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Dmitry Petrov
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![DVC icone](https://s3-us-west-2.amazonaws.com/dvc-share/images/favicon_rounded.jpg)
DVC is an open source tool for data science projects. It orchestrates your Git code, external data files (local drive, S3, GCP Storage) and dependencies to a single reproducibly and shareable environment.
DVC is an open source tool for data science projects.
DVC makes your data science projects reproducible by automatically building data dependency graph (DAG). Your code and the dependencies could be easily shared by Git, and data - through cloud storage (AWS S3, GCP) in a single DVC environment.
# Introduction
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[Data Version Control](https://dataversioncontrol.com) or DVC is an open source tool which is designed to help data scientists keep track of their ML processes and file dependencies in the simple form of git-like commands: `dvc run python train_model.py data/train_matrix.p data/model.p`. Your existing ML processes can be easily transformed into reproducible DVC pipelines regardless of which programming language or tool was used.
This DVC introduction walks you through an iterative process of building a machine learning model with DVC using [stackoverflow posts dataset](https://archive.org/details/stackexchange).
# Tutorial
This DVC tutorial walks you through an iterative process of building a machine learning model with DVC using [stackoverflow posts dataset](https://archive.org/details/stackexchange).
First, you should initialize a Git repository and download a modeling source code that we will be using to show DVC in action:
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The code below shows how to share your code and DAG through the Git and data files through S3:
```bash
# Setup cloud settings. Example: Cloud = amazon, StoragePath=/dvc-share/projects/tag_classifier
# Setup cloud settings. Example: Cloud = AWS, StoragePath=/dvc-share/projects/tag_classifier
$ vi dvc.conf
$ git commit -am "Set up AWS path"
[master ec994b6] Set up AWS path