bartekkuncer commented on a change in pull request #20606: URL: https://github.com/apache/incubator-mxnet/pull/20606#discussion_r723108402
########## File path: cpp-package/example/inference/README.md ########## @@ -27,7 +27,7 @@ This directory contains following examples. In order to run the examples, ensure ## [imagenet_inference.cpp](<https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/imagenet_inference.cpp>) -This example demonstrates image classification workflow with pre-trained models using MXNet C++ API. Now this script also supports inference with quantized CNN models generated by Intel® MKL-DNN (see this [quantization flow](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md)). By using C++ API, the latency of most models will be reduced to some extent compared with current Python implementation. +This example demonstrates image classification workflow with pre-trained models using MXNet C++ API. Now this script also supports inference with quantized CNN models generated by Intel® DNNL (see this [quantization flow](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md)). By using C++ API, the latency of most models will be reduced to some extent compared with current Python implementation. Review comment: Done. ########## File path: docs/python_docs/python/tutorials/index.rst ########## @@ -84,10 +84,10 @@ Performance How to use int8 in your model to boost training speed. .. card:: - :title: MKL-DNN + :title: DNNL :link: performance/backend/mkldnn/index.html - How to get the most from your CPU by using Intel's MKL-DNN. + How to get the most from your CPU by using Intel's DNNL. Review comment: done -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
