📌 Updated README.md for ml_project_setup
# 🚀 ml_project_setup
A simple Python package to **automate the creation of structured Machine Learning projects** with a single command.
## 📖 Overview
Setting up a clean and organized ML project can be time-consuming. **`ml_project_setup`** makes it effortless by generating the **entire project structure**, including essential files and dependencies.
---
## 📂 Project Structure Created
When you run `mlsetup my_project`, the following structure is created:
my_project/ │── source/ │ ├── components/ │ ├── constants/ │ ├── entity/ │ ├── pipeline/ │ ├── utility/ │ ├── exception/ │ ├── logger/ │── data/ # Placeholder for datasets │── models/ # Stores trained models │── notebooks/ # Jupyter notebooks for experiments │── .gitignore # Ignores unnecessary files │── config.yaml # Configuration settings │── Dockerfile # For containerization │── main.py # Entry point script │── README.md # Project documentation │── requirements.txt # Dependencies │── setup.py # Package setup │── setup_env.bat # Windows environment setup │── setup_env.sh # Linux/macOS environment setup
---
## 🛠 Installation
Install the package using **pip**:
```sh
pip install ml_project_setup
🚀 Usage
To create a new Machine Learning project, run:
mlsetup my_project
This will generate a structured ML project named my_project in the current directory.
➕ Select ML Framework During Setup
You'll be prompted to choose an ML framework, and the package will automatically install it:
🚀 Welcome to ML Project Setup! 🚀
Enter your project name: my_ml_project
Select ML Framework:
[1] scikit-learn (default)
[2] PyTorch
[3] TensorFlow
Enter your choice (1/2/3): 2
📦 Installing PyTorch... This may take a while.
✅ PyTorch installed successfully!
✅ Project 'my_ml_project' created with PyTorch framework!
📦 Dependencies
This package installs dependencies automatically, depending on the framework you select:
| Framework | Installed Packages |
|---|---|
| scikit-learn (default) | scikit-learn |
| PyTorch | torch, torchvision, torchaudio |
| TensorFlow | tensorflow |
If needed, you can manually install dependencies:
pip install -r requirements.txt
📝 License
This project is licensed under the MIT License. See the LICENSE file for details.
👤 Author
Developed by Amogh Pathak
📧 Contact: amogh9792@gmail.com
✨ Contribute & Improve
Have suggestions or feature requests? Feel free to open an issue or contribute on GitHub!
🔗 PyPI: https://pypi.org/project/ml-project-setup/
---
### **🔹 What’s Updated?**
✅ **Includes automatic framework installation (scikit-learn, PyTorch, TensorFlow)**
✅ **Shows example CLI prompt when running `mlsetup`**
✅ **Lists dependencies based on the selected framework**
Release files for ml-project-setup 0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ml_project_setup-0.4.tar.gz | 5.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ml_project_setup-0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.8 kB
Release files / ml_project_setup-0.4.tar.gz
| Download URL | ml_project_setup-0.4.tar.gz |
|---|---|
| Size | 5.0 kB |
| Tags | Source |
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Release files / ml_project_setup-0.4-py3-none-any.whl
| Download URL | ml_project_setup-0.4-py3-none-any.whl |
|---|---|
| Size | 5.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.5
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