consecution
Pipeline builder
A simple pipeline abstraction for building data processing workflows with Python
A pipeline abstraction for Python
169 stars
9 watching
14 forks
Language: Python
last commit: about 4 years ago
Linked from 1 awesome list
pditommaso/awesome-pipelineRelated 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. | 949 |
 zorbash/opus | A framework for building pluggable business logic pipelines with a focus on modular and composable components. | 362 |
 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. | 230 |
 minyus/pipelinex | A Python package to build and experiment with machine learning pipelines using Kedro, MLflow, and other tools | 226 |
 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. | 31 |
 calebwin/pipelines | A language and runtime for crafting massively parallel data pipelines | 375 |
 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. | 370 |
 compas-dev/compas_fab | Facilitates planning and execution of robotic fabrication processes using Python and existing software libraries | 111 |
 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 |