# Hidet: An Open-Source Deep Learning Compiler
[**Documentation**](http://docs.hidet.org/) |
[**Research Paper**](https://dl.acm.org/doi/10.1145/3575693.3575702) |
[**Releases**](https://github.com/hidet-org/hidet/releases) |
[**Contributing**](https://docs.hidet.org/stable/developer-guides/contributing.html)
Hidet is an open-source deep learning compiler, written in Python.
It supports end-to-end compilation of DNN models from PyTorch and ONNX to efficient cuda kernels.
A series of graph-level and operator-level optimizations are applied to optimize the performance.
Currently, hidet focuses on optimizing the inference workloads on NVIDIA GPUs, and requires
- Linux OS
- CUDA Toolkit 11.6+
- Python 3.8+
## Getting Started
### Installation
```bash
pip install hidet
```
You can also try the [nightly build version](https://docs.hidet.org/stable/getting-started/install.html) or [build from source](https://docs.hidet.org/stable/getting-started/build-from-source.html#).
### Usage
Optimize a PyTorch model through hidet (require PyTorch 2.0):
```python
import torch
# Define pytorch model
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).cuda().eval()
x = torch.rand(1, 3, 224, 224).cuda()
# Compile the model through Hidet
# Optional: set optimization options (see our documentation for more details)
# import hidet
# hidet.torch.dynamo_config.search_space(2) # tune each tunable operator
# hidet.torch.dynamo_config.use_fp16() # use float16 for acceleration
model_opt = torch.compile(model, backend='hidet')
# Run the optimized model
y = model_opt(x)
```
See the following tutorials to learn other usages:
- [Quick Start](http://docs.hidet.org/stable/gallery/getting-started/quick-start.html)
- [Optimize PyTorch models](http://docs.hidet.org/stable/gallery/tutorials/optimize-pytorch-model.html)
- [Optimize ONNX models](http://docs.hidet.org/stable/gallery/tutorials/run-onnx-model.html)
## Publication
Hidet originates from the following research work:
> **Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs**
> Yaoyao Ding, Cody Hao Yu, Bojian Zheng, Yizhi Liu, Yida Wang, and Gennady Pekhimenko.
> ASPLOS '23
If you used **Hidet** in your research, welcome to cite our
[paper](https://dl.acm.org/doi/10.1145/3575693.3575702).
## Development
Hidet is currently under active development by a team at [CentML Inc](https://centml.ai/).
## Contributing
We welcome contributions from the community. Please see
[contribution guide](https://docs.hidet.org/stable/developer-guides/contributing.html)
for more details.
## License
Hidet is released under the [Apache 2.0 license](LICENSE).
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"description": "# Hidet: An Open-Source Deep Learning Compiler\n[**Documentation**](http://docs.hidet.org/) |\n[**Research Paper**](https://dl.acm.org/doi/10.1145/3575693.3575702) |\n[**Releases**](https://github.com/hidet-org/hidet/releases) |\n[**Contributing**](https://docs.hidet.org/stable/developer-guides/contributing.html)\n\n\n\n\n\nHidet is an open-source deep learning compiler, written in Python. \nIt supports end-to-end compilation of DNN models from PyTorch and ONNX to efficient cuda kernels.\nA series of graph-level and operator-level optimizations are applied to optimize the performance.\n\nCurrently, hidet focuses on optimizing the inference workloads on NVIDIA GPUs, and requires\n- Linux OS\n- CUDA Toolkit 11.6+\n- Python 3.8+\n\n## Getting Started\n\n### Installation\n```bash\npip install hidet\n```\nYou can also try the [nightly build version](https://docs.hidet.org/stable/getting-started/install.html) or [build from source](https://docs.hidet.org/stable/getting-started/build-from-source.html#).\n\n### Usage\n\nOptimize a PyTorch model through hidet (require PyTorch 2.0):\n```python\nimport torch\n\n# Define pytorch model\nmodel = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).cuda().eval()\nx = torch.rand(1, 3, 224, 224).cuda()\n\n# Compile the model through Hidet\n# Optional: set optimization options (see our documentation for more details)\n# import hidet \n# hidet.torch.dynamo_config.search_space(2) # tune each tunable operator\n# hidet.torch.dynamo_config.use_fp16() # use float16 for acceleration\nmodel_opt = torch.compile(model, backend='hidet') \n\n# Run the optimized model\ny = model_opt(x)\n```\nSee the following tutorials to learn other usages:\n- [Quick Start](http://docs.hidet.org/stable/gallery/getting-started/quick-start.html)\n- [Optimize PyTorch models](http://docs.hidet.org/stable/gallery/tutorials/optimize-pytorch-model.html)\n- [Optimize ONNX models](http://docs.hidet.org/stable/gallery/tutorials/run-onnx-model.html)\n\n## Publication\nHidet originates from the following research work:\n\n> **Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs** \n> Yaoyao Ding, Cody Hao Yu, Bojian Zheng, Yizhi Liu, Yida Wang, and Gennady Pekhimenko. \n> ASPLOS '23\n\nIf you used **Hidet** in your research, welcome to cite our\n[paper](https://dl.acm.org/doi/10.1145/3575693.3575702).\n\n## Development \nHidet is currently under active development by a team at [CentML Inc](https://centml.ai/). \n\n## Contributing\nWe welcome contributions from the community. Please see \n[contribution guide](https://docs.hidet.org/stable/developer-guides/contributing.html)\nfor more details.\n\n## License\nHidet is released under the [Apache 2.0 license](LICENSE).\n",
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