# nodus.job

> `Job`: a run-to-completion container with checkpoints, recovery and outputs (resources.md §3.1).

Source: https://nodus-platform-site.pages.dev/docs/reference/python/nodus-job/
Build revision: 211ad9f836655b1c3a2668c4693e442471f28614

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`Job`: a run-to-completion container with checkpoints, recovery and outputs (resources.md §3.1).

`distributed=nodus.Distributed(...)` makes it a multi-node gang (Beta, ADR-112); logs and exec then take a `rank`.

## `Distributed`

```python
class Distributed(nodes: int | None = None, total_gpus: int | None = None, gpus_per_node: int | None = None, launcher: str | None = None, network: str | None = None, transport: str | None = None, startup_timeout: Any = None) -> None
```

A gang: `nodes` or `total_gpus`; `launcher` `Plain`, `Torchrun`, `Ray`, `Verl`; `network` and `transport`.

### `Distributed.gpus_per_node`

Type: `int | None`

### `Distributed.launcher`

Type: `str | None`

### `Distributed.network`

Type: `str | None`

### `Distributed.nodes`

Type: `int | None`

### `Distributed.spec`

```python
spec() -> Obj
```

### `Distributed.startup_timeout`

Type: `Any`

### `Distributed.total_gpus`

Type: `int | None`

### `Distributed.transport`

Type: `str | None`

## `Job`

```python
class Job(obj: Obj) -> None
```

### `Job.attempts`

```python
attempts() -> list[View]
```

### `Job.cancel`

```python
cancel() -> None
```

### `Job.create`

```python
create(*, name: str | None = None, image: Any = None, command: list[str] | None = None, args: list[str] | None = None, source: str | os.PathLike[str] | Mapping[str, Any] | None = None, gpu: Any = None, cpu: Any = None, memory: Any = None, disk: Any = None, env: dict[str, str] | None = None, secrets: list[Any] | None = None, volumes: Mapping[str, Any] | None = None, workdir: str | None = None, network: _spec.Egress | None = None, checkpoint: Any = None, timeout: Any = None, expected_duration: Any = None, interruptible: Any = None, region: Any = None, profile: Any = None, max_cost: Any = None, completions: int | None = None, parallelism: int | None = None, distributed: Distributed | None = None, outputs: Mapping[str, str] | None = None, labels: dict[str, str] | None = None, allow_large_source: bool = False, project: str | None = None) -> _Job
```

### `Job.estimate`

```python
estimate() -> View
```

The Job’s `status.estimate`: cost p50/p90, start ETA, hold (and topology and gang hold for gangs).

### `Job.exec`

```python
exec(*command: str, pty: bool = False, rank: int | None = None, index: int | None = None) -> _Process
```

### `Job.files`

Type: `_Files`

### `Job.from_name`

```python
from_name(name: str, project: str | None = None) -> _Job
```

### `Job.logs`

```python
logs(follow: bool = False, rank: int | str | None = None, **params: Any) -> AsyncIterator[str]
```

Log lines; for a gang, `rank=n` reads one rank and `rank="all"` merges them with `[r<n>]` prefixes.

### `Job.name`

Type: `str`

### `Job.outputs`

Type: `_Outputs`

`job.outputs["model"].download("./model")`.

### `Job.resume`

```python
resume() -> None
```

### `Job.run`

```python
run(**kwargs: Any) -> _Job
```

Create the Job and return its handle (the same as `create`); `.wait()` blocks until it finishes.

### `Job.status`

```python
status() -> View
```

### `Job.suspend`

```python
suspend() -> None
```

### `Job.wait`

```python
wait(timeout: float | None = None) -> View
```

Block until the Job finishes; raises `JobFailed` with the exit code and log tail when it fails.

## `Output`

```python
class Output(parent: Any, name: str, kind: str = 'Job') -> None
```

### `Output.download`

```python
download(path: str | os.PathLike[str], index: int | None = None) -> Path
```

Save the output at `path`: the sha256 from `X-Nodus-SHA256` is verified, then the file is renamed in.

### `Output.name`

Type: `str`

## `Outputs`

```python
class Outputs(parent: Any, kind: str = 'Job') -> None
```

Declared and collected outputs of a Job, TrainingJob or AgentRun: `outputs["name"].download(path)` for one, `outputs.download(dir)` for all of them, `outputs.list()`.

### `Outputs.download`

```python
download(path: str | os.PathLike[str], prefix: str = '') -> Path
```

Save every output named under `prefix` (all by default) into the directory `path`, each verified, at its name below `prefix`: `outputs.download("./adapter", prefix="adapter/")`.

### `Outputs.list`

```python
list() -> list[Obj]
```