The Intel® Low Precision Optimization Tool (Intel® LPOT) is an open-source Python library that delivers a unified low-precision inference interface across multiple Intel-optimized Deep Learning (DL) frameworks on both CPUs and GPUs. It supports automatic accuracy-driven tuning strategies, along with additional objectives such as optimizing for performance, model size, and memory footprint. It also provides easy extension capability for new backends, tuning strategies, metrics, and objectives.
Note: GPU support is under development.
| Infrastructure | Workflow |
|---|---|
 |
 |
Supported Intel-optimized DL frameworks are:
- TensorFlow*, including 1.15.0 UP2, 1.15.0 UP1, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0
Note: TF 2.5.0 requires setting environment variable TF_ENABLE_MKL_NATIVE_FORMAT=0 for INT8 quantization.
- PyTorch*, including 1.5.0+cpu, 1.6.0+cpu, 1.8.0+cpu
- Apache* MXNet, including 1.6.0, 1.7.0
- ONNX* Runtime, including 1.6.0
Get started with installation, tutorials, examples, and more!
View the Intel® LPOT repo at: https://github.com/intel/lpot.