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Feature/sg 000 add note that qat only supports gpu (#1830)
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* updated doc files

* updated doc files

* updated notebook
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shaydeci committed Feb 12, 2024
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2 changes: 2 additions & 0 deletions documentation/source/ptq_qat.md
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Expand Up @@ -22,6 +22,8 @@ In this tutorial, we’ll compare post-training quantization (PTQ) to quantizati

For mode detailed information and theoretical background, refer to this [NVIDIA whitepaper](https://arxiv.org/pdf/2004.09602.pdf) and [this practical guide from PyTorch](https://pytorch.org/blog/quantization-in-practice/).

**Note: quantization is currently supported exclusively for GPU and TensorRT environments.**


## Quantization: FP32 vs FP16 vs INT8
Quantization is a model size reduction technique that converts model weights from high-precision floating-point representation (32-bit float) to low-precision floating-point (FP) representation, such as 16-bit or 8-bit.
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1 change: 1 addition & 0 deletions documentation/source/qat_ptq_yolo_nas.md
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Expand Up @@ -15,6 +15,7 @@ Pre-requisites:
- [Training with configuration files](https://github.com/Deci-AI/super-gradients/blob/master/documentation/source/configuration_files.md)
- [PTQ and QAT](https://github.com/Deci-AI/super-gradients/blob/master/documentation/source/ptq_qat.md)

**Note: quantization is currently supported exclusively for GPU and TensorRT environments.**

Now, let's get to it.

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6 changes: 4 additions & 2 deletions notebooks/PTQ_and_QAT_for_classification.ipynb
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Expand Up @@ -32,7 +32,9 @@
"\n",
"Full tutorial can be found [here](https://github.com/Deci-AI/super-gradients/blob/master/documentation/source/ptq_qat.md)\n",
"\n",
"SuperGradient's Recipes for PTQ/QAT can be found [here](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes/quantization_params)\n"
"SuperGradient's Recipes for PTQ/QAT can be found [here](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes/quantization_params)\n",
"\n",
"**Note: quantization is currently supported exclusively for GPU and TensorRT environments.**"
]
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{
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},
"nbformat": 4,
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}

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