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Integration:  dvc git github
Mritunjay1121 c07a707c97
Ml flow in research/st_04.py, streamlit page, docker updated
1 year ago
c0322c9b48
dvc initialised
1 year ago
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req updates
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Stage 3 ran success
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config.yaml
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c07a707c97
Ml flow in research/st_04.py, streamlit page, docker updated
1 year ago
c07a707c97
Ml flow in research/st_04.py, streamlit page, docker updated
1 year ago
src
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Stage 4 evaluation all files
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req updates
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dvc initialised
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Ml flow in research/st_04.py, streamlit page, docker updated
1 year ago
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Create LICENSE
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c07a707c97
Ml flow in research/st_04.py, streamlit page, docker updated
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dvc lock and gitignore
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Evaluation stage files with ml flow
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requirements installed
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setup complete
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c07a707c97
Ml flow in research/st_04.py, streamlit page, docker updated
1 year ago
c07a707c97
Ml flow in research/st_04.py, streamlit page, docker updated
1 year ago
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setup complete
1 year ago
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setup complete
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setup complete
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requiremts satisfied and tox intitialised
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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

img

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

MLFLOW_TRACKING_URI = https://dagshub.com/Mritunjay1121/DEEPCNN_CLASSIFIER.mlflow MLFLOW_TRACKING_USERNAME=Mritunjay1121 MLFLOW_TRACKING_PASSWORD=01a8c0c421c701dc3b9ece7eccb51ca5e0f38ef2

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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