Deep learning Project

workflow

1.update your config.yaml 2.update secrets.yaml [Optional] 3.update params.yaml 4.update the entity 5.update the configuration manager in srs 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

17/9/2022

Autocomplete the following information from the command line for each stage information yourself and you can also specify TabNine::

Tox - To create the environment and test everything pytest - to check the testcase Mypy - will check the error for eg( if you are passing wrong datatype to the function) ---it called linting of datatype flake8 - It will check the standard code formatting eg: indentation ---it is called code linting

MLFLOW_TRACKING_URI=https://dagshub.com/Mukesh-areo/CNN_claasaifier.mlflow MLFLOW_TRACKING_USERNAME=Mukesh-areo MLFLOW_TRACKING_PASSWORD=434cc55976d25211145784e69290bf348ce1e14a python script.py

Project setups

1. command To create the folder structure use the following command ----Python template.py----

2. Then connect with the github To connect the local repository with git use ----git init-----

3. To create the Environment Use init_setup.sh file ----bash init-setup.sh ----

4. Activate the environment ---- conda activate ./env-----

5. Then run the Tox ------tox ---- or tox --recreate