deep-solutions

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

📦 Installation

Install from PyPI:

pip install deep-solutions

Core Dependencies

This package requires the following core dependencies:

• NumPy (>=1.17.0): Numerical computing
• SciPy (>=1.5.0): Scientific computing
• PyTorch (>=1.7.0): Deep learning framework
• Matplotlib (>=3.3.0): Visualization and plotting

These dependencies are automatically installed with the package.

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

Document	Description
Contributing Guide	Start here — step-by-step guide for new contributors
Developer Guide	How to clone, setup environment, and contribute
Project Structure	Directory structure, dependency management, Python version requirements
Code Standards	Commit conventions, PR workflow, merge requirements
Local Testing Guide	Using check.sh, tox, pytest, etc.
CI Workflow	GitHub Actions CI/CD documentation
Publishing Guide	How to publish to PyPI
Agent Development Guide	Technical reference for developers and AI agents

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

We'd love your help! Contributions of all kinds are welcome — bug fixes, new features, documentation improvements, and more.

👉 See our Contributing Guide for step-by-step instructions — This guide walks you through the entire contribution workflow, from setup to submitting a PR.

Quick Checklist:

• ✅ Read the Contributing Guide — it has everything you need
• ✅ Ensure Python 3.8 environment (see Project Structure)
• ✅ Run bash scripts/check.sh to verify all checks pass locally
• ✅ Follow Commit Conventions for clear commit messages
• ✅ Submit your PR with a clear description and link to related issues if applicable

Merge Process:

• All PRs are reviewed and tested via CI
• Approved PRs are merged using Squash and Merge for a clean commit history
• Your commits will be consolidated into a single, well-formatted commit following Conventional Commits

📧 Contact

• GitHub: @FrostyHec
• Repository: deep-solutions

🔗 Links

• PyPI Package
• GitHub Repository
• Issue Tracker

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