Startup¶

This tutorial is necessary to run the repository with your own configurations.

Fork 24-2-mlops-project-car_object_detection, clone the repository and install all the requirements with:

git clone https://github.com/fork-name

pip install -r requirements.txt

Ensure Python version 3.12 is being used

WARNING

If you would like to run with GPU, download CUDA Toolkit 12.6 https://developer.nvidia.com/cuda-downloads

Create .env file in the root of the repository

ROBOFLOW_API_KEY=""
AWS_ACCESS_KEY_ID=""
AWS_SECRET_ACCESS_KEY=""
AWS_REGION=""
AWS_LAMBDA_ROLE_ARN=""

INFO

ROBOFLOW_API_KEY Roboflow API key to download dataset.
AWS_ACCESS_KEY_ID AWS access key id.
AWS_SECRET_ACCESS_KEY AWS secret access key.
AWS_REGION AWS region.
AWS_LAMBDA_ROLE_ARN AWS Lambda role ARN.

Create two S3 buckets. The first one will store the ONNX object detection model and the second one will store all the datasets from the data versioning.

python3 data/s3_bucket.py --bucket_model bucket-model-name --bucket_dataset bucket-dataset-name

This command will automatically save the bucket name in the .env file:

BUCKET_MODEL="bucket-model-name"
BUCKET_DATASET="bucket-dataset-name"

Add the following secrets and variables in the “Actions secrets and variables” section at settings

github env secrets

github env variables

INFO

AWS_ACCESS_KEY_ID AWS access key ID.
AWS_SECRET_ACCESS_KEY AWS secret access key.
AWS_REGION AWS region.
AWS_LAMBDA_ROLE_ARN AWS Lambda role ARN.
AWS_ACCOUNT_ID AWS account ID.
BUCKET_MODEL is the name of the bucket were the model is stored.
ECR_NAME is the name of the ECR container.
HEROKU_API_KEY API key from Heroku. Necessary for deploying the website.
HEROKU_APP_NAME NAme of the Heroku app.
HEROKU_EMAIL Email of the Heroku

In the file app/setup.sh, change to:

#!/bin/bash

mkdir -p ~/.streamlit/

echo "\
[general]\n\
email = \"your-email.com\"\n\
" > ~/.streamlit/credentials.toml

echo "\
[server]\n\
headless = true\n\
enableCORS=false\n\
port = $PORT\n\
" > ~/.streamlit/config.toml