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.dvc Configure default HTTP remote (read-only), add README Sep 4, 2019
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.gitignore Create training stage Sep 4, 2019
README.md
auc.metric Evaluate bigrams model Sep 4, 2019
evaluate.dvc Evaluate bigrams model Sep 4, 2019
featurize.dvc Reproduce model using bigrams Sep 4, 2019
prepare.dvc
train.dvc

README.md

DVC Get Started

This is an auto-generated repository for use in https://dvc.org/doc/get-started. Please report any issues in its source project, example-repos-dev.

Get Started is a step-by-step introduction into basic DVC concepts. It doesn't go into details much, but provides links and expandable sections to learn more.

Note that this project imports a dataset from https://github.com/iterative/dataset-registry.

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.

Installation

Start by cloning the project:

$ git clone https://github.com/iterative/example-get-started
$ cd example-get-started

Now let's install the requirements. But before we do that, we strongly recommend creating a virtual environment with a tool such as virtualenv:

$ virtualenv -p python3 .env
$ source .env/bin/activate
$ pip install -r src/requirements.txt

This DVC project comes with a preconfigured DVC remote storage that holds raw data (input), intermediate, and final results that are produced. This is a read-only HTTP remote.

$ dvc remote list
storage https://remote.dvc.org/get-started

You can run dvc pull to download the data:

$ dvc pull

Running in your environment

Run dvc repro to reproduce the pipeline:

$ dvc repro evaluate.dvc

dvc repro requires a target stage file (DVC-file) to reconstruct and regenerate a pipeline. In this case we use evaluate.dvc, the last stage in this project's pipeline.

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 a "local remote" on your file system:

This kind of remote is located in the local file system, but is external to the DVC project.

$ mkdir -P /tmp/dvc-storage
$ dvc remote add local /tmp/dvc-storage

You should now 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 initialized.
  • 1-initialize: DVC has been initialized. .dvc/ with the cache directory created.
  • 2-remote: Remote HTTP storage initialized. It's a shared read only storage that contains all data artifacts produced during next steps.
  • 3-add-file: Raw data file data.xml downloaded and put under DVC control with dvc add. First DVC-file (.dvc file extension) created.
  • 4-source: Source code downloaded and put under Git control.
  • 5-preparation: First stage file (DVC-file) created using dvc run. It transforms XML data into TSV.
  • 6-featurization: Feature extraction stage created. It takes data in TSV format and produces two .pkl files that contain serialized feature matrices.
  • 7-train: Model training stage created. It produces model.pkl file – the actual result that can then get deployed to an app that implements NLP classification.
  • 8-evaluate: Evaluation stage. Runs the model on a test dataset to produce its performance AUC value. The result is dumped into a DVC metric file so that we can compare it with other experiments later.
  • 9-bigrams-model: 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, regenerating the model with the updated data.
  • 10-bigrams-experiment: Reproduce the evaluation stage with the bigrams based model.

There are two additional tags:

  • baseline-experiment: First end-to-end result that we have performance metric for.
  • bigrams-experiment: Second experiment (model trained using bigrams features).

These tags can be used to illustrate -a or -T options across different DVC commands.

Project structure

The data files, DVC-files, and results change as stages are created one by one. After cloning and using dvc pull to download data under DVC control, the workspace should look like this:

$ tree
.
├── auc.metric            # <-- DVC metric compares baseline and bigrams
├── data                  # <-- Directory with raw and intermediate data
│   ├── features          # <-- Extracted feature matrices
│   │   ├── test.pkl
│   │   └── train.pkl
│   └── prepared          # <-- Processed dataset (split and TSV formatted)
│       ├── test.tsv
│       └── train.tsv
│   ├── data.xml          # <-- Initial XML StackOverflow dataset (raw data)
│   ├── data.xml.dvc
├── evaluate.dvc          # <-- DVC-files in the project root describe pipeline
├── featurize.dvc
├── model.pkl
├── prepare.dvc
├── src                   # <-- Source code to run the pipeline stages
│   ├── evaluate.py
│   ├── featurization.py
│   ├── prepare.py
│   └── train.py
│   └── requirements.txt  # <-- Python dependencies needed in the project
└── train.dvc
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