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.gitignore add train stage Apr 24, 2019 add source code Apr 24, 2019
auc.metric try using bigrams Apr 24, 2019
evaluate.dvc try using bigrams Apr 24, 2019
featurize.dvc try using bigrams Apr 24, 2019
prepare.dvc add data preparation stage Apr 24, 2019
requirements.txt add source code Apr 24, 2019
train.dvc try using bigrams Apr 24, 2019

DVC Get Started

This is an auto-generated repository (please, don't create issues here, use the example-get-started-dev).

The idea of the project is a simplified version of the tutorial. It explores the natural language processing (NLP) problem of predicting tags for a given StackOverflow question. For example, we want one classifier which can predict a post that is about the Python language by tagging it python.


First, you need to download the project:

    $ git clone

Second, let's install the requirements. But before we do that, we strongly recommend creating a virtual environment with virtualenv or a similar tool:

    $ cd example-get-started
    $ virtualenv -p python3 .env
    $ source .env/bin/activate

Now, we can install requirements for the project:

    $ pip install -r requirements.txt

Running in Your Environment

This project comes with a predefined remote DVC storage that contains all input, intermediate and final results that were produced.

    $ dvc remote list

You can run dvc pull to download the data:

    $ dvc pull -r storage

and dvc repro to reproduce the pipeline:

    $ dvc repro evaluate.dvc

If you'd like to test commands like dvc push, that require write access to the remote storage, the easiest way would be to set up the local remote on your file system:

    $ dvc remote add local /tmp/dvc-storage

You should be able to run:

    $ dvc push -r local

Existing Stages

This project with the help of the Git tags reflects the sequence of actions that are run in the DVC get started guide. Feel free to checkout one of them and play with the DVC commands having the playground ready.

  • 0-empty - empty Git repository.
  • 1-initialize - DVC has been initialized. The .dvc with the cache directory created.
  • 2-remote - remote HTTP storage initialized. It is a shared read only storage that contains all data artifacts produced during next steps.
  • 3-add-file - input data file data.xml downloaded and put under DVC control with dvc add. First .dvc meta-file created.
  • 4-source - source code downloaded and put under Git control.
  • 5-preparation - first DVC stage created using dvc run. It transforms XML data into TSV.
  • 6-featurization - feature extraction step added. It also includes the split step for simplicity. It takes data in TSV format and produces two .pkl files that contain serialized feature matrices.
  • 7-train - the model training stage added. It produces model.pkl file - the actual result that can be then deployed somewhere and classify questions.
  • 8-evaluate - evaluate stage, we run it on a test dataset to see the AUC value for the model. The result is dumped into a DVC metric file so that we can compare it with other experiments later.
  • 9-bigrams - bigrams experiment, code has been modified to extract more features. We run dvc repro for the first time to illustrate how DVC can reuse cached files and detect changes along the computational graph.

There are two additional tags:

  • baseline-experiment - the first end-to-end result that we performance metric for.
  • bigrams-experiment - second version of the experiment.

Both these tags could be used to illustrate -a or -T DVC options across different commands.

Project Structure

The project files, DVC files, data files changes as you apply stages one by one, but right after you for Git clone and dvc pull to download files that are under DVC control, the structure of the project should look like this:

    ├── auc.metric           <-- DVC metric file to compare baseline and bigrams
    ├── data                 <-- directory with input and intermediate data
    │   ├── features         <-- extracted feature matrices
    │   │   ├── test.pkl
    │   │   └── train.pkl
    │   └── prepared         <-- pre-processed dataset, split and TSV formatted
    │       ├── test.tsv
    │       └── train.tsv
    │   ├── data.xml         <-- initial XML StackOverflow dataset
    │   ├── data.xml.dvc
    ├── evaluate.dvc         <-- DVC files in the project root describe pipeline
    ├── featurize.dvc
    ├── model.pkl
    ├── prepare.dvc
    ├── requirements.txt     <-- Python dependencies you need to run the project
    ├── src                  <-- sources to run the pipeline
    │   ├──
    │   ├──
    │   ├──
    │   └──
    └── train.dvc
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