Baseline ML Workflow Skeleton

Repository → experiments-test

A minimal engineering sandbox for isolating core machine learning workflow logic across notebooks. Built to strip away noise and validate raw workflow mechanics.

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Table of Contents

• Scope

• Upstream Integration

• Notebook Tools Installation

• ⚠️ Development Status

• Reproducibility Framework

  • Environment Setup
  • Environment Details
  • Environment Management

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Scope

• Self-contained notebook logic
• Core workflow structure only
• No external orchestration
• No Python data science virtual environment dependency hell conflicts

Upstream Integration

• Primary development repo → traffic-vision-v0.4
• Current unstable work lives in → feature/experiments-framework — a chaotic prototype branch being deprecated.

This repo will drive a clean rebuild of workflow logic in the next iteration of traffic-vision-v0.4.

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Notebook Tools Installation

cd /path/to/notebook_tools
pip install -e .

This installs the library in "editable" mode - any changes you make to the code are immediately available without reinstalling.

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⚠️ Development Status

All modules in lib/ are early-stage development prototypes. Functionality is still being worked out — some modules may be dead code, others are spaghetti. Creating modular packages as I identify what's killing my bandwidth.

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Reproducibility Framework

Environment Setup

This project uses a Conda environment to manage dependencies for reproducible analysis. Follow these steps to set up the environment:

Prerequisites

• Anaconda or Miniconda installed on your system
• Git for cloning the repository

Setup Instructions

1. Clone the repository:

   git clone https://github.com/iTrauco/experiments-test.git
   cd experiments-test

2. Create the Conda environment:

   conda create -n traffic-vision-env python=3.11 -y

3. Activate the environment:

   conda activate traffic-vision-env

4. Install baseline packages:

   conda install -c conda-forge jupyter numpy pandas matplotlib seaborn scikit-learn opencv -y

5. Install deep learning and computer vision packages:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   pip install ultralytics supervision

6. Launch Jupyter Notebook:

   jupyter notebook

7. Access the notebook in your browser via the URL displayed in the terminal.

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Environment Details

The environment includes essential data science and computer vision packages:

• Python 3.11
• Jupyter Notebook
• pandas & numpy for data manipulation
• matplotlib & seaborn for visualization
• scikit-learn for traditional ML algorithms
• OpenCV for image and video processing
• PyTorch for deep learning model development
• Ultralytics for YOLO object detection
• Supervision for object tracking utilities

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Environment Management

For collaborators who enhance the environment with additional packages:

# Export the updated environment
conda activate traffic-vision-env
conda env export > environment.yml

This ensures full reproducibility across systems by preserving all dependencies and versions.