deep-solutions

A library that provides useful tools and standard solutions for deep learning tasks.

📦 Installation

Install from PyPI:

pip install deep-solutions

Install from source (for development):

git clone https://github.com/FrostyHec/deep-solutions.git
cd deep-solutions
pip install -e ".[dev]"

🚀 Quick Start

from deep_solutions import hello_world, DeepSolution, format_output

# Simple function
message = hello_world()
print(message)  # Output: Hello from deep-solutions!

# Use DeepSolution class
solution = DeepSolution("my_solution")
result = solution.process("data")
print(result)  # Output: Processing data with my_solution

# Format output
formatted = format_output("result data", prefix="Output")
print(formatted)  # Output: Output: result data

📚 Documentation

Note: Chinese documentation is available in docs/zh-CN/

🛠️ Development

Setup Development Environment

We use the Pip-in-Conda strategy for dependency management:

• Conda manages only Python version and pip
• All package dependencies are managed in pyproject.toml
• This ensures development and production dependencies are always in sync

# Create conda environment (only Python + pip)
conda env create -f environment.yml

# Activate environment
conda activate deep-solutions

# Install package with all dependencies from pyproject.toml
pip install -e ".[dev]"

Run Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=deep_solutions --cov-report=html

Code Quality

# Format code (using Ruff)
ruff format src/ tests/

# Lint code
ruff check src/ tests/

# Type check
mypy src/

# Run all checks at once
./scripts/check.sh

📝 Features

• Core Functionality: Essential deep learning utilities
• Easy to Use: Simple and intuitive API
• Well Tested: Comprehensive test coverage
• Type Hints: Full type annotation support
• Extensible: Easy to extend with new features

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please see our Developer Guide for details.

⚠️ Important: Development must be done in Python 3.8 environment. See Project Structure for details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Run checks before commit (./scripts/check.sh)
4. Commit your changes (git commit -m 'feat: add amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request

Merge Requirements: PRs must pass all CI checks and receive at least one review approval.

📧 Contact

• GitHub: @FrostyHec
• Repository: deep-solutions

🔗 Links

• PyPI Package
• GitHub Repository
• Issue Tracker

Note: This project is in active development - the API may change in future releases.