Get started DVC project

Ivan Shcheklein 9e05ab4590 Evaluate bigrams model 1 month ago
.dvc 2def01d870 Configure default remote 1 month ago
data 4cdad6e982 Create ML pipeline stages 1 month ago
src bbf19d999e Add source code files to repo 1 month ago
.gitignore 4cdad6e982 Create ML pipeline stages 1 month ago
README.md 4ecabbdc86 Initialize Git repository 1 month ago
dvc.lock 9e05ab4590 Evaluate bigrams model 1 month ago
dvc.yaml 1a6a633946 Create evaluation stage 1 month ago
model.pkl
params.yaml fff73261c1 Reproduce model using bigrams 1 month ago
prc.json 9e05ab4590 Evaluate bigrams model 1 month ago
scores.json 9e05ab4590 Evaluate bigrams model 1 month ago

Data Pipeline

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Git Managed File
Metric
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External File

README.md

DVC Get Started

This is an auto-generated repository for use in DVC Get Started. It is a step-by-step quick introduction into basic DVC concepts.

The project is a natural language processing (NLP) binary classifier 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.

🐛 Please report any issues found in this project here - example-repos-dev.

Installation

Python 3.6+ is required to run code from this repo.

$ 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
Data and pipelines are up to date.

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-git-init: Empty Git repository initialized.
  • 1-dvc-init: DVC has been initialized. .dvc/ with the cache directory created.
  • 2-track-data: Raw data file data.xml downloaded and tracked with DVC using dvc add. First .dvc file created.
  • 3-config-remote: Remote HTTP storage initialized. It's a shared read only storage that contains all data artifacts produced during next steps.
  • 4-import-data: Use dvc import to get the same data.xml from the DVC data registry.
  • 5-source-code: Source code downloaded and put into Git.
  • 6-prep-stage: Create dvc.yaml and the first pipeline stage with dvc run. It transforms XML data into TSV.
  • 8-ml-pipeline: Feature extraction and train stages created. It takes data in TSV format and produces two .pkl files that contain serialized feature matrices. Tain runs random forest classifier and creates the model.pkl file.
  • 9-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.
  • 10-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.
  • 11-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 tracked by DVC, the workspace should look like this:

$ tree
.
├── README.md
├── data                  # <-- Directory with raw and intermediate data
│   ├── data.xml          # <-- Initial XML StackOverflow dataset (raw data)
│   ├── data.xml.dvc      # <-- .dvc file - a placeholder/pointer to raw data
│   ├── features          # <-- Extracted feature matrices
│   │   ├── test.pkl
│   │   └── train.pkl
│   └── prepared          # <-- Processed dataset (split and TSV formatted)
│       ├── test.tsv
│       └── train.tsv
├── dvc.lock
├── dvc.yaml              # <-- DVC pipeline file
├── model.pkl             # <-- Trained model file
├── params.yaml           # <-- Parameters file
├── prc.json              # <-- Precision-recall curve data points
├── scores.json           # <-- Binary classifier final metrics (e.g. AUC)
└── src                   # <-- Source code to run the pipeline stages
    ├── evaluate.py
    ├── featurization.py
    ├── prepare.py
    ├── requirements.txt  # <-- Python dependencies needed in the project
    └── train.py