DVC Setup and Usage Guide
This guide will help you set up and use Data Version Control (DVC) effectively.
Introduction to DVC
DVC is a data version control tool that enables machine learning teams to manage datasets, models, and pipelines in a Git-like manner.
Prerequisites
Before using DVC, ensure that you have the following tools installed:
- Python 3.x
- Git
Installation
To install DVC, run:
pip install dvc
cd /path/to/your/project
git init
dvc init
git add .dvc
git commit -m "Initialize DVC"
dvc add data/my_dataset.csv
git add data/my_dataset.csv.dvc
git commit -m "Add dataset to DVC"
dvc remote add -d myremote s3://mybucket/dvcstore
dvc push
dvc pull
dvc add models/my_model.pkl
git add models/my_model.pkl.dvc
git commit -m "Add trained model to DVC"
dvc run -n train_model \
-d data/my_dataset.csv \
-o models/my_model.pkl \
python train.py
dvc repro