the source code of NNI for DCU

docs/en_US/Compression/v2_pruning.rst

+Pruning V2
+==========
+
+Pruning V2 is a refactoring of the old version and provides more powerful functions.
+Compared with the old version, the iterative pruning process is detached from the pruner and the pruner is only responsible for pruning and generating the masks once.
+What's more, pruning V2 unifies the pruning process and provides a more free combination of pruning components.
+Task generator only cares about the pruning effect that should be achieved in each round, and uses a config list to express how to pruning in the next step.
+Pruner will reset with the model and config list given by task generator then generate the masks in current step.
+
+For a clearer structure vision, please refer to the figure below.
+
+.. image:: ../../img/pruning_process.png
+   :target: ../../img/pruning_process.png
+   :alt:
+
+In V2, a pruning process is usually driven by a pruning scheduler, it contains a specific pruner and a task generator.
+But users can also use pruner directly like in the pruning V1.
+
+For details, please refer to the following tutorials:
+
+..  toctree::
+    :maxdepth: 2
+
+    Pruning Algorithms <v2_pruning_algo>
+    Pruning Scheduler <v2_scheduler>
+    Pruning Config List <v2_pruning_config_list>

docs/en_US/Makefile

+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/en_US/NAS/Hypermodules.rst

+Hypermodules
+============
+
+Hypermodule is a (PyTorch) module which contains many architecture/hyperparameter candidates for this module. By using hypermodule in user defined model, NNI will help users automatically find the best architecture/hyperparameter of the hypermodules for this model. This follows the design philosophy of Retiarii that users write DNN model as a space.
+
+There has been proposed some hypermodules in NAS community, such as AutoActivation, AutoDropout. Some of them are implemented in the Retiarii framework.
+
+..  autoclass:: nni.retiarii.nn.pytorch.AutoActivation
+    :members:
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