Keras file format updates (#128)

* update save, load syntax * regenerate outdated notebooks * generate new model files * remove old keras files * Test using IDAES/idaes-pse#1401 * Update version constraint for TensorFlow * Address Python 3.8 failures due to Tensorflow 2.16.1 not being available * Remove exclusion for Python 3.12 for Tensorflow * Remove Python 3.8 support * Restore installing idaes-pse from main branch --------- Co-authored-by: Ludovico Bianchi <lbianchi@lbl.gov>
IDAES · Aug 16, 2024 · e0d3966 · e0d3966
1 parent 9d87c17
commit e0d3966
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diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
@@ -34,7 +34,7 @@ concurrency:
 
 env:
   # default Python version to use for checks that do not require multiple versions
-  DEFAULT_PYTHON_VERSION: '3.8'
+  DEFAULT_PYTHON_VERSION: '3.10'
   IDAES_CONDA_ENV_NAME_DEV: idaes-examples-dev
   PYTEST_ADDOPTS: "--color=yes"
 
@@ -62,9 +62,10 @@ jobs:
       fail-fast: false
       matrix:
         python-version:
-          - '3.8'
           - '3.9'
           - '3.10'
+          - '3.11'
+          - '3.12'
         os:
           - linux
           - win64

diff --git a/.readthedocs.yml b/.readthedocs.yml
@@ -3,7 +3,7 @@ version: 2
 build:
   os: ubuntu-20.04
   tools:
-    python: "3.8"
+    python: "3.10"
   jobs:
     pre_build:
       # Set conf vars and  update the Sphinx configuration (conf.py)

diff --git a/idaes_examples/browse.py b/idaes_examples/browse.py
@@ -4,9 +4,6 @@
 # stdlib
 from importlib import resources
 
-if not hasattr(resources, "files"):
-    # importlib.resources.files() added in Python 3.9
-    import importlib_resources as resources
 import json
 import logging
 from logging.handlers import RotatingFileHandler

diff --git a/idaes_examples/mod/surrogates/AR_training_methods.py b/idaes_examples/mod/surrogates/AR_training_methods.py
@@ -224,7 +224,7 @@ def train_load_surrogates(retrain=False) -> Set[SurrType]:
 
     # Train surrogates using Keras
 
-    if os.path.exists('keras_surrogate/') and retrain is False:
+    if os.path.exists('keras_surrogate/keras_model.keras') and retrain is False:
         # surrogates folder already exists, skip training
         # we will load the object into the flowsheet later
         pass
@@ -263,7 +263,7 @@ def train_load_surrogates(retrain=False) -> Set[SurrType]:
         # Train surrogate (calls optimizer on neural network and solves
         # for weights)
         model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
-        mcp_save = tf.keras.callbacks.ModelCheckpoint('.mdl_wts.hdf5',
+        mcp_save = tf.keras.callbacks.ModelCheckpoint('.mdl_wts.keras',
                                                       save_best_only=True,
                                                       monitor='val_loss',
                                                       mode='min')
@@ -281,7 +281,7 @@ def train_load_surrogates(retrain=False) -> Set[SurrType]:
                                          input_bounds=input_bounds,
                                          input_scaler=input_scaler,
                                          output_scaler=output_scaler)
-        keras_surrogate.save_to_folder('keras_surrogate')
+        keras_surrogate.save_to_folder(keras_folder_name="keras_surrogate", keras_model_name="keras_model")
 
     trained_surr.add(SurrType.KERAS)
 

diff --git a/idaes_examples/notebooks/docs/surrogates/.mdl_wts.keras b/idaes_examples/notebooks/docs/surrogates/.mdl_wts.keras
diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb
@@ -223,7 +223,7 @@
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n",
         "    elif surrogate_type == SurrType.KERAS:\n",
-        "        keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "        keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(\n",
         "            keras_surrogate,\n",
@@ -531,7 +531,7 @@
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n",
         "    elif surrogate_type == SurrType.KERAS:\n",
-        "        keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "        keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(\n",
         "            keras_surrogate,\n",

diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb
diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb
@@ -223,7 +223,7 @@
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n",
         "    elif surrogate_type == SurrType.KERAS:\n",
-        "        keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "        keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(\n",
         "            keras_surrogate,\n",
@@ -531,7 +531,7 @@
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n",
         "    elif surrogate_type == SurrType.KERAS:\n",
-        "        keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "        keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(\n",
         "            keras_surrogate,\n",
@@ -635,4 +635,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 3
-}
+}
diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb
@@ -223,7 +223,7 @@
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n",
         "    elif surrogate_type == SurrType.KERAS:\n",
-        "        keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "        keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(\n",
         "            keras_surrogate,\n",
@@ -531,7 +531,7 @@
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n",
         "    elif surrogate_type == SurrType.KERAS:\n",
-        "        keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "        keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "        m.fs.surrogate = SurrogateBlock()\n",
         "        m.fs.surrogate.build_model(\n",
         "            keras_surrogate,\n",
@@ -635,4 +635,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 3
-}
+}
diff --git a/idaes_examples/notebooks/docs/surrogates/keras_surrogate/keras_model.keras b/idaes_examples/notebooks/docs/surrogates/keras_surrogate/keras_model.keras
diff --git a/...xamples/notebooks/docs/surrogates/keras_surrogate/variables/variables.data-00000-of-00001 b/...xamples/notebooks/docs/surrogates/keras_surrogate/variables/variables.data-00000-of-00001
diff --git a/idaes_examples/notebooks/docs/surrogates/keras_surrogate/variables/variables.index b/idaes_examples/notebooks/docs/surrogates/keras_surrogate/variables/variables.index
diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/.mdl_wts.keras b/idaes_examples/notebooks/docs/surrogates/omlt/.mdl_wts.keras
diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb
@@ -250,7 +250,7 @@
         "# Train surrogate (calls optimizer on neural network and solves for weights)\n",
         "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n",
         "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n",
-        "    \".mdl_wts.hdf5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n",
+        "    \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n",
         ")\n",
         "history = model.fit(\n",
         "    x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n",
@@ -268,7 +268,7 @@
         "    input_scaler=input_scaler,\n",
         "    output_scaler=output_scaler,\n",
         ")\n",
-        "keras_surrogate.save_to_folder(\"keras_surrogate\")\n",
+        "keras_surrogate.save_to_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "\n",
         "# revert back to normal output capture\n",
         "sys.stdout = oldstdout\n",
@@ -411,7 +411,7 @@
         "\n",
         "# create the Pyomo/IDAES block that corresponds to the surrogate\n",
         "# Keras\n",
-        "keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "m.fs.surrogate = SurrogateBlock()\n",
         "m.fs.surrogate.build_model(\n",
         "    keras_surrogate,\n",
@@ -523,4 +523,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
+}
diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb
diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb
@@ -250,7 +250,7 @@
         "# Train surrogate (calls optimizer on neural network and solves for weights)\n",
         "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n",
         "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n",
-        "    \".mdl_wts.hdf5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n",
+        "    \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n",
         ")\n",
         "history = model.fit(\n",
         "    x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n",
@@ -268,7 +268,7 @@
         "    input_scaler=input_scaler,\n",
         "    output_scaler=output_scaler,\n",
         ")\n",
-        "keras_surrogate.save_to_folder(\"keras_surrogate\")\n",
+        "keras_surrogate.save_to_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "\n",
         "# revert back to normal output capture\n",
         "sys.stdout = oldstdout\n",
@@ -411,7 +411,7 @@
         "\n",
         "# create the Pyomo/IDAES block that corresponds to the surrogate\n",
         "# Keras\n",
-        "keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "m.fs.surrogate = SurrogateBlock()\n",
         "m.fs.surrogate.build_model(\n",
         "    keras_surrogate,\n",

diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb
@@ -250,7 +250,7 @@
         "# Train surrogate (calls optimizer on neural network and solves for weights)\n",
         "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n",
         "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n",
-        "    \".mdl_wts.hdf5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n",
+        "    \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n",
         ")\n",
         "history = model.fit(\n",
         "    x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n",
@@ -268,7 +268,7 @@
         "    input_scaler=input_scaler,\n",
         "    output_scaler=output_scaler,\n",
         ")\n",
-        "keras_surrogate.save_to_folder(\"keras_surrogate\")\n",
+        "keras_surrogate.save_to_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "\n",
         "# revert back to normal output capture\n",
         "sys.stdout = oldstdout\n",
@@ -411,7 +411,7 @@
         "\n",
         "# create the Pyomo/IDAES block that corresponds to the surrogate\n",
         "# Keras\n",
-        "keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n",
+        "keras_surrogate = KerasSurrogate.load_from_folder(keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\")\n",
         "m.fs.surrogate = SurrogateBlock()\n",
         "m.fs.surrogate.build_model(\n",
         "    keras_surrogate,\n",

diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras b/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras
diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.h5 b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.h5
diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.keras b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.keras
diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb
@@ -2,7 +2,7 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -17,7 +17,7 @@
         "# University, West Virginia University Research Corporation, et al.\n",
         "# All rights reserved.  Please see the files COPYRIGHT.md and LICENSE.md\n",
         "# for full copyright and license information.\n",
-        "###############################################################################"
+        "###############################################################################\n"
       ]
     },
     {
@@ -37,7 +37,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 2,
       "metadata": {},
       "outputs": [
         {
@@ -47,7 +47,7 @@
               "<IPython.core.display.Image object>"
             ]
           },
-          "execution_count": 1,
+          "execution_count": 2,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -75,9 +75,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 3,
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated.  Please import\n",
+            "expression symbols from pyomo.core.expr  (deprecated in 6.6.2) (called from\n",
+            "<frozen importlib._bootstrap>:241)\n"
+          ]
+        }
+      ],
       "source": [
         "from pyomo.environ import (ConcreteModel,\n",
         "                           Block,\n",
@@ -117,33 +127,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 4,
       "metadata": {},
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n",
-            "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:41 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n",
-            "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:41 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n",
-            "2023-08-19 22:20:42 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:42 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n",
-            "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:43 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:44 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:44 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n",
-            "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:44 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n",
-            "2023-08-19 22:20:45 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:45 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n",
-            "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n",
-            "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:41:57 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n",
+            "2024-01-24 21:42:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:02 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n",
+            "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n",
+            "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:04 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n",
+            "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:05 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:07 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:07 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:07 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n",
+            "2024-01-24 21:42:08 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n",
+            "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:09 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n",
+            "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n",
+            "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n",
             "--------------------------------------------------------------------\n",
             "The degrees of freedom for the flowsheet is  0\n",
             "--------------------------------------------------------------------\n",
@@ -204,8 +214,8 @@
             "Number of equality constraint Jacobian evaluations   = 2\n",
             "Number of inequality constraint Jacobian evaluations = 0\n",
             "Number of Lagrangian Hessian evaluations             = 1\n",
-            "Total CPU secs in IPOPT (w/o function evaluations)   =      0.119\n",
-            "Total CPU secs in NLP function evaluations           =      0.003\n",
+            "Total CPU secs in IPOPT (w/o function evaluations)   =      0.362\n",
+            "Total CPU secs in NLP function evaluations           =      0.008\n",
             "\n",
             "EXIT: Optimal Solution Found.\n",
             "\n",
@@ -689,9 +699,9 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.12"
+      "version": "3.10.6"
     }
   },
   "nbformat": 4,
   "nbformat_minor": 3
-}
+}