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https://github.com/csurfer/pypette
Ridiculously simple flow controller for building complex pipelines
https://github.com/csurfer/pypette
multiprocessing multithreading python threads
Last synced: about 1 month ago
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Ridiculously simple flow controller for building complex pipelines
- Host: GitHub
- URL: https://github.com/csurfer/pypette
- Owner: csurfer
- License: mit
- Created: 2017-10-22T00:55:55.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-09T05:43:24.000Z (almost 2 years ago)
- Last Synced: 2024-07-22T17:38:37.750Z (about 2 months ago)
- Topics: multiprocessing, multithreading, python, threads
- Language: Python
- Homepage: https://csurfer.github.io/pypette
- Size: 568 KB
- Stars: 264
- Watchers: 11
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.rst
- License: LICENSE
Awesome Lists containing this project
- starred-awesome - pypette - Ridiculously simple flow controller for building complex pipelines (Python)
README
---
pypette (to be read as pipette) is a module which makes building pipelines
ridiculously simple, allowing users to control the flow with minimal
instructions.
## Features
- Ridiculously simple interface.
- Ability to view pipeline structure within the comfort of a terminal.
- Run pipeline in exception resilient way if needed.
- Create dependencies on pipelines easily.
- Generate a easy to view/understand report within the comfort of a terminal.
## Setup
### Using pip
```bash
pip install pypette
```
### Directly from the repository
```bash
git clone https://github.com/csurfer/pypette.git
python pypette/setup.py install
```
## Documentation
Detailed documentation can be found at https://csurfer.github.io/pypette
## Structures
### Job
The basic unit of execution, say a python method or a callable.
```python
from pypette import Job
def print_hello():
print("Hello!")
def print_hello_msg(msg):
print("Hello " + msg + "!")
# Job without arguments
j1 = Job(print_hello)
# Job with arguments specified as argument list
j2 = Job(print_hello_msg, args=("pypette is simple",))
# Job with arguments specified as key word arguments
j3 = Job(print_hello_msg, kwargs={"msg":"pypette is simple"})
```
### BashJob
The basic unit of execution, which runs a bash command.
```python
from pypette import BashJob
# Job with bash commands
b1 = BashJob(['ls', '-l'])
b2 = BashJob(['pwd'])
```
### Pipe
Structure to specify the flow in which the jobs need to be executed. The whole
interface consists of only 4 methods.
```python
from pypette import Pipe
# 1. Create a new Pipe
p = Pipe('TestPipe')
# 2. Add jobs to execute. (Assuming job_list is a list of python/bash jobs)
# To run the jobs in job_list in order one after the other where each job waits
# for the job before it to finish.
p.add_jobs(job_list)
# To run the jobs in job_list parallelly and run the next step only after all
# jobs in job list finish.
p.add_jobs(job_list, run_in_parallel=True)
# Add jobs in a builder format.
p.add_stage(job1).add_stage(job2) # To add jobs in series.
p.add_stage(job1, job2) # To add jobs in parallel.
```
### Building complex pipelines
Jobs submitted to pipeline should be callables i.e. structures which can be run.
This means python methods, lambdas etc qualify.
What about Pipe itself?
Of course, it is a callable and you can submit a pipe object to be run along
with regular jobs. This way you can build small pipelines which achieve a
specific task and then combine them to create more complex pipelines.
```python
from pypette import BashJob, Job, Pipe
def welcome():
print("Welcome user!")
def havefun():
print("Have fun!")
def goodbye():
print("Goodbye!")
# Build a simple pipeline
p1 = Pipe('Fun')
p1.add_jobs([
Job(havefun),
])
# Include simple pipeline into a complicated pipeline
p2 = Pipe('Overall')
p2.add_jobs([
Job(welcome),
p1,
Job(goodbye),
BashJob(['ls', '-l']),
BashJob(['pwd'])
])
p2.run() # This first runs welcome, then runs p1 pipeline then runs goodbye.
```
### Example pipeline
An example pipeline and its code are included in
**[examples](https://github.com/csurfer/pypette/tree/master/examples)** folder.
### Visualizing the pipeline using graph()
Pipeline objects have a method called `graph()` which helps visualize the
pipeline within the comfort of your terminal. The graph is recursive in nature
and it visualizes everything that will be run if we call `run()` on the pipe
object.
Visualizing the top-level pipeline in
[examples/basic.py](https://github.com/csurfer/pypette/tree/master/examples/basic.py)
led to the following visualization.
### Running the entire pipeline.
The only thing you need to do at this point to run the entire pipeline is to call
`run()` on your pipeline object.
### Reporting the entire pipeline.
The only thing you need to do at this point to get the report of entire pipeline
is to call `report()` on your pipeline object.
## Contributing
### Bug Reports and Feature Requests
Please use [issue tracker](https://github.com/csurfer/pypette/issues) for
reporting bugs or feature requests.
### Development
1. Checkout the repository.
2. Make your changes and add/update relavent tests.
3. Install **`poetry`** using **`pip install poetry`**.
4. Run **`poetry install`** to create project's virtual environment.
5. Run tests using **`poetry run tox`** (Any python versions which you don't have checked out will fail this). Fix failing tests and repeat.
6. Make documentation changes that are relavant.
7. Install **`pre-commit`** using **`pip install pre-commit`** and run **`pre-commit run --all-files`** to do lint checks.
8. Generate documentation using **`poetry run sphinx-build -b html docs/ docs/_build/html`**.
9. Generate **`requirements.txt`** for automated testing using **`poetry export --dev --without-hashes -f requirements.txt > requirements.txt`**.
10. Commit the changes and raise a pull request.
### Buy the developer a cup of coffee!
If you found the utility helpful you can buy me a cup of coffee using
[](https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=3BSBW7D45C4YN&lc=US¤cy_code=USD&bn=PP%2dDonationsBF%3abtn_donate_SM%2egif%3aNonHosted)