TOXIC-AUDIO-DETECTION - Project Under Construction 🏗️

🚨 Warning: This project is currently undergoing restructuring. 😎

Please avoid using it at the moment and patiently await further updates.

Anticipated completion in the next couple of months. 😎 🚀

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Development 😎 🏗️

Prerequisites

Ensure that pyenv is installed on your local machine before proceeding.

How to Use

To make use of this project, follow the steps below:

Create a Virtual Environment with Pipenv

pyenv virtualenv 3.10 toxic-audio

Activate the virtual environment before working on the project.

pyenv local toxic-audio

Install the dependencies

make develop

Environment Variables

Create a .env file in the root directory of the project. Add the necessary environment variables; refer to .env.example for the required variables.

# .env
KAGGLE_USERNAME=your_kaggle_username
KAGGLE_KEY=your_kaggle_api_key
DATASET_RAW=fangfangz/audio-based-violence-detection-dataset
PREFECT_API_DATABASE_CONNECTION_URL=postgresql+asyncpg://postgres:postgres@database:5432/prefect
PREFECT_API_URL=http://127.0.0.1:4200/api
MLFLOW_TRACKING_URI=http://localhost:5000
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres
POSTGRES_DB=prefect

Commands

To fetch the raw data, execute the following command:

python src/scripts/fetch_raw_data.py

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🚀 Windows Instructions 🚀

bug fix on notebook - docker container

1. use git bash as of now
2. change the token as follows in line 8 c.NotebookApp.token = "" file location toxic-audio-detection\conf\.jupyter\jupyter_notebook_config.py
3. save it and build the docker image
4. run the container from git bash

tadah!! it works right? 🥳

Install pyenv for Windows 😎

Run a PowerShell terminal as an administrator Run the following command:

Invoke-WebRequest -UseBasicParsing -Uri "https://raw.githubusercontent.com/pyenv-win/pyenv-win/master/pyenv-win/install-pyenv-win.ps1" -OutFile "./install-pyenv-win.ps1"; &"./install-pyenv-win.ps1"

If you are getting any UnauthorizedAccess error Run the following command:

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope LocalMachine

Now re-run the above installation command. Installation complete!

After installation make sure that the pyenv variables are set in your machine environment variables. Windows > Edit the system environment variables > Environment Variables and check for the variables PYENV, PYENV_HOME, PYENV_ROOT.

The next step is to uninstall your current version of Python in Windows.

Now let's disable aliases related to Python Windows > Manage application execution aliases And disable all aliases related to Python

Pyenv for Windows is ready to be used! Please refer to this resource for the Windows pyenv commands.

https://pypi.org/project/pyenv-win/#validate

Let's install the python version for this project Open a CMD in the project toxic-audio-detection Run the following command:

pyenv install 3.10.0

Change the current python version using

pyenv global 3.10.0
pyenv local 3.10.0

Test your changes checking the python version on your shell. Output should be 3.10.0

pyenv shell 3.10.0
python -V

C:\Users\bhair\Desktop\canada_study_permit\Class_AIML\term_2\AML_2404_Lab\git_repo\toxic-audio-detection\conf.jupyter\jupyter_notebook_config.py

How to set up make command on Windows 😎

Download mingw-get for Windows https://sourceforge.net/projects/mingw/files/Installer/mingw-get-setup.exe/download

Run the installer and make sure the installation path has no whitespaces (Example: C:\MinGW) Close the installer Set mingw in your machine environment variables. Open: Windows > Edit the system environment variables > Environment Variables Edit the Path user variable and add the following path C:\MinGW\bin Save and close.

