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Machine learning data flow for reproducible data science

Project description

IDEAL HACKDUCK PROJECT

Several pipelines for dataflow (Prefect):

  • nothing -> data_generation -> save to disk
  • preprocessing
  • augmentation
  • postprocessing

Model handle (Pytorch & Ignite):

  • fit -> give X and Y and learn
  • evaluate -> give X and Y, predict and return metrics
  • predict -> give X, return Y

Save logs and artifacts (MLflow):

  • save metrics during training (ignite)
  • save a bunch of data before and after each pipeline

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 ? [ ] pip package for TaskBank and save commit (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) [ ] run it in a docker [ ] put to prod thanks to travis CI that create the MLflow git repo [ ] do deep learning with it

Project details


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