Metadata-Version: 2.1
Name: HackDuck
Version: 0.1.4
Summary: Machine learning data flow for reproducible data science
Home-page: https://github.com/AlexandreKempf/HackDuck
Author: Alexandre Kempf
Author-email: alexandre.kempf@cri-paris.org
License: UNKNOWN
Description: # IDEAL HACKDUCK PROJECT
        
        Run model from with a REST app (MLflow):
          - save a github folder for each project
          - can easely have predition on a bunch of data
        
        
        
        # FEATURES:
         - seed for reproducibility
         - map arguments to loop over a list
         - mlflow integration (automatic logs parameters, can log metrics or artifacts)
         - all prefect avantages
         - handle subflows
         - task bank to do basic operations
         - unit test handle by ward
        
        
        # TODO:
         - [ ] map over subflows ?
         - [ ] create a script to run it with HackDuck file.yaml --argsname argvalue ...
         - [ ] run it in a docker
         - [ ] save version for all requirements (needed to rerun the flow)
         - [ ] save python files inside mlruns/... and git them and save git commit
         - [ ] being able to rerun a previous flow (save args and kwargs and output ref)
         - [ ] put to prod thanks to travis CI that create the MLflow git repo
         - [ ] generate examples for people to use
        
        
        # use it
        ```python
        from HackDuck import run_flow
        config = yaml.load(open('/home/alex/awesome/HackDuck/iris/flows/iris_classif_with_sub.yaml', 'r'), Loader=yaml.FullLoader)
        run_flow(config, {})
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
