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https://github.com/sicara/dvc-streamlit-example

Dvc + Streamlit = ❤️
https://github.com/sicara/dvc-streamlit-example

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Dvc + Streamlit = ❤️

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README 

        
Dvc + Streamlit = ❤️

===



![DVC + Streamlit = Love](./docs/images/dvc_streamlit_header.png)



This repository is an example that demonstrates how [dvc](https://dvc.org/)

together with [streamlit](https://streamlit.io/) 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](https://www.tensorflow.org/tutorials/images/transfer_learning).



Data, metrics, model weights produced during the training and evaluation processed

are tracked using [dvc](https://dvc.org/)

while a [streamlit app](https://streamlit.io/) 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](https://python-poetry.org/docs/master/#installing-with-the-official-installer) if you don't have it already.

2. Install dependencies: `poetry install`



## DVC Commands



The repository contains [a single dvc pipeline](./dvc.yaml) that looks like this :



![Dcv pipeline](./docs/images/dvc-pipeline.png)



**Stages description**:



| # | Stage Name         | Description |

|---|--------------------|-------------|

| 1 | `download_dataset` | Download the [cat_vs_dogs dataset](https://www.tensorflow.org/datasets/catalog/cats_vs_dogs) 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/plots/confusion.json).|

| `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](https://dvc.org/doc/command-reference).



> ⚠️ **A note on dvc remote storage**:

> remote storage is [the Sicara's public s3 bucket](s3://public-sicara/dvc-remotes/dvc-streamlit-example)

> (see [dvc config file](./.dvc/config)).

> 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](https://dvc.org/doc/command-reference/remote/add).



## Streamlit Dashboard



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



Open you browser, you should see the Streamlit app :



![Streamlit App](./docs/images/streamlit-inference.png)



## 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

   ```