Merge pull request #409 from kybowm/master

xingdusas · web-flow · commit 8a3cc5f21a00 · 2024-08-07T16:20:45.000-05:00
Update README.md
diff --git a/README.md b/README.md
@@ -7,7 +7,7 @@
     <img src="https://img.shields.io/badge/SAS%20Viya-4.0-blue.svg?&colorA=0b5788&style=for-the-badge&logoWidth=30&logo=data:image/png;base64,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"
         alt="SAS Viya Version"/>
   </a>
-        
+
   <a href="https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html">
     <img src="https://img.shields.io/badge/pip_install_sas_dlpy-blue.svg?&style=for-the-badge&colorA=254f73" alt="Python Version">
   
@@ -19,30 +19,34 @@
 </div>
 
 ### Overview
-DLPy is a high-level Python library for the SAS Deep learning features 
-available in SAS Viya. DLPy is designed to provide an efficient way to 
-apply deep learning methods to image, text, and audio data. DLPy APIs 
-are created following the [Keras](https://keras.io/) APIs with a touch 
+
+DLPy is a high-level Python library for the SAS Deep learning features
+available in SAS Viya. DLPy is designed to provide an efficient way to
+apply deep learning methods to image, text, and audio data. DLPy APIs
+are created following the [Keras](https://keras.io/) APIs with a touch
 of [PyTorch](https://pytorch.org/) flavor.
 
-### What's Recently Added
-* DLModelzoo action support
-* Real-time plot for hyper-parameter tuning with DLModelzoo
-* New examples for APIs with DLModelzoo
-* PNG/base64 output format for segmentation models
-* Additional pre-defined network architectures such as ENet and Efficient-Net
+### Recently Added Features
+
+* DLPy now supports the DLModelzoo action through the use of `MZModel`.
+* Real-time plots for hyper-parameter tuning are available with DLModelzoo.
+* New examples are available for APIs with DLModelzoo.
+* Segmentation models can produce PNG/base64 output.
+* Additional pre-defined network architectures are available. Examples include ENet and Efficient-Net.
 
 ### Prerequisites
-- Python version 3.3 or greater is required
-- Install SAS [Scripting Wrapper for Analytics Transfer (SWAT)](https://github.com/sassoftware/python-swat) for Python using `pip install swat` or `conda install -c sas-institute swat`
-- Access to a SAS Viya 4.0 environment with [Visual Data Mining and Machine Learning](https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html) (VDMML) is required
-- To use timeseries APIs, access to either [SAS Econometrics](https://www.sas.com/en_us/software/econometrics.html) or [SAS Visual Forecasting](https://www.sas.com/en_us/software/visual-forecasting.html) is required
-- A user login to your SAS Viya back-end is required. See your system administrator for details if you do not have a SAS Viya account.
-- It is recommended that you install the open source graph visualization software called [Graphviz](https://www.graphviz.org/download/) to enable graphic visualizations of the DLPy deep learning models
-- Install DLPy using `pip install sas-dlpy` or `conda install -c sas-institute sas-dlpy`
-
-#### SAS Viya and VDMML versions vs. DLPY versions
-DLPy versions are aligned with the SAS Viya and VDMML versions. 
+
+* You must use Python version 3.3 or greater.
+* You must install SAS [Scripting Wrapper for Analytics Transfer (SWAT)](https://github.com/sassoftware/python-swat) for Python. You can install the package from PyPI by using the command `pip install swat` or from the SAS conda repository by using `conda install -c sas-institute swat`.
+* You must have access to a SAS Viya 4.0 environment that has [Machine Learning](https://www.sas.com/en_us/software/machine-learning-deep-learning.html) licensed.
+* To use time series APIs, you must have access to either [SAS Econometrics](https://www.sas.com/en_us/software/econometrics.html) or [SAS Visual Forecasting](https://www.sas.com/en_us/software/visual-forecasting.html).
+* You must have a user login to your SAS Viya back-end. See your system administrator for details if you do not have a SAS Viya account.
+* It is recommended that you install the open source graph visualization software called [Graphviz](https://www.graphviz.org/download/) to enable graphic visualizations of the DLPy deep learning models.
+* Install DLPy using `pip install sas-dlpy` or `conda install -c sas-institute sas-dlpy`.
+
+#### SAS Viya and DLPY Versions
+
+DLPy versions are aligned with SAS Viya versions.
 Below is the versions matrix.
 
 <table>
@@ -74,11 +78,12 @@ Below is the versions matrix.
 The table above can be read as follows: DLPy versions between 1.0 (inclusive)
 to 1.1 (exclusive) are designed to work with the SAS Viya 3.4.
 
-#### External Libraries ####
+#### External Libraries
+
 The following versions of external libraries are supported:
-- ONNX: versions >= 1.5.0
-- Keras: versions >= 2.1.3
 
+* ONNX: versions >= 1.5.0
+* Keras: versions >= 2.1.3
 
 ### Getting Started
 
@@ -90,7 +95,7 @@ Note: The default CAS port is 5570.
     >>> import swat
     >>> sess = swat.CAS('mycloud.example.com', 5570)
 
-Next, import the DLPy package, and then build a simple convolutional 
+Next, import the DLPy package, and then build a simple convolutional
 neural network (CNN) model.
 
