) for autoscaling purposes. For functions where request cost varies significantly (e.g., different prompt lengths for an LLM), you can define a custom workload calculator that computes the load from the request payload: 
def calculate_tokens(payload: dict) -> float:
    """Heavier requests count for more load."""
    return float(len(payload.get("tokens", [])))
This allows the autoscaler to make more accurate scaling decisions based on actual request complexity rather than just request count.

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Benchmark Examples

Simple function: 
@app.remote(benchmark_dataset=[{"x": 2}])
async def square(x):
    return x * x
Image inference (28x28 zero image): 
@app.remote(benchmark_dataset=[{"pixel_values": [[0.0] * 28] * 28}])
async def infer(pixel_values: list[list[float]]) -> dict:
    ...
LLM generation: 
@app.remote(benchmark_dataset=[{"prompt": "Hello"}])
async def generate(prompt: str, max_tokens: int = 128) -> str:
    ...