{"id":30855330,"url":"https://github.com/itrauco/experiments-test","last_synced_at":"2025-09-07T11:05:14.302Z","repository":{"id":300671937,"uuid":"1006760729","full_name":"iTrauco/experiments-test","owner":"iTrauco","description":"Minimal sandbox for isolating and testing core machine learning workflow logic across Jupyter notebooks. 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Baseline ML Workflow Skeleton\n\n**Repository** → [experiments-test](https://github.com/iTrauco/experiments-test)\n\nA minimal engineering sandbox for isolating core machine learning workflow logic across notebooks.\nBuilt to strip away noise and validate raw workflow mechanics.\n\n---\n\n## Table of Contents\n\n* [Scope](#scope)\n* [Upstream Integration](#upstream-integration)\n* [Notebook Tools Installation](#notebook-tools-installation)\n* [⚠️ Development Status](#️-development-status)\n* [Reproducibility Framework](#reproducibility-framework)\n\n  * [Environment Setup](#environment-setup)\n  * [Environment Details](#environment-details)\n  * [Environment Management](#environment-management)\n\n---\n\n## Scope\n\n* Self-contained notebook logic\n* Core workflow structure only\n* No external orchestration\n* No Python data science virtual environment dependency hell conflicts\n\n## Upstream Integration\n\n* Primary development repo → [traffic-vision-v0.4](https://github.com/iTrauco/traffic-vision-v0.4)\n* 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.\n\nThis repo will drive a clean rebuild of workflow logic in the next iteration of `traffic-vision-v0.4`.\n\n---\n\n## Notebook Tools Installation\n\n```bash\ncd /path/to/notebook_tools\npip install -e .\n```\n\nThis installs the library in \"editable\" mode - any changes you make to the code are immediately available without reinstalling.\n\n---\n\n## ⚠️ Development Status\n\nAll 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.\n\n---\n\n## Reproducibility Framework\n\n### Environment Setup\n\nThis project uses a Conda environment to manage dependencies for reproducible analysis. Follow these steps to set up the environment:\n\n#### Prerequisites\n\n* Anaconda or Miniconda installed on your system\n* Git for cloning the repository\n\n#### Setup Instructions\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/iTrauco/experiments-test.git\n   cd experiments-test\n   ```\n\n2. Create the Conda environment:\n\n   ```bash\n   conda create -n traffic-vision-env python=3.11 -y\n   ```\n\n3. Activate the environment:\n\n   ```bash\n   conda activate traffic-vision-env\n   ```\n\n4. Install baseline packages:\n\n   ```bash\n   conda install -c conda-forge jupyter numpy pandas matplotlib seaborn scikit-learn opencv -y\n   ```\n\n5. Install deep learning and computer vision packages:\n\n   ```bash\n   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n   pip install ultralytics supervision\n   ```\n\n6. Launch Jupyter Notebook:\n\n   ```bash\n   jupyter notebook\n   ```\n\n7. Access the notebook in your browser via the URL displayed in the terminal.\n\n---\n\n### Environment Details\n\nThe environment includes essential data science and computer vision packages:\n\n* [Python 3.11](https://www.python.org/downloads/release/python-3110/)\n* [Jupyter Notebook](https://jupyter.org/documentation)\n* [pandas](https://pandas.pydata.org/docs/) \u0026 [numpy](https://numpy.org/doc/stable/) for data manipulation\n* [matplotlib](https://matplotlib.org/stable/index.html) \u0026 [seaborn](https://seaborn.pydata.org/) for visualization\n* [scikit-learn](https://scikit-learn.org/stable/documentation.html) for traditional ML algorithms\n* [OpenCV](https://docs.opencv.org/4.x/) for image and video processing\n* [PyTorch](https://pytorch.org/docs/stable/index.html) for deep learning model development\n* [Ultralytics](https://docs.ultralytics.com/) for YOLO object detection\n* [Supervision](https://supervision.roboflow.com/) for object tracking utilities\n\n---\n\n### Environment Management\n\nFor collaborators who enhance the environment with additional packages:\n\n```bash\n# Export the updated environment\nconda activate traffic-vision-env\nconda env export \u003e environment.yml\n```\n\nThis ensures full reproducibility across systems by preserving all dependencies and versions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fitrauco%2Fexperiments-test","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fitrauco%2Fexperiments-test","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fitrauco%2Fexperiments-test/lists"}