Skip to content
The SAS Deep Learning Python (DLPy) package provides the high-level Python APIs to deep learning methods in SAS Visual Data Mining and Machine Learning. It allows users to build deep learning models using friendly Keras-like APIs.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
dlpy - updating doc string for faster rcnn Aug 14, 2019
doc - pre release stuff Aug 13, 2019
examples table of contents fix Aug 7, 2019
.gitignore adding an item to gitignore May 31, 2019 - pre release stuff Aug 13, 2019 initial push to dlpy v1 Oct 25, 2018
ContributorAgreement.txt Review and reformat docstrings; add Sphinx directory; add contributor… Nov 17, 2017
LICENSE.txt Review and reformat docstrings; add Sphinx directory; add contributor… Nov 17, 2017 - minor update on readme Aug 13, 2019 image path change Mar 22, 2019 - post release stuff Aug 13, 2019

DLPy - SAS Viya Deep Learning API for Python

SAS Deep Learning Icon

An efficient way to apply deep learning methods to image, text, and audio data.

SAS Viya Version Python Version Python Version


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 created following the Keras APIs with a touch of PyTorch flavor.

What's Recently Added

  • Text, audio, and time series support in addition to image
  • New APIs for:
    • RNN based tasks: text classification, text generation, and sequence labeling
    • Object detection
    • Image segmentation
    • Time series processing and modeling
  • Processing audio files and creating speech recognition models
  • Additional pre-defined network architectures such as DenseNet, DarkNet, Inception, Yolo, FasterRCNN, U-Net, MobileNet, and ShuffleNet
  • Enhanced data visualization and metadata handling


  • Python version 3 or greater is required
  • Install SAS Scripting Wrapper for Analytics Transfer (SWAT) for Python using pip install swat or conda install -c sas-institute swat
  • Access to a SAS Viya 3.4 environment with Visual Data Mining and Machine Learning (VDMML) 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 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. Below is the versions matrix.

1.1.x 3.4 8.4
1.0.x 3.4 8.3

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 and VDMML 8.3.

External Libraries

The following versions of external libraries are supported:

  • ONNX: versions >= 1.5.0
  • Keras: versions >= 2.1.3

Getting Started

To connect to a SAS Viya server, import SWAT and use the swat.CAS class to create a connection:

Note: The default CAS port is 5570.

>>> import swat
>>> sess = swat.CAS('', 5570)

Next, import the DLPy package, and then build a simple convolutional neural network (CNN) model.

Import DLPy model functions:

>>> from dlpy import Model, Sequential
>>> from dlpy.layers import *

Use DLPy to create a sequential model and name it Simple_CNN:

>>> model1 = Sequential(sess, model_table = 'Simple_CNN')

Define an input layer to add to model1:

# The input shape contains RGB images (3 channels)
# The model images are 224 px in height and 224 px in width

>>> model1.add(InputLayer(3,224,224))

NOTE: Input layer added.

Add a 2D convolution layer and a pooling layer:

# Add 2-Dimensional Convolution Layer to model1
# that has 8 filters and a kernel size of 7. 

>>> model1.add(Conv2d(8,7))

NOTE: Convolutional layer added.

# Add Pooling Layer of size 2

>>> 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 

>>> model1.add(Conv2d(8,7))

NOTE: Convolutional layer added.

# Add a pooling layer of size 2 to # complete the second pair of 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
# the Softmax activation function 

>>> model1.add(OutputLayer(act='softmax',n=2))

NOTE: Output layer added.
NOTE: Model compiled successfully 

Additional Resources


Have something cool to share? SAS gladly accepts pull requests on GitHub! See the Contributor Agreement for details.


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

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.

You can’t perform that action at this time.