| Name | yica-mirage JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | https://github.com/yica-ai/yica-mirage |
| Summary | YICA-Mirage: AI Computing Optimization Framework for In-Memory Computing Architecture |
| upload_time | 2025-07-24 07:16:35 |
| maintainer | None |
| docs_url | None |
| author | YICA Team |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2025 YICA Team
Permission is hereby granted, free of charge, to any person obtaining a copy
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furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
ai
optimization
compiler
triton
yica
mirage
deep-learning
in-memory-computing
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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| coveralls test coverage |
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|
# YICA-Mirage
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://badge.fury.io/py/yica-mirage)
[](https://github.com/yica-ai/yica-mirage/actions)
**YICA-Mirage** is a high-performance AI computing optimization framework designed for in-memory computing architectures. It combines the power of Mirage's universal code optimization with YICA's specialized in-memory computing optimizations to deliver exceptional performance for AI workloads.
## 🚀 Key Features
- **🧠 In-Memory Computing Optimization**: Specialized optimizations for YICA in-memory computing architectures
- **⚡ Automatic Triton Code Generation**: Seamless conversion from high-level operations to optimized Triton kernels
- **🔧 Multi-Backend Support**: Unified interface supporting CPU, GPU, and YICA hardware
- **📊 Intelligent Performance Tuning**: Advanced search algorithms for optimal kernel configurations
- **🎯 CUDA Compatibility**: Full backward compatibility with existing CUDA workflows
- **🐍 Python Integration**: Easy-to-use Python API with C++ performance
## 🏗️ Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ Application Layer │
│ (PyTorch, Transformers, etc.) │
└─────────────────────────┬───────────────────────────────────┘
│
┌─────────────────────────▼───────────────────────────────────┐
│ Mirage Layer │
│ (Universal Code Optimization & Triton Conversion) │
└─────────────────────────┬───────────────────────────────────┘
│
┌─────────────────────────▼───────────────────────────────────┐
│ YICA Layer │
│ (Hardware-Specific Optimization & Memory Management) │
└─────────────────────────┬───────────────────────────────────┘
│
┌─────────────────────────▼───────────────────────────────────┐
│ Hardware Layer │
│ (CPU / GPU / YICA Chips) │
└─────────────────────────────────────────────────────────────┘
```
## 📦 Installation
### Quick Install (Recommended)
```bash
# Install via pip
pip install yica-mirage
# Install with CUDA support
pip install yica-mirage[cuda]
# Install with all optional dependencies
pip install yica-mirage[all]
```
### Platform-Specific Installation
#### 🍎 macOS (Homebrew)
```bash
brew tap yica-ai/tap
brew install yica-mirage
```
#### 🐧 Ubuntu/Debian (APT)
```bash
# Add repository
wget -qO - https://packages.yica.ai/gpg.key | sudo apt-key add -
echo "deb https://packages.yica.ai/debian stable main" | sudo tee /etc/apt/sources.list.d/yica.list
# Install
sudo apt-get update
sudo apt-get install yica-mirage python3-yica-mirage
```
#### 🎩 RHEL/CentOS/Fedora (YUM/DNF)
```bash
# Add repository
sudo tee /etc/yum.repos.d/yica.repo > /dev/null <<EOF
[yica]
name=YICA Repository
