Data Version Control: Tool for Iterative Machine Learning

Dmitry Petrov
Audience level: Intermediate
Topic area: Modeling
Data version control or DVC is a new 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. This presentation post walks you through an iterative process of building a machine learning model with DVC.

Transcript

1. Data Version Control:
   Tool for Iterative Machine Learning
   Dmitry Petrov, PhD
   Conference http://data-intelligence.ai
   June 2017. McLean, VA
   

   View Slide

2. Open source:
   1) Created
   github.com/dataversioncontrol/dvc
   2) Implemented Wavelet image hash
   (wHash) for
   github.com/JohannesBuchner/ima
   gehash
   Blog: fullstackml.com
   Dmitry Petrov
   @FullStackML
   Data Scientist.
   Ex-Data Scientist @Microsoft.
   PhD in CS. Ex-Researcher.
   

   View Slide

3. Agenda
   1. Dependencies in data science projects.
   2. DVC introduction.
   3. Tutorial: NLP with DVC.
   4. Beyond the Horizon.
   5. Q&A.
   

   View Slide

4. I. Dependencies in machine learning
   projects
   

   View Slide

5. Problem 1: ML is slow
   RUNNING
   RUNNING
   

   View Slide

6. Problem 2: ML is slow and iterative
   Possible solution: Makefile
   Data
   preparation
   Feature
   extraction
   Model
   training
   Model
   evaluation
   

   View Slide

7. Problem 3: Makefile is not enough
   Possible solution: Replace timestamp based model to versioning (Git
   hashes)
   Makefile is based on timestamps and keeps only the last version.
   Data
   preparation
   Feature
   extraction
   Model
   training
   Model
   evaluation
   Version: 0267f11
   Version: 33e292f
   Version: 6e9aa50
   

   View Slide

8. Problem 4: Data and code are not connected
   Git is not good for large files.
   Local
   repository
   Git server
   (GitHub)
   Local
   repository
   Local files
   Cloud
   (S3, GCP)
   Local files
   Code:
   Data:
   Data
   scientist 1
   Data
   scientist 2
   Services
   

   View Slide

9. II. DVC introduction
   

   View Slide

10. DVC basic
    Home page: http://dataversioncontrol.com
    Command: $ dvc init
    DVC directories:
    1) data/ - data files (actually symlinks to content)
    2) .cache/ - data files content
    3) .state/ - dependency graph (DAG)
    4) * .git/ - standard Git directory
    

    View Slide

11. Running your code
    Any data manipulation command has to be run through DVC.
    Import data
    Command: $ dvc run / import / remove
    

    View Slide

12. Data and .cache directories
    DVC separates data files and it’s content. Content is never stored in the Git.
    

    View Slide

13. Dependencies tracking - .state directory
    A dependencies graph (DAG) is being created automatically.
    

    View Slide

14. DVC data flows
    

    View Slide

15. III. Tutorial: NLP with DVC
    Link to the tutorial
    

    View Slide

16. IV. Beyond the Horizon
    

    View Slide

17. Important!
    DVC is a tool for modeling, not data engineering!
    DVC workflow != Airflow workflow
    DVC
    Optimizes: model creation time or
    data scientists agility
    Airflow/Luigi
    Optimizes: execution time\resources
    and reliability
    

    View Slide

18. Languages
    DVC is written in Python but the tool supports
    any languages:
    $ dvc run python code/train_model.py data/matrix-train.p 20170426 data/model.p
    $ dvc run Rscript prc_plot.R data/model.p data/matrix-test.p data/prc.jpg
    $ dvc run MyApp data/input.tsv data/output.tsv
    

    View Slide

19. Q&A
    Dmitry Petrov
    Twitter: @FullStackML
    Email: dmitry@dataversioncontrol.com
    

    View Slide