kubicle

Name	kubicle JSON
Version	0.1.29 JSON
	download
home_page	None
Summary	Background jobs on Kubernetes
upload_time	2024-10-25 21:00:48
maintainer	None
docs_url	None
author	Patrick Barker
requires_python	<4.0,>=3.10
license	MIT
keywords
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # Kubicle

Python background jobs on Kubernetes

-> No runtime dependencies, simply run Python functions on Kubernetes and get the results back.

## Installation

```
pip install kubicle
```

## Usage

Define a python function with inputs and an output that use [pydantic](https://docs.pydantic.dev/latest/) `BaseModel`

```py
from pydantic import BaseModel

class Foo(BaseModel):
    a: int
    b: str

class Bar(BaseModel):
    c: float
    d: bool


def call_me_remote(foo: Foo) -> Bar:
    ...

```

Run as a Kubernetes job

```py
from kubicle import Runner

os.environ["JOB_IMG"] = "foo/bar:latest"

runner = Runner()

job = runner.run(call_me_remote, Foo(a=1, b="hello"))

```

Wait for the job to finish

```py
from kubicle import JobStatus

while True:
    job.refresh()
    if job.status == JobStatus.FINISHED:
        break
```

Get the result

```py
bar = Bar.model_validate_json(job.result)
```

### Local

When in development it can be handy to run the job locally.

Do this by setting env var `JOB_LOCAL=true` or passing the parameter on object creation `Runner(local=true)` and the job will execute using multiprocessing.

## Backends

Job storage can be backed by:

- Sqlite
- Postgresql

Sqlite will be used by default. To use postgres simply configure the env vars:

```sh
DB_TYPE=postgres
DB_NAME=jobs
DB_HOST=localhost
DB_USER=postgres
DB_PASS=abc123
```

            

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