---
title: "Runtimes"
description: "Reference for HUD runtimes: local Docker, remote workers, and the platform runtime that decides where each rollout's environment is started and executed."
icon: "rocket"
---

A **runtime** chooses where each rollout's environment runs. You pass it to `task.run` /
`taskset.run` at execution time, and the same task and the same `env.py` run anywhere - only the
runtime changes.

```python
from hud import LocalRuntime

await taskset.run(agent, runtime=LocalRuntime("env.py"))   # serve env.py locally, run here
```

## Built-in runtimes

| Runtime | Where the env runs | When to reach for it |
|---------|--------------------|----------------------|
| `LocalRuntime("env.py")` | A child process from your source | Fastest iteration; local development |
| `DockerRuntime("my-env")` | A fresh local container per rollout | Reproducibility and parity with production |
| `ModalRuntime("my-env")` | A fresh [Modal](https://modal.com/) sandbox per rollout | Cloud scale, no infra to manage |
| `DaytonaRuntime("my-env")` | A fresh [Daytona](https://www.daytona.io/) sandbox per rollout | Cloud scale on Daytona |
| `Runtime("tcp://host:8765")` | A substrate you already started | Attaching to a long-lived container or sandbox you own |
| `HUDRuntime()` | A HUD-hosted env, leased by name and tunneled | Local agent loop against a deployed env |
| `HostedRuntime()` | The whole rollout on a HUD-leased box | Agent and env run together off your machine |

Most runtimes are on the top-level package (`from hud import LocalRuntime, DockerRuntime, HUDRuntime,
HostedRuntime, Runtime`); `ModalRuntime` and `DaytonaRuntime` import from `hud.eval`.

<Note>
**Omit `runtime=` and it's inferred** from each task's `_source`, the file its template was defined
in. When every task shares one `_source`, that source is served locally as `LocalRuntime(source)`;
otherwise (mixed sources, or rows loaded from a file or the platform with no source) it falls back to
`HUDRuntime()`. Pass a runtime explicitly the moment you want something else.
</Note>

To deploy an environment to the platform and run against it, see
[running an eval](/v6/guides/running-an-eval) and
[deploying to the platform](/v6/guides/creating-an-environment#deploying-to-the-platform).

## RuntimeConfig

`RuntimeConfig` carries the construction hints a container-based runtime needs: which image, how much
hardware, and what timeouts. Set it on the runtime (`runtime_config=`) or per row on
[`Task.runtime_config`](/v6/reference/tasks#task); the runtime merges the two and applies what it
supports.

```python
from hud.eval import RuntimeConfig, RuntimeResources, RuntimeGPU, RuntimeLimits

RuntimeConfig(
    image="my-env",
    resources=RuntimeResources(cpu=4, memory_mb=8192, gpu=RuntimeGPU(type="A100", count=1)),
    limits=RuntimeLimits(startup_timeout_s=300, run_timeout_s=1800),
)
```

| Field | Description |
|-------|-------------|
| `image` | Image to run. |
| `resources` | `RuntimeResources(cpu, memory_mb, gpu=RuntimeGPU(type, count))`. |
| `limits` | `RuntimeLimits(startup_timeout_s, run_timeout_s)`. |

Support differs per runtime: `DockerRuntime`, `ModalRuntime`, and `DaytonaRuntime` accept it (Docker
ignores `limits`; Daytona ignores `run_timeout_s` and resource overrides when booting from a snapshot).
`LocalRuntime` and `HUDRuntime` reject a per-task `runtime_config`.

## Runtime directory

The constructor for each built-in runtime:

### `LocalRuntime`

```python
LocalRuntime(path, *, env=None, ready_timeout=120.0)
```

- **`path`** - `.py` file (or directory) that declares the env. The child's working directory is the source's directory, so sibling imports and relative data paths resolve.
- **`env`** - pin a specific env name when the source declares more than one. Defaults to the placed task's env.
- **`ready_timeout`** - seconds to wait for the child to start serving.