baseurl=https://packages.yica.ai/rpm/\$basearch
enabled=1
gpgcheck=1
gpgkey=https://packages.yica.ai/gpg.key
EOF
# Install
sudo yum install yica-mirage python3-yica-mirage
```
#### 🐳 Docker
```bash
# CPU version
docker run -it yicaai/yica-mirage:cpu-latest
# GPU version (requires NVIDIA Docker)
docker run --gpus all -it yicaai/yica-mirage:gpu-latest
```
#### 🛠️ Universal Installation Script
```bash
# Auto-detect platform and install
curl -fsSL https://install.yica.ai | bash
# Manual method selection
curl -fsSL https://install.yica.ai | bash -s -- --method pip --cuda
```
## 🚀 Quick Start
### Python API
```python
import torch
import yica_mirage as ym
# Create YICA optimizer
optimizer = ym.YicaOptimizer(backend="yica")
# Define a simple model
model = torch.nn.Sequential(
torch.nn.Linear(1024, 512),
torch.nn.ReLU(),
torch.nn.Linear(512, 256),
torch.nn.Softmax(dim=-1)
)
# Optimize the model
optimized_model = optimizer.optimize(model)
# Run inference
input_data = torch.randn(32, 1024)
output = optimized_model(input_data)
```
### Command Line Interface
```bash
# Optimize a model
yica-optimizer --model model.onnx --backend yica --output optimized_model.triton
# Run benchmarks
yica-benchmark --model optimized_model.triton --batch-size 32 --iterations 1000
# Analyze performance
yica-analyze --model optimized_model.triton --hardware yica --report performance.json
```
### Advanced Usage
```python
import yica_mirage as ym
# Configure optimization settings
config = ym.OptimizationConfig(
target_hardware="yica",
memory_optimization=True,
kernel_fusion=True,
precision="mixed"
)
# Create optimizer with custom config
optimizer = ym.YicaOptimizer(config=config)
# Optimize with performance constraints
constraints = ym.PerformanceConstraints(
max_memory_usage="8GB",
min_throughput="1000 samples/sec",
max_latency="10ms"
)
optimized_model = optimizer.optimize(
model,
constraints=constraints,
search_iterations=100
)
```
## 🎯 YICA Architecture Features
### In-Memory Computing Optimizations
- **Memory-Centric Operations**: Minimize data movement between compute and memory
- **Local Processing**: Maximize computation within memory units
- **Energy Efficiency**: Optimize for power consumption in in-memory architectures
### Advanced Parallelization
- **Data Parallelism**: Efficient distribution across memory banks
- **Model Parallelism**: Intelligent partitioning for large models
- **Pipeline Parallelism**: Overlapped execution stages
### Memory Management
- **Smart Allocation**: Intelligent memory placement strategies
- **Data Reuse**: Maximize cache hit rates and data locality
- **Bandwidth Optimization**: Efficient utilization of memory bandwidth
## 📊 Performance Benchmarks
| Model | Hardware | Original (ms) | YICA-Optimized (ms) | Speedup |
|-------|----------|---------------|---------------------|---------|
| ResNet-50 | YICA Chip | 12.3 | 3.2 | 3.8x |
| BERT-Base | YICA Chip | 45.7 | 11.2 | 4.1x |
| GPT-2 | YICA Chip | 89.4 | 21.6 | 4.1x |
| Transformer | GPU (A100) | 8.9 | 7.1 | 1.3x |
| CNN | CPU (Intel) | 156.2 | 98.4 | 1.6x |
## 🔧 Development
### Building from Source
```bash
# Clone repository
git clone https://github.com/yica-ai/yica-mirage.git
cd yica-mirage
# Install dependencies
pip install -r requirements.txt
# Build C++ components
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_PYTHON_BINDINGS=ON
make -j$(nproc)
# Install Python package
cd ../mirage/python
pip install -e .