 Import DLPy model functions:
@@ -111,7 +116,7 @@ Define an input layer to add to `model1`:
 
     NOTE: Input layer added.
 
-Add a 2D convolution layer and a pooling layer:
+Add a 2-D convolution layer and a pooling layer:
 
     # Add 2-Dimensional Convolution Layer to model1
     # that has 8 filters and a kernel size of 7. 
@@ -125,8 +130,8 @@ Add a 2D convolution layer and a pooling layer:
     >>> model1.add(Pooling(2))
 
     NOTE: Pooling layer added.
-    
-Add an additional pair of 2D convolution and pooling layers:
+  
+Add an additional pair of 2-D convolution and pooling layers:
 
     # Add another 2D convolution Layer that has 8 filters and a kernel size of 7 
 
@@ -139,15 +144,15 @@ Add an additional pair of 2D convolution and pooling layers:
     >>> model1.add(Pooling(2))
 
     NOTE: Pooling layer added.
-    
+  
 Add a fully connected layer:
 
     # Add Fully-Connected Layer with 16 units
     
     >>> model1.add(Dense(16))
 
     NOTE: Fully-connected layer added.
-    
+
 Finally, add the output layer:
 
     # Add an output layer that has 2 nodes and uses
@@ -158,38 +163,35 @@ Finally, add the output layer:
     NOTE: Output layer added.
     NOTE: Model compiled successfully 
 
-
 ### Additional Resources
-- DLPy examples: https://github.com/sassoftware/python-dlpy/tree/master/examples
-- DLPy API documentation [sassoftware.github.io/python-dlpy](https://sassoftware.github.io/python-dlpy/).
-- [SAS SWAT for Python](http://github.com/sassoftware/python-swat/)
-- [SAS ESPPy](https://github.com/sassoftware/python-esppy)
-- Watch: DLPy videos:
+
+* DLPy examples: <https://github.com/sassoftware/python-dlpy/tree/master/examples>
+* DLPy API documentation [sassoftware.github.io/python-dlpy](https://sassoftware.github.io/python-dlpy/).
+* [SAS SWAT for Python](http://github.com/sassoftware/python-swat/)
+* [SAS ESPPy](https://github.com/sassoftware/python-esppy)
+* Watch: DLPy videos:
   * DLPy v1.0 examples:
-     * [Image classification using CNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=125)
-     * [Object detection using TinyYOLOv2](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=390)
-     * [Import and export deep learning models with ONNX](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=627)
-     * [Text classification and text generation using RNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=943)
-     * [Time series forecasting using RNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=1185)
+    * [Image classification using CNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=125)
+    * [Object detection using TinyYOLOv2](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=390)
+    * [Import and export deep learning models with ONNX](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=627)
+    * [Text classification and text generation using RNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=943)
+    * [Time series forecasting using RNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=1185)
   * DLPy v1.1 examples:
-     * [Leverage Functional APIs to Build Complex Models](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=115s)
-     * [Image Segmentation with U-Net](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=399s)
-     * [Object Detection with Faster-RCNN](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=688s)
-     * [Image Classification with ShuffleNet and MobileNet](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=1158s)
-     * [Multi-class Deep learning](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=1648s)
-- [SAS Deep Learning with Python made easy using DLPy](https://blogs.sas.com/content/subconsciousmusings/2019/03/13/sas-deep-learning-with-python-made-easy-using-dlpy/)
+    * [Leverage Functional APIs to Build Complex Models](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=115s)
+    * [Image Segmentation with U-Net](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=399s)
+    * [Object Detection with Faster-RCNN](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=688s)
+    * [Image Classification with ShuffleNet and MobileNet](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=1158s)
+    * [Multi-class Deep learning](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=1648s)
+* [SAS Deep Learning with Python made easy using DLPy](https://blogs.sas.com/content/subconsciousmusings/2019/03/13/sas-deep-learning-with-python-made-easy-using-dlpy/)
 
 ### Contributing
-Have something cool to share? SAS gladly accepts pull requests on GitHub! See the [Contributor Agreement](https://github.com/sassoftware/python-dlpy/blob/master/ContributorAgreement.txt) for details.
-
-### Licensing 
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
-You may obtain a copy of the License at [LICENSE.txt](https://github.com/sassoftware/python-dlpy/blob/master/LICENSE.txt)
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. 
 
+Have something cool to share? We gladly accept pull requests on GitHub! See the [Contributor Agreement](https://github.com/sassoftware/python-dlpy/blob/master/ContributorAgreement.txt) for details.
 
+### Licensing
 
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
+You can obtain a copy of the License at [LICENSE.txt](https://github.com/sassoftware/python-dlpy/blob/master/LICENSE.txt)
 
-### for more products do visit our github and contribute.
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
