diff --git a/docs/en_US/CommunitySharings/NNI_AutoFeatureEng.rst b/docs/en_US/CommunitySharings/NNI_AutoFeatureEng.rst index c6a669b9e8..8c16f3cb3f 100644 --- a/docs/en_US/CommunitySharings/NNI_AutoFeatureEng.rst +++ b/docs/en_US/CommunitySharings/NNI_AutoFeatureEng.rst @@ -137,5 +137,6 @@ Conclusion: NNI could offer users some inspirations of design and it is a good o Tips: Because the scripts of open source projects are compiled based on gcc7, Mac system may encounter problems of gcc (GNU Compiler Collection). The solution is as follows: -brew install libomp -=================== +.. code-block:: bash + + brew install libomp diff --git a/docs/en_US/FeatureEngineering/GBDTSelector.rst b/docs/en_US/FeatureEngineering/GBDTSelector.rst index 4ae04f6163..daded470b0 100644 --- a/docs/en_US/FeatureEngineering/GBDTSelector.rst +++ b/docs/en_US/FeatureEngineering/GBDTSelector.rst @@ -22,7 +22,7 @@ Then .. code-block:: python - from nni.feature_engineering.gbdt_selector import GBDTSelector + from nni.algorithms.feature_engineering.gbdt_selector import GBDTSelector # load data ... diff --git a/docs/en_US/FeatureEngineering/GradientFeatureSelector.rst b/docs/en_US/FeatureEngineering/GradientFeatureSelector.rst index 46630e5319..6b2aafae72 100644 --- a/docs/en_US/FeatureEngineering/GradientFeatureSelector.rst +++ b/docs/en_US/FeatureEngineering/GradientFeatureSelector.rst @@ -18,7 +18,7 @@ Usage .. code-block:: python - from nni.feature_engineering.gradient_selector import FeatureGradientSelector + from nni.algorithms.feature_engineering.gradient_selector import FeatureGradientSelector # load data ... diff --git a/docs/en_US/NAS/Overview.rst b/docs/en_US/NAS/Overview.rst index 61cf4ae92d..6c56fb171d 100644 --- a/docs/en_US/NAS/Overview.rst +++ b/docs/en_US/NAS/Overview.rst @@ -60,8 +60,8 @@ NNI currently supports the one-shot NAS algorithms listed below and is adding mo - `ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware `__. It removes proxy, directly learns the architectures for large-scale target tasks and target hardware platforms. * - `TextNAS `__ - `TextNAS: A Neural Architecture Search Space tailored for Text Representation `__. It is a neural architecture search algorithm tailored for text representation. - * - `Cream `__ - - `Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search `__. It is a new NAS algorithm distilling prioritized paths in search space, without using evolutionary algorithms. Achieving competitive performance on ImageNet, especially for small models (e.g. <200 M Flops). + * - `Cream `__ + - `Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search `__. It is a new NAS algorithm distilling prioritized paths in search space, without using evolutionary algorithms. Achieving competitive performance on ImageNet, especially for small models (e.g. <200 M FLOPs). One-shot algorithms run **standalone without nnictl**. NNI supports both PyTorch and Tensorflow 2.X. diff --git a/docs/en_US/Tuner/BuiltinTuner.rst b/docs/en_US/Tuner/BuiltinTuner.rst index c13d890c0e..9d5f0fbc4f 100644 --- a/docs/en_US/Tuner/BuiltinTuner.rst +++ b/docs/en_US/Tuner/BuiltinTuner.rst @@ -188,7 +188,7 @@ SMAC Built-in Tuner Name: **SMAC** -**Please note that SMAC doesn't support running on Windows currently. For the specific reason, please refer to this `GitHub issue `__.** +**Please note that SMAC doesn't support running on Windows currently**. For the specific reason, please refer to this `GitHub issue `__. **Installation**