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The following template presents an iteration coming from the cookie
cutter template and includes the key building blocks for making experiments
for DVC VSCode extension.
Some of the cookie cutter template features have been dropped out for
simplicity.
Setup instructions
Install DVC VSCode extension from the marketplace. For that, Inside your
IDE, go to Extensions and search for DVC
Clone this repository
Read the documentation and customize the ´dvc.yaml´ , ´params.yaml´ files.
Open a terminal in VSCode
DVC Experiments
Managing machine learning experiments can be challenging. The ultimate goal
of DVC OpenSource extension is to make analysis, collaboration and reproducibility easier.
With the extension , we are able to visualize experiments in the table and plots.
For defining stages
We can use CLI or ´dvc.yaml´ file. Please refer here
for a template of how to build you own pipeline with DVC and here to customize your parameters.
Visit our community gem
as a common FAQ that might take place when executing you pipeline.
For defining metrics we use DVCLive python library. As a starting point we
will be using log_metrics() and sklearn_plots() methods to setup experiments
For experiments, they can be executed from the CLI or the UI coming from
the table. The main CLI command will be using will ´dvc exp run´
and ´dvc repro´ for CLI interface.
The resulting directory structure
The directory structure of your new project looks like this:
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
├── dvc.yaml <- includes pipeline stages definition. Linked to params.yaml file
└── params.yaml <- define the hyperparameters we want to iterate from that will be useful for the table
