modify readme for Neural Coder (#1387)

README.md

@@ -66,6 +66,13 @@ dataset = quantizer.dataset('dummy', shape=(1, 224, 224, 3))
 quantizer.calib_dataloader = common.DataLoader(dataset)
 quantizer.fit()
 ```
+### Quantization with [JupyterLab Extension](./neural_coder/extensions/neural_compressor_ext_lab/README.md) (Experimental)
+Search for ```jupyter-lab-neural-compressor``` in the Extension Manager in JupyterLab and install with one click:
+
+<a target="_blank" href="./neural_coder/extensions/screenshots/extmanager.png">
+  <img src="./neural_coder/extensions/screenshots/extmanager.png" alt="Extension" width="35%" height="35%">
+</a>
+
 ### Quantization with [GUI](./docs/bench.md)
 ```shell
 # An ONNX Example
@@ -79,7 +86,7 @@ inc_bench
   <img src="./docs/imgs/INC_GUI.gif" alt="Architecture">
 </a>
 
-### Quantization with [Auto-coding API](./neural_coder/docs/AutoQuant.md) (Experimental)
+### Quantization with [Neural Coder](./neural_coder/docs/Quantization.md) (Experimental)
 
 ```python
 from neural_coder import auto_quant

neural_coder/README.md

@@ -35,11 +35,11 @@ simultaneously on below PyTorch evaluation code, we generate the optimized code
 
 ## Getting Started!
 
-### Auto-Quant Feature
-We provide a feature named Auto-Quant that helps automatically enable quantization features on a PyTorch model script and automatically evaluates for the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a PyTorch model with no manual coding needed. Supported features include Post-Training Static Quantization, Post-Training Dynamic Quantization, and Mixed Precision. For more details please refer to this [guide](docs/AutoQuant.md).
+### Neural Coder for Quantization
+We provide a feature that helps automatically enable quantization on Deep Learning models and automatically evaluates for the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a model with no manual coding needed. Supported features include Post-Training Static Quantization, Post-Training Dynamic Quantization, and Mixed Precision. For more details please refer to this [guide](docs/AutoQuant.md).
 
 ### General Guide
-We currently provide 3 main user-facing APIs: enable, bench and superbench.
+We currently provide 3 main user-facing APIs for Neural Coder: enable, bench and superbench.
 #### Enable
 Users can use ```enable()``` to enable specific features into DL scripts:
 ```

neural_coder/docs/AutoQuant.md → neural_coder/docs/Quantization.md

@@ -1,6 +1,6 @@
-Auto-Quant Feature
+Neural Coder for Quantization
 ===========================
-This feature helps automatically enable quantization features on a PyTorch model script and automatically evaluates for the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a PyTorch model with no manual coding needed. Supported features include Post-Training Static Quantization, Post-Training Dynamic Quantization, and Mixed Precision.
+This feature helps automatically enable quantization on Deep Learning models and automatically evaluates for the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a model with no manual coding needed. Supported features include Post-Training Static Quantization, Post-Training Dynamic Quantization, and Mixed Precision.
 
 
 ## Features Supported