| Name | tinypipeline JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | |
| Summary | A tiny mlops library for building machine learning pipelines on your local machine. |
| upload_time | 2023-03-21 03:14:48 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
mlops
machine learning
pipelines
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
No coveralls.
|
# tinypipeline
## Overview
`tinypipeline` is a tiny mlops library that provides a simple framework for organizing your machine learning pipeline code into a series of steps. It does not handle networking, I/O, or compute resources. You do the rest in your pipeline steps.
## Installation
```
$ pip install tinypipeline
```
## Usage
`tinypipeline` exposes two main objects:
- `pipeline`: a decorator for defining your pipeline. Returns a `Pipeline` instance.
- `step`: a decorator that is used to define individual pipeline steps. Returns a `Step` instance.
Each object requires you provide a `name`, `version`, and `description` to explicitly define what pipeline you're creating.
The `Pipeline` object that is returned from the decorator has a single method: `run()`.
## API
If you'd like to use this package, you can follow the `example.py` below:
```python
from tinypipeline import pipeline, step
# define all of the steps
@step(name='step_one', version='0.0.1', description='first step')
def step_one():
print("Step function one")
@step(name='step_two', version='0.0.1', description='second step')
def step_two():
print("Step function two")
@step(name='step_three', version='0.0.1', description='third step')
def step_three():
print("Step function three")
@step(name='step_four', version='0.0.1', description='fourth step')
def step_four():
print("Step function four")
# define the pipeline
@pipeline(
name='test-pipeline',
version='0.0.1',
description='a test tinypipeline',
)
def pipe():
# run the steps in the defined order
return [
step_one,
step_two,
step_three,
step_four,
]
pipe = pipe()
pipe.run()
```
You can also define steps using a dictionary, where each key of the dictionary
is a step to run, and the values are steps that run after the step named in the key.
```python
# define the pipeline
@pipeline(
name='test-pipeline',
version='0.0.1',
description='a test tinypipeline',
)
def pipe():
# run the steps in the defined order of the graph
return {
step_one: [step_two, step_four],
step_two: [step_three, step_four]
}
```
**Output**:
You can run the `example.py` like so:
```console
$ python example.py
+-------------------------------------------------------------------+
| Running pipeline: Pipeline(name='test-pipeline', version='0.0.1') |
+-------------------------------------------------------------------+
Running step [step_one]...
Step function one
Step [step_one] completed in 0.000325 seconds
Running step [step_two]...
Step function two
Step [step_two] completed in 0.000286 seconds
Running step [step_three]...
Step function three
Step [step_three] completed in 0.000251 seconds
Running step [step_four]...
Step function four
Step [step_four] completed in 0.000313 seconds
```
## Running tests
Tests are run using pytest. To run the tests you can do:
```console
$ pip install pytest
$ pytest
```
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"description": "# tinypipeline\n\n## Overview\n\n`tinypipeline` is a tiny mlops library that provides a simple framework for organizing your machine learning pipeline code into a series of steps. It does not handle networking, I/O, or compute resources. You do the rest in your pipeline steps.\n\n## Installation\n\n```\n$ pip install tinypipeline\n```\n\n## Usage\n\n`tinypipeline` exposes two main objects:\n- `pipeline`: a decorator for defining your pipeline. Returns a `Pipeline` instance.\n- `step`: a decorator that is used to define individual pipeline steps. Returns a `Step` instance.\n\nEach object requires you provide a `name`, `version`, and `description` to explicitly define what pipeline you're creating.\n\nThe `Pipeline` object that is returned from the decorator has a single method: `run()`.\n\n## API\n\nIf you'd like to use this package, you can follow the `example.py` below:\n\n```python\nfrom tinypipeline import pipeline, step\n\n# define all of the steps\n@step(name='step_one', version='0.0.1', description='first step')\ndef step_one():\n print(\"Step function one\")\n\n@step(name='step_two', version='0.0.1', description='second step')\ndef step_two():\n print(\"Step function two\")\n\n@step(name='step_three', version='0.0.1', description='third step')\ndef step_three():\n print(\"Step function three\")\n\n@step(name='step_four', version='0.0.1', description='fourth step')\ndef step_four():\n print(\"Step function four\")\n\n\n# define the pipeline\n@pipeline(\n name='test-pipeline', \n version='0.0.1', \n description='a test tinypipeline',\n)\ndef pipe():\n # run the steps in the defined order\n return [\n step_one, \n step_two, \n step_three, \n step_four,\n ]\n\npipe = pipe()\npipe.run()\n```\n\nYou can also define steps using a dictionary, where each key of the dictionary\nis a step to run, and the values are steps that run after the step named in the key.\n\n```python\n# define the pipeline\n@pipeline(\n name='test-pipeline', \n version='0.0.1', \n description='a test tinypipeline',\n)\ndef pipe():\n # run the steps in the defined order of the graph\n return {\n step_one: [step_two, step_four],\n step_two: [step_three, step_four]\n }\n```\n\n\n**Output**:\n\nYou can run the `example.py` like so:\n\n```console\n$ python example.py\n+-------------------------------------------------------------------+\n| Running pipeline: Pipeline(name='test-pipeline', version='0.0.1') |\n+-------------------------------------------------------------------+\n\nRunning step [step_one]...\nStep function one\nStep [step_one] completed in 0.000325 seconds\n\nRunning step [step_two]...\nStep function two\nStep [step_two] completed in 0.000286 seconds\n\nRunning step [step_three]...\nStep function three\nStep [step_three] completed in 0.000251 seconds\n\nRunning step [step_four]...\nStep function four\nStep [step_four] completed in 0.000313 seconds\n```\n\n## Running tests\n\nTests are run using pytest. To run the tests you can do:\n\n```console\n$ pip install pytest\n$ pytest\n```\n",
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