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Apache MXNet rename (#11871)

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* MXNet

MXNet renaming into Apache MXNet

* Update docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md

Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>

* MXNet 2

* MXNet 3

* Revert "MXNet 3"

This reverts commit 046c25239d.

Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>
This commit is contained in:
Maciej Smyk
2022-07-05 15:04:03 +02:00
committed by GitHub
parent f635621aab
commit d91a06ac08
15 changed files with 37 additions and 37 deletions
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docs/Extensibility_UG/Intro.md
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@@ -15,7 +15,7 @@
@endsphinxdirective
The Intel® Distribution of OpenVINO™ toolkit supports neural network models trained with various frameworks, including
TensorFlow, PyTorch, ONNX, PaddlePaddle, MXNet, Caffe, and Kaldi. The list of supported operations is different for
TensorFlow, PyTorch, ONNX, PaddlePaddle, Apache MXNet, Caffe, and Kaldi. The list of supported operations is different for
each of the supported frameworks. To see the operations supported by your framework, refer to
[Supported Framework Operations](../MO_DG/prepare_model/Supported_Frameworks_Layers.md).
@@ -52,7 +52,7 @@ Depending on model format used for import, mapping of custom operation is implem
2. If model is represented in TensorFlow, Caffe, Kaldi or MXNet formats, then [Model Optimizer Extensions](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md) should be used. This approach is available for model conversion in Model Optimizer only.
Existing of two approaches simultaneously is explained by two different types of frontends used for model conversion in OpenVINO: new frontends (ONNX, PaddlePaddle) and legacy frontends (TensorFlow, Caffe, Kaldi and MXNet). Model Optimizer can use both front-ends in contrast to the direct import of model with `read_model` method which can use new frontends only. Follow one of the appropriate guides referenced above to implement mappings depending on framework frontend.
Existing of two approaches simultaneously is explained by two different types of frontends used for model conversion in OpenVINO: new frontends (ONNX, PaddlePaddle) and legacy frontends (TensorFlow, Caffe, Kaldi and Apache MXNet). Model Optimizer can use both front-ends in contrast to the direct import of model with `read_model` method which can use new frontends only. Follow one of the appropriate guides referenced above to implement mappings depending on framework frontend.
If you are implementing extensions for ONNX or PaddlePaddle new frontends and plan to use Model Optimizer `--extension` option for model conversion, then the extensions should be