### `DockerRuntime`

```python
DockerRuntime(image=None, *, port=8765, run_args=(), runtime_config=None)
```

- **`image`** - image name to run; shorthand for `runtime_config.image`.
- **`port`** - port the image's CMD serves inside the container (the scaffolded `Dockerfile.hud` serves `8765`).
- **`run_args`** - extra `docker run` flags, e.g. `["--gpus", "all"]` or `["-e", "KEY=VAL"]`.
- **`runtime_config`** - a `RuntimeConfig` (image, resources) for finer control.

### `ModalRuntime`

```python
ModalRuntime(image_name=None, *, image=None, command=None, app_name="hud-envs", workdir=None, port=8765, runtime_config=None, env_vars=None)
```

- **`image_name`** - published Modal image name (the preferred durable handle), e.g. `ModalRuntime("hud-libero-env")`.
- **`image`** - an `Image` to build lazily on first use, as an escape hatch.
- **`command`** - override the serving command (defaults to the scaffolded `hud serve` entrypoint).
- **`workdir`** - working directory inside the sandbox. Left unset, Modal keeps the image's `WORKDIR`.
- **`app_name`** / **`port`** / **`env_vars`** - Modal app name, in-sandbox serving port, and extra environment variables.

Requires the `modal` extra and a configured token.

### `DaytonaRuntime`

```python
DaytonaRuntime(snapshot_name=None, *, image=None, command=None, workdir="/app", port=8765, ssh_host="ssh.app.daytona.io", ssh_expires_minutes=1440, runtime_config=None)
```

- **`snapshot_name`** - Daytona snapshot to boot from (the durable handle).
- **`image`** - Dockerfile/registry ref to build the snapshot once if it's missing. Resources (cpu/memory/gpu) live on the snapshot.
- **`workdir`** / **`port`** - guest working directory and in-sandbox serving port.
- **`ssh_host`** / **`ssh_expires_minutes`** - SSH tunnel settings (Daytona exposes services over an SSH local-forward).

### `HUDRuntime`

```python
HUDRuntime(*, run_timeout=3600.0, runtime_url=None)
```

- **`run_timeout`** - bound on one rollout end to end, including instance startup.
- **`runtime_url`** - override the runtime endpoint the tunnel connects to.

The SDK leases your deployed env by name and tunnels to its control channel; the agent loop runs local.

### `HostedRuntime`

```python
HostedRuntime(*, poll_interval=5.0, run_timeout=3600.0)
```

- **`poll_interval`** - seconds between trace-status polls while the rollout runs remotely.
- **`run_timeout`** - bound on one rollout end to end, including instance provisioning and queueing.

Where `HUDRuntime` runs the agent loop locally against a tunneled env, `HostedRuntime` runs the
**whole rollout** off-box: the platform leases an instance, brings the env's container up on it, and
runs the agent right next to it. This process only submits the rollout and polls its trace to
completion. It requires a gateway agent that can serialize its identity (Claude/OpenAI/Gemini).

### `Runtime`

```python
Runtime(url, params=..., config=...)
```

- **`url`** - control-channel address of an already-running substrate (e.g. `tcp://host:8765`).
- **`params`** - connection-time data a transport may need (auth token, sandbox id).

## Run on your own infra

A **runtime is just a function**: given a task, start a container somewhere and yield its
control-channel URL. That one function is the whole integration surface for any provider - Modal, E2B,
Runloop, your own Kubernetes:

```python run.py
from contextlib import asynccontextmanager
from hud import Runtime

@asynccontextmanager
async def my_runtime(task):
    sandbox = await start_my_sandbox(image="my-env")   # your infra brings it up
    try:
        yield Runtime(f"tcp://{sandbox.host}:{sandbox.port}")
    finally:
        await sandbox.terminate()                       # ...and tears it down

await taskset.run(agent, runtime=my_runtime)
```

`DockerRuntime` and the rest are just built-in versions of this. Anything that starts your image and
hands back a URL plugs in with no change to the environment or the task - that's what "run anywhere"
means concretely. Constructed directly, `Runtime(url)` yields itself with a no-op lifecycle, since
whoever provisioned the substrate owns teardown.

Placement can also vary per task: a runtime is called once per rollout with the task row being placed,
so one callable can route heavier rows to heavier substrates.
