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Machine learning data flow for reproducible data science

Project description


Run model from with a REST app (MLflow):

  • save a github folder for each project
  • can easely have predition on a bunch of data


  • 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


  • 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

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, {})

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