openvino/tools/pot/docs/Introduction.md

Optimizing models post-training

@sphinxdirective

.. toctree:: :maxdepth: 1 :hidden:

Quantizing Model <pot_default_quantization_usage> Quantizing Model with Accuracy Control <pot_accuracyaware_usage> Quantization Best Practices <pot_docs_BestPractices> API Reference <pot_compression_api_README> Command-line Interface <pot_compression_cli_README> Examples <pot_examples_description> pot_docs_FrequentlyAskedQuestions

@endsphinxdirective

Post-training model optimization is the process of applying special methods without model retraining or fine-tuning. Therefore, it does not require either a training dataset or a training pipeline in the source DL framework. In OpenVINO, post-training methods, such as post-training 8-bit quantization, require:

• A floating-point precision model (FP32 or FP16), converted to the OpenVINO IR format (Intermediate Representation) and run on CPU with OpenVINO.
• A representative calibration dataset representing a use case scenario, for example, 300 samples.
• In case of accuracy constraints, a validation dataset and accuracy metrics should be available.

OpenVINO provides a Post-training Optimization Tool (POT) that supports the uniform integer quantization method. It can substantially increase inference performance and reduce the size of a model.

The figure below shows the optimization workflow with POT:

Quantizing models with POT

Depending on your needs and requirements, POT provides two main quantization methods that can be used:

• [Default Quantization](@ref pot_default_quantization_usage) -- a recommended method that provides fast and accurate results in most cases. It requires only an unannotated dataset for quantization. For more details, see the [Default Quantization algorithm](@ref pot_compression_algorithms_quantization_default_README) documentation.

• [Accuracy-aware Quantization](@ref pot_accuracyaware_usage) -- an advanced method that allows keeping accuracy at a predefined range, at the cost of performance improvement, when Default Quantization cannot guarantee it. This method requires an annotated representative dataset and may require more time for quantization. For more details, see the [Accuracy-aware Quantization algorithm](@ref accuracy_aware_README) documentation.

Different hardware platforms support different integer precisions and quantization parameters. For example, 8-bit is used by CPU, GPU, VPU, and 16-bit by GNA. POT abstracts this complexity by introducing a concept of the "target device" used to set quantization settings, specific to the device.

Note

: There is a special target_device: "ANY" which leads to portable quantized models compatible with CPU, GPU, and VPU devices. GNA-quantized models are compatible only with CPU.

For benchmarking results collected for the models optimized with the POT tool, refer to the [INT8 vs FP32 Comparison on Select Networks and Platforms](@ref openvino_docs_performance_int8_vs_fp32).

Additional Resources

• Performance Benchmarks
• INT8 Quantization by Using Web-Based Interface of the DL Workbench