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README.md 3.5 KB

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Dvc + Streamlit = ❤️

DVC + Streamlit = Love

This repository is an example that demonstrates how dvc together with streamlit can help tracking the model performance during R&D exploration.

The python code is not the purpose of this repository. It is adapted from the transfer learning Tensorflow tutorial.

Data, metrics, model weights produced during the training and evaluation processed are tracked using dvc while a streamlit app allows to visually explore model predictions and compare trained models.

Installation

Requirements

  • python >= 3.7.1,< 3.10
  • For GPU support for Tensorflow 2.4.x: Nvidia drivers > 450 and Cuda >= 11

Install dependencies

  1. [Optional] Install poetry if you don't have it already.
  2. Install dependencies: poetry install

DVC Commands

The repository contains a single dvc pipeline that looks like this :

Dcv pipeline

Stages description:

# Stage Name Description
1 download_dataset Download the cat_vs_dogs dataset to data/raw folder
2 split_dataset The cat vs dogs has no test subset. This stage keeps the train subset as is and splits the val subset into val and test subsets. Then, it copies images in train / val / test subfolders in data/dataset
3 train Train a classifier using transfer learning from a pre-trained network
4 evaluate Compute accuracy of the trained model on the test subset

Useful dvc commands:

Command Description
dvc pull Pull all the data: dataset images, model weights, etc
dvc repro Relaunch the whole pipeline. Use -f to force pipeline execution or -s to launch a single stage.
dvc plots show data/evaluation/predictions.csv --out data/evaluation/confusion.html Generate confusion matrix using the dvc predefined template.
dvc dag --full --dot | dot -Tpng -o docs/images/dvc-pipeline.png Regenerate the pipeline graph above. The graphviz package is required.

To go further, see the dvc CLI reference.

⚠️ A note on dvc remote storage: remote storage is the Sicara's public s3 bucket (see dvc config file). By default, you have permission to read (dvc pull) but you cannot write (dvc push). If you want to run experiments and save your result with dvc push, consider adding your own dvc remote.

Streamlit Dashboard

Launch the Streamlit app: streamlit run st_scripts/st_dashboard.py

Open you browser, you should see the Streamlit app :

Streamlit App

Run with docker

  1. Build the docker image: docker build -t dvc-streamlit-example .
  2. Run the docker: docker run --gpus all --rm -v $PWD:/tmp --shm-size=1g dvc-streamlit-example ${CMD}. For instance, to relaunch the training pipeline:
    docker run --gpus all --rm -v $PWD:/tmp --shm-size=1g dvc-streamlit-example dvc repro
    
Tip!

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