SparkSnail · SparkSnail · Feb 25, 2021 · Feb 24, 2021 · Feb 24, 2021 · Feb 25, 2021
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -31,7 +31,7 @@ If you cannot find an existing issue that describes your bug or feature, create
 # Writing good bug reports or feature requests
 File a single issue per problem and feature request. Do not enumerate multiple bugs or feature requests in the same issue.
 
-Provide as many information as you think might relevant to the context (thinking the issue is assigning to you, what kinds of info you will need to debug it!!!). To give you a general idea about what kinds of info are useful for developers to dig out the issue, we had provided issue template for you.
+Provide as much information as you think might relevant to the context (thinking the issue is assigning to you, what kinds of info you will need to debug it!!!). To give you a general idea about what kinds of info are useful for developers to dig out the issue, we had provided issue template for you.
 
 Once you had submitted an issue, be sure to follow it for questions and discussions. 
 
@@ -58,11 +58,11 @@ contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additio
 After getting familiar with contribution agreements, you are ready to create your first PR =), follow the NNI developer tutorials to get start:
 
 * We recommend new contributors to start with simple issues: ['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22).
-* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
-* [How to debug](docs/en_US/Tutorial/HowToDebug.md)
-* If you have any questions on usage, review [FAQ](https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/FAQ.md) first, if there are no relevant issues and answers to your question, try contact NNI dev team and users in [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) or [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
-* [Customize your own Tuner](docs/en_US/Tuner/CustomizeTuner.md)
-* [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.md)
-* [Implement a new NAS trainer on NNI](docs/en_US/NAS/Advanced.md)
-* [Customize your own Advisor](docs/en_US/Tuner/CustomizeAdvisor.md)
+* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.rst)
+* [How to debug](docs/en_US/Tutorial/HowToDebug.rst)
+* If you have any questions on usage, review [FAQ](https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/FAQ.rst) first, if there are no relevant issues and answers to your question, try contact NNI dev team and users in [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) or [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
+* [Customize your own Tuner](docs/en_US/Tuner/CustomizeTuner.rst)
+* [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.rst)
+* [Implement a new NAS trainer on NNI](docs/en_US/NAS/Advanced.rst)
+* [Customize your own Advisor](docs/en_US/Tuner/CustomizeAdvisor.rst)
 
diff --git a/dependencies/required.txt b/dependencies/required.txt
@@ -13,6 +13,7 @@ scikit-learn >= 0.23.2
 websockets
 filelock
 prettytable
+ipython
 dataclasses ; python_version < "3.7"
 numpy < 1.19.4 ; sys_platform == "win32"
 numpy < 1.20 ; sys_platform != "win32" and python_version < "3.7"

diff --git a/docs/en_US/CommunitySharings/community_sharings.rst b/docs/en_US/CommunitySharings/community_sharings.rst
@@ -28,12 +28,13 @@ External Repositories
 
 Relevant Articles
 =================
+  * `Cost-effective Hyper-parameter Tuning using AdaptDL with NNI - Feb 23, 2021 <https://medium.com/casl-project/cost-effective-hyper-parameter-tuning-using-adaptdl-with-nni-e55642888761>`__
+  * `(in Chinese) A summary of NNI new capabilities in NNI 2.0 - Jan 21, 2021 <https://www.msra.cn/zh-cn/news/features/nni-2>`__
+  * `(in Chinese) A summary of NNI new capabilities in 2019 - Dec 26, 2019 <https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA>`__
+  * `Find thy hyper-parameters for scikit-learn pipelines using Microsoft NNI - Nov 6, 2019 <https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1>`__ 
+  * `(in Chinese) AutoML tools (Advisor, NNI and Google Vizier) comparison - Aug 05, 2019 <http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90>`__  
   * `Hyper Parameter Optimization Comparison <./HpoComparison.rst>`__ 
   * `Neural Architecture Search Comparison <./NasComparison.rst>`__ 
   * `Parallelizing a Sequential Algorithm TPE <./ParallelizingTpeSearch.rst>`__ 
   * `Automatically tuning SVD with NNI <./RecommendersSvd.rst>`__ 
   * `Automatically tuning SPTAG with NNI <./SptagAutoTune.rst>`__ 
-  * `Find thy hyper-parameters for scikit-learn pipelines using Microsoft NNI <https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1>`__ 
-  * `(in Chinese) AutoML tools (Advisor, NNI and Google Vizier) comparison <http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90>`__  
-  * `(in Chinese) A summary of NNI new capabilities in 2019 <https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA>`__
-
diff --git a/docs/en_US/Compression/CompressionReference.rst b/docs/en_US/Compression/CompressionReference.rst
@@ -1,16 +1,121 @@
-Python API Reference of Compression Utilities
-=============================================
+Model Compression API Reference
+===============================
 