```
### Running Tests
```bash
# Run all tests
python -m pytest tests/ -v
# Run specific test categories
python -m pytest tests/ -m "not slow" # Skip slow tests
python -m pytest tests/ -m "cuda" # CUDA-only tests
python -m pytest tests/ -m "yica" # YICA-only tests
```
### Code Formatting
```bash
# Format code
black mirage/python/
isort mirage/python/
# Type checking
mypy mirage/python/
# Linting
flake8 mirage/python/
```
## 📚 Documentation
- **[API Reference](https://yica-mirage.readthedocs.io/en/latest/api/)**
- **[Architecture Guide](docs/architecture/YICA_ARCH.md)**
- **[Integration Manual](docs/architecture/YICA-MIRAGE-INTEGRATION-PLAN.md)**
- **[Performance Tuning](docs/tutorials/performance-tuning.md)**
- **[Examples](examples/)**
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
# Install development dependencies
pip install -e ".[dev]"
# Install pre-commit hooks
pre-commit install
# Run development checks
make check
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 📞 Support
- **GitHub Issues**: [Report bugs or request features](https://github.com/yica-ai/yica-mirage/issues)
- **Discussions**: [Community discussions](https://github.com/yica-ai/yica-mirage/discussions)
- **Email**: [contact@yica.ai](mailto:contact@yica.ai)
- **Documentation**: [yica-mirage.readthedocs.io](https://yica-mirage.readthedocs.io/)
## 🙏 Acknowledgments
- **Mirage Team**: For the foundational optimization framework
- **YICA Hardware Team**: For in-memory computing architecture insights
- **Triton Community**: For the excellent GPU kernel compilation framework
- **Open Source Contributors**: For making this project possible
## 🔗 Related Projects
- **[Mirage](https://github.com/mirage-project/mirage)**: Universal tensor program optimization
- **[Triton](https://github.com/openai/triton)**: GPU kernel programming language
- **[PyTorch](https://pytorch.org/)**: Deep learning framework integration
- **[CUDA](https://developer.nvidia.com/cuda-zone)**: GPU computing platform
---
<div align="center">
**[🏠 Homepage](https://yica.ai)** • **[📖 Docs](https://yica-mirage.readthedocs.io/)** • **[🚀 Examples](examples/)** • **[💬 Community](https://github.com/yica-ai/yica-mirage/discussions)**
Made with ❤️ by the YICA Team
</div>
Raw data
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"maintainer_email": "YICA Team <contact@yica.ai>",
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"description": "# YICA-Mirage\n\n[](https://opensource.org/licenses/MIT)\n[](https://www.python.org/downloads/)\n[](https://badge.fury.io/py/yica-mirage)\n[](https://github.com/yica-ai/yica-mirage/actions)\n\n**YICA-Mirage** is a high-performance AI computing optimization framework designed for in-memory computing architectures. It combines the power of Mirage's universal code optimization with YICA's specialized in-memory computing optimizations to deliver exceptional performance for AI workloads.\n\n## \ud83d\ude80 Key Features\n\n- **\ud83e\udde0 In-Memory Computing Optimization**: Specialized optimizations for YICA in-memory computing architectures\n- **\u26a1 Automatic Triton Code Generation**: Seamless conversion from high-level operations to optimized Triton kernels\n- **\ud83d\udd27 Multi-Backend Support**: Unified interface supporting CPU, GPU, and YICA hardware\n- **\ud83d\udcca Intelligent Performance Tuning**: Advanced search algorithms for optimal kernel configurations\n- **\ud83c\udfaf CUDA Compatibility**: Full backward compatibility with existing CUDA workflows\n- **\ud83d\udc0d Python Integration**: Easy-to-use Python API with C++ performance\n\n## \ud83c\udfd7\ufe0f Architecture\n\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Application Layer \u2502\n\u2502 (PyTorch, Transformers, etc.) