swak
Swiss army knife for functional data-science projects.
Introduction
This package is a collection of small, modular, and composable building blocks implementing frequently occurring operations in typical data-science applications. In abstracting away boiler-plate code, it thus saves time and effort.
- Consolidate all the ways to configure your project (command-line arguments,
environment variables, and config files) with the
cliandiopackages, respectively. - Wrap the project config into a versatile
jsonobject. - Focus on writing small, configurable, modular, reusable, and testable
building blocks. Then use the flow controls in
funcflowto compose them into arbitrarily complex workflows, that are still easy to maintain and to expand. - Use the provided
pandasandpolarsbuilding blocks to get you started. - Quickly set up projects on Google BigQuery and Google Cloud as well as AWS object Storage, and
efficiently download lots of data in parallel with the
cloudsub-package. - Build powerful neural-network architectures from the elements in
ptand train your deep-learning models with early stopping and checkpointing. From feature embedding, over feature importance, to repeated residual blocks, a broad variety of options is available. - And much more ...
Installation
- Create a new virtual environment running at least
python 3.12. - The easiest way of installing
swakis from the python package index PyPI, where it is hosted. Simply typepip install swak
or treat it like any other python package in your dependency management. - If you need support for interacting with the Google Cloud Project,
in particular Google BigQuery and Google Cloud Storage, install
extra dependencies with:
pip install swak[gcp]
- Likewise, If you need support for interacting with Amazon AWS,
in particular with S3, install extra dependencies with:
pip install swak[aws]
- In order to use the subpackage
swak.pt, you need to have PyTorch installed. Because there is no way of knowing whether you want to run it on CPU only or also on GPU and, if so, which version of CUDA (or ROC) you have installed on your machine and how, it is not an explicit dependency ofswak. You will have to install it yourself, e.g., following these instructions. If you are usingpipenvfor dependency management, you can also have a look at the Pipfile in the root of theswakrepository and taylor it to your needs. Personally, I gopipenv sync --categories=cpu
for a CPU-only installation of PyTorch andpipenv sync --categories=cuda
if I want GPU support.
Usage
Try making a new repository using the swak-template as a, well, template.
Documentation
The API documentation to swak is hosted on GitHub Pages.
1 maintainer
Metadata
1 maintainer
Release files for swak 1.2.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| swak-1.2.0.tar.gz | 153.9 kB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| swak-1.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 421.3 kB
Release files / swak-1.2.0.tar.gz
| Download URL | swak-1.2.0.tar.gz |
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| Size | 153.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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| Download URL | swak-1.2.0-py3-none-any.whl |
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| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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