 .. contents::
 
-Sensitivity Utilities
+Compressors
+-----------
+
+Compressor
+^^^^^^^^^^
+
+..  autoclass:: nni.compression.pytorch.compressor.Compressor
+    :members:
+
+..  autoclass:: nni.compression.pytorch.compressor.Pruner
+    :members:
+
+..  autoclass:: nni.compression.pytorch.compressor.Quantizer
+    :members:
+
+
+Module Wrapper
+^^^^^^^^^^^^^^
+
+..  autoclass:: nni.compression.pytorch.compressor.PrunerModuleWrapper
+    :members:
+
+
+..  autoclass:: nni.compression.pytorch.compressor.QuantizerModuleWrapper
+    :members:
+
+Weight Masker
+^^^^^^^^^^^^^
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.weight_masker.WeightMasker
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.structured_pruning.StructuredWeightMasker
+    :members:
+
+
+Pruners
+^^^^^^^
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.sensitivity_pruner.SensitivityPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.OneshotPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.LevelPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.SlimPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.L1FilterPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.L2FilterPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.FPGMPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.TaylorFOWeightFilterPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.ActivationAPoZRankFilterPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot.ActivationMeanRankFilterPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.lottery_ticket.LotteryTicketPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.agp.AGPPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.admm_pruner.ADMMPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.auto_compress_pruner.AutoCompressPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.net_adapt_pruner.NetAdaptPruner
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.pruning.simulated_annealing_pruner.SimulatedAnnealingPruner
+    :members:
+
+
+Quantizers
+^^^^^^^^^^
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.quantizers.NaiveQuantizer
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.quantizers.QAT_Quantizer
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.quantizers.DoReFaQuantizer
+    :members:
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.quantizers.BNNQuantizer
+    :members:
+
+
+
+Compression Utilities
 ---------------------
 
+Sensitivity Utilities
+^^^^^^^^^^^^^^^^^^^^^
+
 ..  autoclass:: nni.compression.pytorch.utils.sensitivity_analysis.SensitivityAnalysis
     :members:
 
 Topology Utilities
-------------------
+^^^^^^^^^^^^^^^^^^
 
 ..  autoclass:: nni.compression.pytorch.utils.shape_dependency.ChannelDependency
     :members:
@@ -28,6 +133,6 @@ Topology Utilities
     :members:
 
 Model FLOPs/Parameters Counter
-------------------------------
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 ..  autofunction:: nni.compression.pytorch.utils.counter.count_flops_params
diff --git a/docs/en_US/Compression/Overview.rst b/docs/en_US/Compression/Overview.rst
@@ -87,11 +87,6 @@ Quantization algorithms compress the original network by reducing the number of
      - Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. `Reference Paper <https://arxiv.org/abs/1602.02830>`__
 
 
-Automatic Model Compression
----------------------------
-
-Given targeted compression ratio, it is pretty hard to obtain the best compressed ratio in a one shot manner. An automatic model compression algorithm usually need to explore the compression space by compressing different layers with different sparsities. NNI provides such algorithms to free users from specifying sparsity of each layer in a model. Moreover, users could leverage NNI's auto tuning power to automatically compress a model. Detailed document can be found `here <./AutoPruningUsingTuners.rst>`__.
-
 Model Speedup
 -------------
 
@@ -102,10 +97,11 @@ Compression Utilities
 
 Compression utilities include some useful tools for users to understand and analyze the model they want to compress. For example, users could check sensitivity of each layer to pruning. Users could easily calculate the FLOPs and parameter size of a model. Please refer to `here <./CompressionUtils.rst>`__ for a complete list of compression utilities.
 
-Customize Your Own Compression Algorithms
------------------------------------------
+Advanced Usage
+--------------
+
+NNI model compression leaves simple interface for users to customize a new compression algorithm. The design philosophy of the interface is making users focus on the compression logic while hiding framework specific implementation details from users. Users can learn more about our compression framework and customize a new compression algorithm (pruning algorithm or quantization algorithm) based on our framework. Moreover, users could leverage NNI's auto tuning power to automatically compress a model. Please refer to `here <./advanced.rst>`__ for more details.
 
-NNI model compression leaves simple interface for users to customize a new compression algorithm. The design philosophy of the interface is making users focus on the compression logic while hiding framework specific implementation details from users. The detailed tutorial for customizing a new compression algorithm (pruning algorithm or quantization algorithm) can be found `here <./Framework.rst>`__.
 
 Reference and Feedback
 ----------------------