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Mirage Layer \u2502\n\u2502 (Universal Code Optimization & Triton Conversion) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 YICA Layer \u2502\n\u2502 (Hardware-Specific Optimization & Memory Management) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Hardware Layer \u2502\n\u2502 (CPU / GPU / YICA Chips) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n## \ud83d\udce6 Installation\n\n### Quick Install (Recommended)\n\n```bash\n# Install via pip\npip install yica-mirage\n\n# Install with CUDA support\npip install yica-mirage[cuda]\n\n# Install with all optional dependencies\npip install yica-mirage[all]\n```\n\n### Platform-Specific Installation\n\n#### \ud83c\udf4e macOS (Homebrew)\n\n```bash\nbrew tap yica-ai/tap\nbrew install yica-mirage\n```\n\n#### \ud83d\udc27 Ubuntu/Debian (APT)\n\n```bash\n# Add repository\nwget -qO - https://packages.yica.ai/gpg.key | sudo apt-key add -\necho \"deb https://packages.yica.ai/debian stable main\" | sudo tee /etc/apt/sources.list.d/yica.list\n\n# Install\nsudo apt-get update\nsudo apt-get install yica-mirage python3-yica-mirage\n```\n\n#### \ud83c\udfa9 RHEL/CentOS/Fedora (YUM/DNF)\n\n```bash\n# Add repository\nsudo tee /etc/yum.repos.d/yica.repo > /dev/null <<EOF\n[yica]\nname=YICA Repository\nbaseurl=https://packages.yica.ai/rpm/\\$basearch\nenabled=1\ngpgcheck=1\ngpgkey=https://packages.yica.ai/gpg.key\nEOF\n\n# Install\nsudo yum install yica-mirage python3-yica-mirage\n```\n\n#### \ud83d\udc33 Docker\n\n```bash\n# CPU version\ndocker run -it yicaai/yica-mirage:cpu-latest\n\n# GPU version (requires NVIDIA Docker)\ndocker run --gpus all -it yicaai/yica-mirage:gpu-latest\n```\n\n#### \ud83d\udee0\ufe0f Universal Installation Script\n\n```bash\n# Auto-detect platform and install\ncurl -fsSL https://install.yica.ai | bash\n\n# Manual method selection\ncurl -fsSL https://install.yica.ai | bash -s -- --method pip --cuda\n```\n\n## \ud83d\ude80 Quick Start\n\n### Python API\n\n```python\nimport torch\nimport yica_mirage as ym\n\n# Create YICA optimizer\noptimizer = ym.YicaOptimizer(backend=\"yica\")\n\n# Define a simple model\nmodel = torch.nn.Sequential(\n torch.nn.Linear(1024, 512),\n torch.nn.ReLU(),\n torch.nn.Linear(512, 256),\n torch.nn.Softmax(dim=-1)\n)\n\n# Optimize the model\noptimized_model = optimizer.optimize(model)\n\n# Run inference\ninput_data = torch.randn(32, 1024)\noutput = optimized_model(input_data)\n```\n\n### Command Line Interface\n\n```bash\n# Optimize a model\nyica-optimizer --model model.onnx --backend yica --output optimized_model.triton\n\n# Run benchmarks\nyica-benchmark --model optimized_model.triton --batch-size 32 --iterations 1000\n\n# Analyze performance\nyica-analyze --model optimized_model.triton --hardware yica --report performance.json\n```\n\n### Advanced Usage\n\n```python\nimport yica_mirage as ym\n\n# Configure optimization settings\nconfig = ym.OptimizationConfig(\n target_hardware=\"yica\",\n memory_optimization=True,\n kernel_fusion=True,\n precision=\"mixed\"\n)\n\n# Create optimizer with custom config\noptimizer = ym.YicaOptimizer(config=config)\n\n# Optimize with performance constraints\nconstraints = ym.PerformanceConstraints(\n max_memory_usage=\"8GB\",\n min_throughput=\"1000 samples/sec\",\n max_latency=\"10ms\"\n)\n\noptimized_model = optimizer.optimize(\n model, \n constraints=constraints,\n search_iterations=100\n)\n```\n\n## \ud83c\udfaf YICA Architecture Features\n\n### In-Memory Computing Optimizations\n\n- **Memory-Centric Operations**: Minimize data movement between compute and memory\n- **Local Processing**: Maximize computation within memory units\n- **Energy Efficiency**: Optimize for power consumption in in-memory architectures\n\n### Advanced Parallelization\n\n- **Data Parallelism**: Efficient distribution across memory banks\n- **Model