From f2d36124f2fe8ea2859d3705db47911613caa6df Mon Sep 17 00:00:00 2001 From: kybowm Date: Wed, 7 Aug 2024 10:00:26 -0400 Subject: [PATCH 1/3] Apply markdown linting rules. --- README.md | 98 +++++++++++++++++++++++++++++-------------------------- 1 file changed, 51 insertions(+), 47 deletions(-) diff --git a/README.md b/README.md index a25a8366..f1b5666c 100755 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ SAS Viya Version - + Python Version @@ -19,30 +19,34 @@ ### 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 +### What's Recently Addeda + * 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 -### 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. +### Prerequisitesa + +* 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 versionsa + +DLPy versions are aligned with the SAS Viya and VDMML versions. Below is the versions matrix. @@ -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: @@ -125,7 +130,7 @@ 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 another 2D convolution Layer that has 8 filters and a kernel size of 7 @@ -139,7 +144,7 @@ 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 @@ -147,7 +152,7 @@ Add a fully connected layer: >>> 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 +163,37 @@ 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: +* 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 +### 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. - - - +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. -### for more products do visit our github and contribute. +### for more products do visit our github and contribute From 283fef7f9b75f4392dfdf0cff00b6c5a5b4140e1 Mon Sep 17 00:00:00 2001 From: kybowm Date: Wed, 7 Aug 2024 10:26:07 -0400 Subject: [PATCH 2/3] Update README.md --- README.md | 36 +++++++++++++++++------------------- 1 file changed, 17 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index f1b5666c..aae6c19b 100755 --- a/README.md +++ b/README.md @@ -26,27 +26,27 @@ 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 Addeda +### Recently Added Features -* 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 +* 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. -### Prerequisitesa +### 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 +* 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 with [Machine Learning](https://www.sas.com/en_us/software/machine-learning-deep-learning.html) licensed. +* To use time series 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 versionsa +#### SAS Viya and DLPY Versions -DLPy versions are aligned with the SAS Viya and VDMML versions. +DLPy versions are aligned with SAS Viya versions. Below is the versions matrix.
@@ -116,7 +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. @@ -131,7 +131,7 @@ Add a 2D convolution layer and a pooling layer: 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 @@ -186,14 +186,12 @@ Finally, add the output layer: ### 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. +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 may obtain a copy of the License at [LICENSE.txt](https://github.com/sassoftware/python-dlpy/blob/master/LICENSE.txt) +You can 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. - -### for more products do visit our github and contribute From 0ce288c74e385a3312c5f7461e92c0cc0c300a13 Mon Sep 17 00:00:00 2001 From: kybowm Date: Wed, 7 Aug 2024 10:39:13 -0400 Subject: [PATCH 3/3] Update README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index aae6c19b..6a6dd6af 100755 --- a/README.md +++ b/README.md @@ -36,13 +36,13 @@ of [PyTorch](https://pytorch.org/) flavor. ### Prerequisites -* You must use Python version 3.3 or greater +* 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 with [Machine Learning](https://www.sas.com/en_us/software/machine-learning-deep-learning.html) licensed. -* To use time series 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` +* 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