Update readme doc link (#3482)

docs/en_US/CommunitySharings/ModelCompressionComparison.rst

@@ -13,7 +13,7 @@ The experiments are performed with the following pruners/datasets/models:
 
 
 * 
-  Models: :githublink:`VGG16, ResNet18, ResNet50 <examples/model_compress/models/cifar10>`
+  Models: :githublink:`VGG16, ResNet18, ResNet50 <examples/model_compress/pruning/models/cifar10>`
 
 * 
   Datasets: CIFAR-10
@@ -96,14 +96,14 @@ Implementation Details
   This avoids potential issues of counting them of masked models.
 
 * 
-  The experiment code can be found :githublink:`here <examples/model_compress/auto_pruners_torch.py>`.
+  The experiment code can be found :githublink:`here <examples/model_compress/pruning/auto_pruners_torch.py>`.
 
 Experiment Result Rendering
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
 * 
-  If you follow the practice in the :githublink:`example <examples/model_compress/auto_pruners_torch.py>`\ , for every single pruning experiment, the experiment result will be saved in JSON format as follows:
+  If you follow the practice in the :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>`\ , for every single pruning experiment, the experiment result will be saved in JSON format as follows:
 
   .. code-block:: json
 
@@ -114,8 +114,8 @@ Experiment Result Rendering
        }
 
 * 
-  The experiment results are saved :githublink:`here <examples/model_compress/comparison_of_pruners>`. 
-  You can refer to :githublink:`analyze <examples/model_compress/comparison_of_pruners/analyze.py>` to plot new performance comparison figures.
+  The experiment results are saved :githublink:`here <examples/model_compress/pruning/comparison_of_pruners>`. 
+  You can refer to :githublink:`analyze <examples/model_compress/pruning/comparison_of_pruners/analyze.py>` to plot new performance comparison figures.
 
 Contribution
 ------------

docs/en_US/Compression/Overview.rst

@@ -14,7 +14,9 @@ NNI provides a model compression toolkit to help user compress and speed up thei
 * Provide friendly and easy-to-use compression utilities for users to dive into the compression process and results.
 * Concise interface for users to customize their own compression algorithms.
 
-*Note that the interface and APIs are unified for both PyTorch and TensorFlow, currently only PyTorch version has been supported, TensorFlow version will be supported in future.*
+.. note::
+  Since NNI compression algorithms are not meant to compress model while NNI speedup tool can truly compress model and reduce latency. To obtain a truly compact model, users should conduct `model speedup <./ModelSpeedup.rst>`__. The interface and APIs are unified for both PyTorch and TensorFlow, currently only PyTorch version has been supported, TensorFlow version will be supported in future.
+
 
 Supported Algorithms
 --------------------

docs/en_US/Compression/Tutorial.rst

@@ -17,18 +17,26 @@ The ``dict``\ s in the ``list`` are applied one by one, that is, the configurati
 
 There are different keys in a ``dict``. Some of them are common keys supported by all the compression algorithms:
 
-* **op_types**\ : This is to specify what types of operations to be compressed. 'default' means following the algorithm's default setting.
+* **op_types**\ : This is to specify what types of operations to be compressed. 'default' means following the algorithm's default setting. All suported module types are defined in :githublink:`default_layers.py <nni/compression/pytorch/default_layers.py>` for pytorch.
 * **op_names**\ : This is to specify by name what operations to be compressed. If this field is omitted, operations will not be filtered by it.
 * **exclude**\ : Default is False. If this field is True, it means the operations with specified types and names will be excluded from the compression.
 
 Some other keys are often specific to a certain algorithm, users can refer to `pruning algorithms <./Pruner.rst>`__ and `quantization algorithms <./Quantizer.rst>`__ for the keys allowed by each algorithm.
 
-A simple example of configuration is shown below:
+To prune all ``Conv2d`` layers with the sparsity of 0.6, the configuration can be written as:
 
 .. code-block:: python
 
-   [
-       {
+   [{
+    'sparsity': 0.6,
+    'op_types': ['Conv2d']
+   }]
+
+To control the sparsity of specific layers, the configuration can be written as:
+
+.. code-block:: python
+
+   [{
       'sparsity': 0.8,
       'op_types': ['default']
    }, 
@@ -39,8 +47,7 @@ A simple example of configuration is shown below:
    {
       'exclude': True,
       'op_names': ['op_name3']
-       }
-   ]
+   }]
 
 It means following the algorithm's default setting for compressed operations with sparsity 0.8, but for ``op_name1`` and ``op_name2`` use sparsity 0.6, and do not compress ``op_name3``.
 
@@ -84,12 +91,14 @@ The following example shows a more complete ``config_list``\ , it uses ``op_name
       'quant_types': ['weight'],        
       'quant_bits': 8, 
       'op_names': ['conv1']
-       }, {
+   }, 
+   {
       'quant_types': ['weight'],
       'quant_bits': 4,
       'quant_start_step': 0,
       'op_names': ['conv2']
-       }, {
+   }, 
+   {
       'quant_types': ['weight'],
       'quant_bits': 3,
       'op_names': ['fc1']
@@ -98,8 +107,7 @@ The following example shows a more complete ``config_list``\ , it uses ``op_name
       'quant_types': ['weight'],
       'quant_bits': 2,
       'op_names': ['fc2']
-           }
-   ]
+   }]
 
 In this example, 'op_names' is the name of layer and four layers will be quantized to different quant_bits.
 

examples/model_compress/pruning/.gitignore

+cifar-10-python.tar.gz
+cifar-10-batches-py/
\ No newline at end of file