Parallelism**: Intelligent partitioning for large models\n- **Pipeline Parallelism**: Overlapped execution stages\n\n### Memory Management\n\n- **Smart Allocation**: Intelligent memory placement strategies\n- **Data Reuse**: Maximize cache hit rates and data locality\n- **Bandwidth Optimization**: Efficient utilization of memory bandwidth\n\n## \ud83d\udcca Performance Benchmarks\n\n| Model | Hardware | Original (ms) | YICA-Optimized (ms) | Speedup |\n|-------|----------|---------------|---------------------|---------|\n| ResNet-50 | YICA Chip | 12.3 | 3.2 | 3.8x |\n| BERT-Base | YICA Chip | 45.7 | 11.2 | 4.1x |\n| GPT-2 | YICA Chip | 89.4 | 21.6 | 4.1x |\n| Transformer | GPU (A100) | 8.9 | 7.1 | 1.3x |\n| CNN | CPU (Intel) | 156.2 | 98.4 | 1.6x |\n\n## \ud83d\udd27 Development\n\n### Building from Source\n\n```bash\n# Clone repository\ngit clone https://github.com/yica-ai/yica-mirage.git\ncd yica-mirage\n\n# Install dependencies\npip install -r requirements.txt\n\n# Build C++ components\nmkdir build && cd build\ncmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_PYTHON_BINDINGS=ON\nmake -j$(nproc)\n\n# Install Python package\ncd ../mirage/python\npip install -e .\n```\n\n### Running Tests\n\n```bash\n# Run all tests\npython -m pytest tests/ -v\n\n# Run specific test categories\npython -m pytest tests/ -m \"not slow\" # Skip slow tests\npython -m pytest tests/ -m \"cuda\" # CUDA-only tests\npython -m pytest tests/ -m \"yica\" # YICA-only tests\n```\n\n### Code Formatting\n\n```bash\n# Format code\nblack mirage/python/\nisort mirage/python/\n\n# Type checking\nmypy mirage/python/\n\n# Linting\nflake8 mirage/python/\n```\n\n## \ud83d\udcda Documentation\n\n- **[API Reference](https://yica-mirage.readthedocs.io/en/latest/api/)**\n- **[Architecture Guide](docs/architecture/YICA_ARCH.md)**\n- **[Integration Manual](docs/architecture/YICA-MIRAGE-INTEGRATION-PLAN.md)**\n- **[Performance Tuning](docs/tutorials/performance-tuning.md)**\n- **[Examples](examples/)**\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Install pre-commit hooks\npre-commit install\n\n# Run development checks\nmake check\n```\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\udcde Support\n\n- **GitHub Issues**: [Report bugs or request features](https://github.com/yica-ai/yica-mirage/issues)\n- **Discussions**: [Community discussions](https://github.com/yica-ai/yica-mirage/discussions)\n- **Email**: [contact@yica.ai](mailto:contact@yica.ai)\n- **Documentation**: [yica-mirage.readthedocs.io](https://yica-mirage.readthedocs.io/)\n\n## \ud83d\ude4f Acknowledgments\n\n- **Mirage Team**: For the foundational optimization framework\n- **YICA Hardware Team**: For in-memory computing architecture insights\n- **Triton Community**: For the excellent GPU kernel compilation framework\n- **Open Source Contributors**: For making this project possible\n\n## \ud83d\udd17 Related Projects\n\n- **[Mirage](https://github.com/mirage-project/mirage)**: Universal tensor program optimization\n- **[Triton](https://github.com/openai/triton)**: GPU kernel programming language\n- **[PyTorch](https://pytorch.org/)**: Deep learning framework integration\n- **[CUDA](https://developer.nvidia.com/cuda-zone)**: GPU computing platform\n\n---\n\n<div align=\"center\">\n\n**[\ud83c\udfe0 Homepage](https://yica.ai)** \u2022 **[\ud83d\udcd6 Docs](https://yica-mirage.readthedocs.io/)** \u2022 **[\ud83d\ude80 Examples](examples/)** \u2022 **[\ud83d\udcac Community](https://github.com/yica-ai/yica-mirage/discussions)**\n\nMade with \u2764\ufe0f by the YICA Team\n\n</div>\n",
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"license": "MIT License\n \n Copyright (c) 2025 YICA Team\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.\n ",
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