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Keras file format updates #128

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merged 11 commits into from
Aug 16, 2024
Merged

Keras file format updates #128

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d6f6e4a
update save, load syntax
bpaul4 Jul 17, 2024
c184dd7
regenerate outdated notebooks
bpaul4 Jul 17, 2024
837445c
generate new model files
bpaul4 Jul 17, 2024
a558d8a
remove old keras files
bpaul4 Jul 17, 2024
37a7952
Test using IDAES/idaes-pse#1401
lbianchi-lbl Jul 18, 2024
9ee1b5b
Update version constraint for TensorFlow
lbianchi-lbl Jul 18, 2024
c82968a
Address Python 3.8 failures due to Tensorflow 2.16.1 not being available
lbianchi-lbl Jul 18, 2024
fb7f1d3
Remove exclusion for Python 3.12 for Tensorflow
lbianchi-lbl Jul 18, 2024
78bccea
Merge branch 'main' into keras_file_format_updates
bpaul4 Aug 16, 2024
08e8691
Remove Python 3.8 support
lbianchi-lbl Aug 16, 2024
6efdbe7
Restore installing idaes-pse from main branch
lbianchi-lbl Aug 16, 2024
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5 changes: 3 additions & 2 deletions .github/workflows/core.yml
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Expand Up @@ -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"

Expand Down Expand Up @@ -62,9 +62,10 @@ jobs:
fail-fast: false
matrix:
python-version:
- '3.8'
- '3.9'
- '3.10'
- '3.11'
- '3.12'
os:
- linux
- win64
Expand Down
2 changes: 1 addition & 1 deletion .readthedocs.yml
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Expand Up @@ -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)
Expand Down
3 changes: 0 additions & 3 deletions idaes_examples/browse.py
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Expand Up @@ -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
Expand Down
6 changes: 3 additions & 3 deletions idaes_examples/mod/surrogates/AR_training_methods.py
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Expand Up @@ -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
Expand Down Expand Up @@ -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')
Expand All @@ -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)

Expand Down
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4 changes: 2 additions & 2 deletions idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb
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Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand Down
4,033 changes: 587 additions & 3,446 deletions idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb
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6 changes: 3 additions & 3 deletions idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb
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Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -635,4 +635,4 @@
},
"nbformat": 4,
"nbformat_minor": 3
}
}
6 changes: 3 additions & 3 deletions idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb
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Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -635,4 +635,4 @@
},
"nbformat": 4,
"nbformat_minor": 3
}
}
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8 changes: 4 additions & 4 deletions idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb
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Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -523,4 +523,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
}
}
3,435 changes: 521 additions & 2,914 deletions idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb
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6 changes: 3 additions & 3 deletions idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb
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Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand Down
6 changes: 3 additions & 3 deletions idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb
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Expand Up @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand Down
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72 changes: 41 additions & 31 deletions idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -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"
]
},
{
Expand All @@ -37,7 +37,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [
{
Expand All @@ -47,7 +47,7 @@
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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
}
}
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