consecution
Pipeline builder
A simple pipeline abstraction for building data processing workflows with Python
A pipeline abstraction for Python
168 stars
9 watching
14 forks
Language: Python
last commit: over 3 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
druths/xp | A tool for creating flexible and self-documenting data science pipelines | 56 |
ropensci/targets | A tool for creating reproducible data science pipelines in R. | 940 |
zorbash/opus | A framework for building pluggable business logic pipelines with a focus on modular and composable components. | 361 |
kirillseva/ruigi | A tool for designing and managing data processing pipelines in R. | 42 |
robocorp/rcc | A tool for creating and managing isolated Python environments for automation | 3 |
pipefunc/pipefunc | Automates and simplifies the creation of function pipelines for efficient execution of scientific workflows. | 215 |
minyus/pipelinex | A Python package to build and experiment with machine learning pipelines using Kedro, MLflow, and other tools | 224 |
johnsonc/lambdo | A workflow engine for unifying feature engineering and machine learning operations in data analysis pipelines | 1 |
kinto-b/makepipe | A tool for constructing simple pipelines in R with minimal overheads. | 30 |
calebwin/pipelines | A language and runtime for crafting massively parallel data pipelines | 374 |
rosineygp/mkdkr | A framework that allows developers to build and run CI/CD pipelines using a Makefile and Docker, with support for various pipeline engines. | 369 |
compas-dev/compas_fab | Facilitates planning and execution of robotic fabrication processes using Python and existing software libraries | 109 |
databiosphere/toil | A workflow management system designed to efficiently run pipelines in various environments. | 901 |
msoucy/dproto | Allows mixing protocol buffer files into D code at compile time to create structures. | 37 |
rucaibox/comvint | Creating synthetic visual reasoning instructions to improve the performance of large language models on image-related tasks | 18 |