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MLflow for experiment tracking and DVC for ML Pipeline tracking.
Tasks
Clone the repository
git clone https://github.com/MartinKalema/Kidney-Disease-Classification-MLflow-DVC.git
Create a conda environment after opening the repository and activate it
conda create -n kidney python=3.8 -y
conda activate kidney
Install the requirements
pip install -r requirements.txt
This Project is connected to Dagshub so all my experiments are sent to dagshub and can be viewed on dagshub itself or on the mlflow platform integrated there.
Do not set the tracking uri using,
mlflow.set_tracking_uri()
All experiments will be stored inside an auto generated folder called mlruns. Use the command below to view them in the mlflow web interface
mlflow ui
Connect your github account to DagsHub @ https://dagshub.com
Before running an experiment add the mlflow uri configs as shown below.
export MLFLOW_TRACKING_URI=https://dagshub.com/kalema3502/Kidney-Disease-Classification-MLflow-DVC.mlflow
export MLFLOW_TRACKING_USERNAME=kalema3502
export MLFLOW_TRACKING_PASSWORD=fb3845efcc3b2e46a4157b1d2c977a21e02dd16e
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