Sinagot
Source Code: https://gitlab.com/YannBeauxis/sinagot
Sinagot is a Python lightweight workflow management framework using Ray as distributed computing engine.
The key features are:
- Easy to use: Design workflow with simple Python classes and functions without external configuration files.
- Data exploration: Access to computed data directly with object attributes, including complex type as pandas DataFrame.
- Scalable: The Ray engine enable seamless scaling of workflows to external clusters.
Installation
pip install sinagot
Getting started
import pandas as pd
import sinagot as sg
# Decorate functions to use them as workflow step
@sg.step
def multiply(df: pd.DataFrame, factor: int) -> pd.DataFrame:
return df * factor
@sg.step
def get_single_data(df: pd.DataFrame) -> int:
return int(df.iloc[0, 0])
# Design a workflow
class TestWorkflow(sg.Workflow):
raw_data: pd.DataFrame = sg.seed() # seed is input data
factor: int = sg.seed()
multiplied_data: pd.DataFrame = multiply.step(raw_data, factor=factor)
final_data: int = get_single_data.step(multiplied_data)
# Create a workspace on top of workflow for storage policy of data produced
class TestWorkspace(sg.Workspace[TestWorkflow]):
raw_data = sg.LocalStorage("raw_data/data-{workflow_id}.csv")
factor = sg.LocalStorage("params/factor")
multiplied_data = sg.LocalStorage(
"computed/multiplied_data-{workflow_id}.csv", write_kwargs={"index": False}
)
# In this example final_data is not stored and computed on demand
# Create a workspace with local storage folder root path parameter
ws = TestWorkspace("/path/to/local_storage")
# Access to a single workflow with its ID
wf = ws["001"]
# Access to item data, computed automatically if it does not exist in storage
display(wf.multiplied_data)
print(wf.final_data)
In this example, the storage dataset is structured as follows :
├── params/
│ └── factor
├── raw_data/
│ ├── data-{item_id}.csv
│ └── ...
└── computed/
├── step-1-{item_id}.csv
└── ...
And the workflow is :
Development Roadmap
Sinagot is at an early development stage but ready to be tested on actual datasets for workflows prototyping.
Features development roadmap will be prioritized depending on usage feedbacks, so feel free to post an issue if you have any requirement.
Metadata
Release files for sinagot 0.5.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| sinagot-0.5.3.tar.gz | 9.0 kB | Without provenance | Details for sinagot-0.5.3.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| sinagot-0.5.3-py3-none-any.whl | Python 3 | none | any | Details for sinagot-0.5.3-py3-none-any.whl |
Total release size: 19.3 kB
Release files / sinagot-0.5.3.tar.gz
| Download URL | sinagot-0.5.3.tar.gz |
|---|---|
| Size | 9.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
ec2f0566e733dab98bad9f2d395ac64f69863925a98613213e83d4b0f5cf8e6f
|
|
BLAKE2b-256 checksum How to use checksums |
f8eb1b031706b7a1c06df49639a137f4a618678cac6008c0caedf6ff093c4753
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.1.13 CPython/3.8.12 Linux/5.4.109+
|
Release files / sinagot-0.5.3-py3-none-any.whl
| Download URL | sinagot-0.5.3-py3-none-any.whl |
|---|---|
| Size | 10.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
28993a07504221ed99fab4888c5eaf2775761c7029fc1fdc228b988aa42b2cc7
|
|
BLAKE2b-256 checksum How to use checksums |
17e9b605047ba9e7bbb49f70bb52d7afa7ea1d44b99f07443670c127fea5982a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.1.13 CPython/3.8.12 Linux/5.4.109+
|
Security for sinagot 0.5.3
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.