Reduce ML startup time by 70% • Cut memory usage by 50% • Zero code changes required
Input is welcomed and greatly encouraged.
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Advance Prompt Guide:
Prompt simplified look at advanced propmting with context promting approaches. API and interface propmting are adressesed. It is important to understand the model you are using, its ability to parse data, properly interpret context, and map chains of thought and actions. 📖 Read the Guide -
Lazy Loading of Heavy Libraries:
Efficiency in loading AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Transformers, NumPy, Pandas, Matplotlib, OpenCV) only when they are actually used, minimizing startup time and memory footprint. -
File Structure:
Project file tree for learns to keep things organized:
ai_project/
├── app/
│ ├── __init__.py
│ ├── routes.py
│ ├── templates/
│ ├── static/
│ └── models/
├── notebooks/
├── data/
├── scripts/
├── tests/
├── config.py
├── requirements.txt
├── app.py
├── docker/
│ ├── Dockerfile
│ ├── .dockerignore
│ ├── entrypoint.sh
│ └── README.md
├── docker-compose.yml
└── README.md
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Transparent API:
Access libraries and utility functions as if they were eagerly imported, with no change to your code’s interface. -
Utility Functions:
Includes lazy-loaded helpers for model loading, data preprocessing, and model training. -
Debugging & Development Tools:
Easily inspect which modules have been loaded and force-load all dependencies for testing or warm-up. -
Clear Error Handling:
User-friendly messages when optional dependencies are missing. -
Docker Integration Guide:
Step-by-step instructions for containerizing your AI/ML application using Docker and Docker Compose. -
MCP Use and Set Up:
TBA
Import libraries and functions from lazy_init_py.py as you would from a normal package:
import lazy_init_py as odipie
# Fast startup!
print("App ready!")
# Libraries load only when accessed:
model = odipie.tensorflow.keras.Sequential([...]) # TensorFlow loads here
rf = odipie.sklearn.ensemble.RandomForestClassifier() # scikit-learn loads here
# Utility functions (also lazy):
loaded_model = odipie.load_model('model.h5') # Loads TensorFlow only if needed
processed = odipie.preprocess_data(data)
trained = odipie.train_model(X, y)# See which modules have been loaded so far
print(odipie.get_loaded_modules())
# Force-load all lazy modules (useful for testing)
odipie.force_load_all()| Resource | Description | Link |
|---|---|---|
| 📘 Wiki Home | Complete knowledge base | 🔗 Wiki |
| 🎓 Tutorials | Step-by-step guides | 🔗 Tutorials |
| 📚 API Reference | Full API documentation | 🔗 API Docs |
| 🐳 Docker Guide | Step-by-step guide for Dockerizing a Flask-based AI/ML project, including best practices for Python environments | 🔗 Docker Setup |
| 🧠 Lazy Loading Deep Dive | omprehensive explanation of lazy loading, its benefits for AI/ML, and technical implementation details | 🔗 Guide |
| 💬 Prompt Engineering | Advanced AI prompting | 🔗 Prompt Guide |
| 🏗️ Project Structure | Best practices for ML projects | 🔗 Structure Guide |
See docker-setup.md for a full walkthrough on building and running your AI/ML app in Docker, including sample Dockerfile, .dockerignore, and docker-compose.yml configurations.
This project is licensed under the Apache License 2.0.
We love contributors! 💖
- 🐛 Report bugs via Issues
- 💡 Suggest features in Discussions
- 📝 Improve documentation (especially Wiki!)
- 🧪 Add examples for your favorite frameworks
- 🎨 Enhance code quality with PRs
Never use import * in your code or in __init__.py files. Always explicitly import only the modules you need. See docker-setup.md for more security best practices.
Made with ❤️ by Odiambo
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