diff --git a/docs/zh_cn/tutorials/image_classification_mkldnn_quant_tutorial.md b/docs/zh_cn/tutorials/image_classification_mkldnn_quant_tutorial.md index 8cd49cc908ce5902a7e5a5cb89a87315058b62ec..4018aa1db9bb5ea7d6136bf58a272f42b8b2331b 100644 --- a/docs/zh_cn/tutorials/image_classification_mkldnn_quant_tutorial.md +++ b/docs/zh_cn/tutorials/image_classification_mkldnn_quant_tutorial.md @@ -1,4 +1,4 @@ -# CPU上部署量化模型教程 +# Intel CPU上部署量化模型教程 在Intel Casecade Lake机器上（如：Intel(R) Xeon(R) Gold 6271），经过量化和DNNL加速，INT8模型在单线程上性能为FP32模型的3~3.7倍；在Intel SkyLake机器上（如：Intel(R) Xeon(R) Gold 6148），单线程性能为FP32模型的1.5倍，而精度仅有极小下降。图像分类量化的样例教程请参考[图像分类INT8模型在CPU优化部署和预测](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/mkldnn_quant/README.md)。自然语言处理模型的量化请参考[ERNIE INT8 模型精度与性能复现](https://github.com/PaddlePaddle/benchmark/tree/master/Inference/c%2B%2B/ernie/mkldnn) diff --git a/docs/zh_cn/tutorials/index.rst b/docs/zh_cn/tutorials/index.rst index 72daabc81cd82680d5d15a2f39ad6b95f63d9722..8e8036add2c96d031def2ea6a4a3cf3e07a97b8f 100644 --- a/docs/zh_cn/tutorials/index.rst +++ b/docs/zh_cn/tutorials/index.rst @@ -12,5 +12,5 @@ paddledetection_slim_pruing_tutorial.md paddledetection_slim_prune_dist_tutorial.md paddledetection_slim_quantization_tutorial.md - image_classification_mkldnn_quant_aware_tutorial.md + image_classification_mkldnn_quant_tutorial.md paddledetection_slim_sensitivy_tutorial.md