Open a terminal and run the following command

mingw-get install mingw32-make

Installation complete! For more help, watch the following step by step video. https://www.youtube.com/watch?v=taCJhnBXG_w

(alternative-choco) How to set up make command on Windows 😎

1. open your powershell with admin privileges
2. run the following command

Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))

3. close the powershell and open it again (allowing it to update)
4. run choco install make
5. it will automatically set the PATH in environment variables
6. go to the project repository and run make build_notebook and then make start_notebook

Currently --> it allows me to workon jupyter server, but the notebook is not inline with the common repo (updated_06_feb_24_evening)

Cloning the project for Windows 😎

When working on Windows you have to clone the repository using the following command (to avoid Unix chars and makefile issues)

git clone https://github.com/NILodio/toxic-audio-detection.git --config core.autocrlf=input

Install Docker Desktop for Windows 😎

https://www.docker.com/products/docker-desktop/

Building the docker container and setting up the project 😎

Now that we have pyenv, make, and the project correctly cloned for Windows we can proceed to install dependencies and build our Docker container.

Open Docker Desktop and minimize it Run the following commands in the project folder

pyenv install 3.10.0
pyenv global 3.10.0
pyenv local 3.10.0
mingw32-make install
mingw32-make develop
mingw32-make build_notebook
mingw32-make start_notebook

Open the container port: http://localhost:8888/ When asked for a token insert: 123

Now you have access to the jupyter notebooks!

Setting up the data fetcher from Kaggle 😎

Go to Kaggle.com and open your settings On the API section Create New Token. A json file name kaggle.json should start downloading. Create a new .env file in the project repository, copying it from the example.env file. Replace your user and token from the kaggle.json file into the new .env file.

Add or replace the kaggle.json file into the following path of your machine. C://Users//<Your username>//.kaggle//kaggle.json

Open a terminal in the project folder and run the following command:

python src/scripts/fetch_raw_data.py

The data should start downloading under /data within the project repository.

Important: you should be on the project's Python version using pyenv, and with the version set globally and locally. Check previous pyenv steps.

Overview 🏗️

The full setup consists of three steps:

1. Training - A training script trains a model for the Threat dataset with sklearn, training is orchestrated by prefect and the models metrics and artifacts (the actual models) are uploaded to mlflow.

2. Serving - The model is pulled and FastAPI delivers the prediction, a streamlit app serves as the user interface.

The individual services are packaged as docker containers and setup with docker compose.

How to use

Prerequisite: Install Docker

Start docker compose (from project folder)

    docker-compose --profile all build
    or
    make build

Access individual services

• Prefect http://localhost:4200
• Agent http://localhost:4200
• Minio http://localhost:9000
• Cli bash
• Mlflow http://localhost:5000
• FastAPI (to test) http://localhost:8086/docs
• Streamlit UI http://localhost:8501

Create example model

Run deployment in Prefect UI, deploy model artifacts in mlflow, tag it with "production" in mflow.

Note: The UI will only work if there is one "production" model in mlflow.

Services

1) Docker and docker compose

docker-compose.yaml contains the definitions for all services. For every service it contains the docker image (either through build if based on a Dockerfile, or through image if a remote image). Also it opens the relevant ports within your "docker compose network", so that the services can communicate with each other. Additionally, a common volume for all containers that use mlflow is created and mounted into /mlruns. For Prometheus/Grafana a few configuration files are also mounted.

To initialize all services the command docker compose up can be used from the project folder.

2+3) Training script and prefect

The training script and prefect (for orchestration) are packaged into one service.

The training script is placed under flows.

The prefect service is defined in docker-compose.yaml and is based on the Dockerfile in the prefect folder. It contains the prefect server and agent. The agent is used to execute the flows. The server is used to monitor the flows and artifacts.

Documentation

4) FastAPI

FastAPI is a framework for high-performance API. In this project I implemented a /predict endpoint. If that endpoint is queried

Please note: Currently the script will fetch the first model that is in production. It won't show any error if there is no model or there are multiple models.

7) Streamlit

Streamlit is a Python library to rapidly build UIs. The app is very simple and only passes input to the API to retrieve results.

Limitations

Multiple host machines: Kubernetes

This project is meant to be deployed on a single host machine. In practice, you might want to use Kubernetes to deploy it on multiple instances to gain more isolation and scalability. Kompose could be an option to convert your docker compose file to Kubernetes yaml.

Storage on cloud

All artifacts, logs, etc. are saved locally/on docker volumes. In practice, you would save them to the cloud.

Advanced Security

Security - of course. Authentication, SSL encryption, API authentication and what not. Good example using nginx. Example

References