https://github.com/itrauco/experiments-test
Minimal sandbox for isolating and testing core machine learning workflow logic across Jupyter notebooks. Used to rebuild clean, reproducible foundations for future MLOps development.
https://github.com/itrauco/experiments-test
computer-vision conda-environment experimental jupyter machine-learning minimal-example ml-workflows mlops model-training notebook-development notebook-isolation python reproducible-research sandbox workflow-prototyping
Last synced: 10 months ago
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Minimal sandbox for isolating and testing core machine learning workflow logic across Jupyter notebooks. Used to rebuild clean, reproducible foundations for future MLOps development.
- Host: GitHub
- URL: https://github.com/itrauco/experiments-test
- Owner: iTrauco
- Created: 2025-06-23T00:15:20.000Z (about 1 year ago)
- Default Branch: develop
- Last Pushed: 2025-06-23T02:01:01.000Z (about 1 year ago)
- Last Synced: 2025-06-23T02:36:14.411Z (about 1 year ago)
- Topics: computer-vision, conda-environment, experimental, jupyter, machine-learning, minimal-example, ml-workflows, mlops, model-training, notebook-development, notebook-isolation, python, reproducible-research, sandbox, workflow-prototyping
- Language: Shell
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Baseline ML Workflow Skeleton
**Repository** → [experiments-test](https://github.com/iTrauco/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.
---
## Table of Contents
* [Scope](#scope)
* [Upstream Integration](#upstream-integration)
* [Notebook Tools Installation](#notebook-tools-installation)
* [⚠️ Development Status](#️-development-status)
* [Reproducibility Framework](#reproducibility-framework)
* [Environment Setup](#environment-setup)
* [Environment Details](#environment-details)
* [Environment Management](#environment-management)
---
## 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](https://github.com/iTrauco/traffic-vision-v0.4)
* Current unstable work lives in → [feature/experiments-framework](https://github.com/iTrauco/traffic-vision-v0.4/tree/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`.
---
## Notebook Tools Installation
```bash
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.
---
## ⚠️ 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.
---
## 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:
```bash
git clone https://github.com/iTrauco/experiments-test.git
cd experiments-test
```
2. Create the Conda environment:
```bash
conda create -n traffic-vision-env python=3.11 -y
```
3. Activate the environment:
```bash
conda activate traffic-vision-env
```
4. Install baseline packages:
```bash
conda install -c conda-forge jupyter numpy pandas matplotlib seaborn scikit-learn opencv -y
```
5. Install deep learning and computer vision packages:
```bash
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install ultralytics supervision
```
6. Launch Jupyter Notebook:
```bash
jupyter notebook
```
7. Access the notebook in your browser via the URL displayed in the terminal.
---
### Environment Details
The environment includes essential data science and computer vision packages:
* [Python 3.11](https://www.python.org/downloads/release/python-3110/)
* [Jupyter Notebook](https://jupyter.org/documentation)
* [pandas](https://pandas.pydata.org/docs/) & [numpy](https://numpy.org/doc/stable/) for data manipulation
* [matplotlib](https://matplotlib.org/stable/index.html) & [seaborn](https://seaborn.pydata.org/) for visualization
* [scikit-learn](https://scikit-learn.org/stable/documentation.html) for traditional ML algorithms
* [OpenCV](https://docs.opencv.org/4.x/) for image and video processing
* [PyTorch](https://pytorch.org/docs/stable/index.html) for deep learning model development
* [Ultralytics](https://docs.ultralytics.com/) for YOLO object detection
* [Supervision](https://supervision.roboflow.com/) for object tracking utilities
---
### Environment Management
For collaborators who enhance the environment with additional packages:
```bash
# 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.