DLPy example

Author: jlwalke2

examples/register_sas_dlpy_model.py

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+#!/usr/bin/env python
+# encoding: utf-8
+#
+# Copyright © 2019, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import swat
+from dlpy.applications import Sequential
+from dlpy.layers import Dense, InputLayer, OutputLayer
+from sasctl import Session
+from sasctl.tasks import register_model, publish_model
+
+
+# Connect to the CAS server
+s = swat.CAS('hostname', 5570, 'username', 'password')
+
+# Upload the training data to CAS
+tbl = s.upload('data/iris.csv').casTable
+
+# Construct a simple neural network
+model = Sequential(conn=s, model_table='dlpy_model')
+model.add(InputLayer())
+model.add(Dense(n=64))
+model.add(Dense(n=32))
+model.add(OutputLayer(n=3))
+
+# Train on the sample
+model.fit(data=tbl,
+          inputs=['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'],
+          target='Species',
+          max_epochs=50,
+          lr=0.001)
+
+# Export the model as an ASTORE and get a reference to the new ASTORE table
+s.deeplearn.dlexportmodel(modelTable=model.model_table, initWeights=model.model_weights, casout='astore_table')
+astore = s.CASTable('astore_table')
+
+# Connect to the SAS environment
+with Session('hostname', 'username', 'password'):
+    # Register the trained model by providing:
+    #  - the ASTORE containing the model
+    #  - a name for the model
+    #  - a name for the project
+    #
+    # NOTE: the force=True option will create the project if it does not exist.
+    model = register_model(astore, 'Deep Learning', 'Iris', force=True)
+
+    # Publish the model to SAS® Micro Analytic Service (MAS).  Specifically to
+    # the default MAS service "maslocal".
+    module = publish_model(model, 'maslocal')
+
+    # sasctl wraps the published module with Python methods corresponding to
+    # the various steps defined in the module (like "predict").
+    response = module.score(SepalLength=5.1, SepalWidth=3.5,
+                            PetalLength=1.4, PetalWidth=0.2)
+
+s.terminate()