Welcome to the ML Project Template! 🎯 This repository is designed to keep your project code organized while providing pre-written setup scripts to help you run your project seamlessly across multiple environments:
- ✅ Jupyter Notebook Server (GPU3)
- ✅ Local Code Editors (VS Code, PyCharm, etc.)
- ✅ Google Colab
Whether you are working on data preprocessing, model training, or evaluation, this template provides a structured workflow to streamline your ML experiments.
Here's a quick breakdown of the repository contents:
| File / Directory | Purpose |
|---|---|
main.ipynb |
📓 The main Jupyter Notebook for running experiments and prototyping ML code. |
README.md |
📄 Documentation for understanding and using this template. |
.gitignore |
🚫 Ensures that large files, logs, and unnecessary cache files are not committed to Git. |
requirements.txt |
📦 Lists all required dependencies (for pip install -r requirements.txt). |
scripts/ |
🛠 Contains reusable Python scripts for preprocessing, training, and evaluation. |
results/ |
📊 Stores logs, metrics, and experiment outputs. |
models/ |
🏗 Stores trained models and checkpoints. |
data/ |
📁 Placeholder for datasets (already in .gitignore, so any datasets added here won’t be committed to Git). |
git clone https://github.com/your-org/ml-project-template.git
cd ml-project-templateTo avoid hardcoding dataset paths, create a symbolic link inside data/ pointing to your dataset location:
ln -s /absolute/path/to/dataset /path/to/ml-project-template/data/dataset_name| Where You're Running the Notebook | Dataset Setup |
|---|---|
| GPU Server (Jupyter Notebook Server) | Use a symlink (ln -s) to point to the real dataset. |
| Local Machine (VS Code, Jupyter, etc.) | Create a small sample dataset inside data/. |
| Google Colab (Cloud) | Upload a sample dataset to Google Drive inside data/. |