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Integration:  git github
98539b87df
Initial commit
2 months ago
9500549f61
evaluation done
4 weeks ago
98539b87df
Initial commit
2 months ago
fa33080d59
done with evaluation
2 weeks ago
src
b37d67435b
small changes
2 months ago
1049831f95
some modification to template
2 months ago
98539b87df
Initial commit
2 months ago
c12363ae70
done with evaluation
3 weeks ago
98539b87df
Initial commit
2 months ago
1049831f95
some modification to template
2 months ago
98539b87df
Initial commit
2 months ago
98539b87df
Initial commit
2 months ago
388cdaab09
change in init setup and req file
1 month ago
0e0b55a9d3
working agian
1 month ago
98539b87df
Initial commit
2 months ago
c12363ae70
done with evaluation
3 weeks ago
b252309677
base model config completed
1 month ago
98539b87df
Initial commit
2 months ago
1049831f95
some modification to template
2 months ago
1049831f95
some modification to template
2 months ago
1049831f95
some modification to template
2 months ago
98539b87df
Initial commit
2 months ago
b252309677
base model config completed
1 month ago
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README.md

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deep Classifier project

workflow

  1. Update config.yaml
  2. Update secrets.yaml [Optional]
  3. Update params.yaml
  4. Update the entity
  5. Update the configuration manager in src config.
  6. Update the components
  7. Update the pipeline
  8. Test run pipeline stage
  9. run tox for testing your package
  10. Update the dvc.yaml
  11. run "dvc repro" for running all the stages in pipeline

STEP 1: Set the env variable | Get it from dagshub -> remote tab -> mlflow tab

STEP 2: install mlflow

STEP 3: Set remote URI

STEP 4: Use context manager of mlflow to start run and then log metrics, params and model

Tip!

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