Update libraries

environment.yml

@@ -4,42 +4,42 @@ channels:
   - defaults
 dependencies:
   - box2d-py=2.3  # used only in chapter 18, exercise 8
-  - ffmpeg=5.1  # used only in the matplotlib tutorial to generate animations
+  - ffmpeg=6.1  # used only in the matplotlib tutorial to generate animations
   - graphviz  # used only in chapter 6 for dot files
   - python-graphviz  # used only in chapter 6 for dot files
-  - ipython=8.5  # a powerful Python shell
-  - ipywidgets=8.0  # optionally used only in chapter 12 for tqdm in Jupyter
-  - joblib=1.1  # used only in chapter 2 to save/load Scikit-Learn models
-  - jupyterlab=3.4  # to edit and run Jupyter notebooks
-  - matplotlib=3.5  # beautiful plots. See tutorial tools_matplotlib.ipynb
-  - nbdime=3.1  # optional tool to diff Jupyter notebooks
-  - nltk=3.6  # optionally used in chapter 3, exercise 4
+  - ipython=8.17  # a powerful Python shell
+  - ipywidgets=8.1  # optionally used only in chapter 12 for tqdm in Jupyter
+  - joblib=1.3  # used only in chapter 2 to save/load Scikit-Learn models
+  - jupyterlab=4.0  # to edit and run Jupyter notebooks
+  - matplotlib=3.8  # beautiful plots. See tutorial tools_matplotlib.ipynb
+  - nbdime=3.2  # optional tool to diff Jupyter notebooks
+  - nltk=3.8  # optionally used in chapter 3, exercise 4
   - numexpr=2.8  # used only in the Pandas tutorial for numerical expressions
-  - numpy=1.23  # Powerful n-dimensional arrays and numerical computing tools
-  - pandas=1.4  # data analysis and manipulation tool
-  - pillow=9.2  # image manipulation library, (used by matplotlib.image.imread)
+  - numpy=1.26  # Powerful n-dimensional arrays and numerical computing tools
+  - pandas=2.1  # data analysis and manipulation tool
+  - pillow=10.1  # image manipulation library, (used by matplotlib.image.imread)
   - pip  # Python's package-management system
-  - py-xgboost=1.6  # used only in chapter 6 for optimized Gradient Boosting
+  - py-xgboost=1.7  # used only in chapter 6 for optimized Gradient Boosting
   - pydot=1.4  # used only for in chapter 10 for tf.keras.utils.plot_model()
   - python=3.10  # your beloved programming language! :)
-  - requests=2.28  # used only in chapter 19 for REST API queries
-  - scikit-learn=1.1  # machine learning library
-  - scipy=1.9  # scientific/technical computing library
-  - statsmodels=0.13  # used only in chapter 15 for time series analysis
-  - tqdm=4.64  # used only in chapter 12 to display nice progress bars
+  - requests=2.31  # used only in chapter 19 for REST API queries
+  - scikit-learn=1.3  # machine learning library
+  - scipy=1.11  # scientific/technical computing library
+  - statsmodels=0.14  # used only in chapter 15 for time series analysis
+  - tqdm=4.66  # used only in chapter 12 to display nice progress bars
   - wheel  # built-package format for pip
   - widgetsnbextension=4.0  # interactive HTML widgets for Jupyter notebooks
   - pip:
-    - keras-tuner~=1.1.3  # used in chapters 10 and 19 for hyperparameter tuning
-    - tensorboard-plugin-profile~=2.8.0  # profiling plugin for TensorBoard
-    - tensorboard~=2.10.0  # TensorFlow's visualization toolkit
-    - tensorflow-addons~=0.17.1  # used in chapters 11 & 16 (for AdamW & seq2seq)
-    - tensorflow-datasets~=4.6.0  # datasets repository, ready to use
-    - tensorflow-hub~=0.12.0  # trained ML models repository, ready to use
-    - tensorflow-serving-api~=2.10.0  # or tensorflow-serving-api-gpu if gpu
-    - tensorflow~=2.10.0  # Deep Learning library
-    - transformers~=4.21.3  # Natural Language Processing lib for TF or PyTorch
-    - urlextract~=1.6.0  # optionally used in chapter 3, exercise 4
-    - gym[classic_control,atari,accept-rom-license]~=0.26.1  # used only in ch18
-    - google-cloud-aiplatform~=1.17.0  # used only in chapter 19
-    - google-cloud-storage~=2.5.0  # used only in chapter 19
+    - keras-core  # used in chapter 10
+    - keras-tuner~=1.4.6  # used in chapters 10 and 19 for hyperparameter tuning
+    - tensorboard-plugin-profile~=2.14.0  # profiling plugin for TensorBoard
+    - tensorboard~=2.14.1  # TensorFlow's visualization toolkit
+    - tensorflow-datasets~=4.9.3  # datasets repository, ready to use
+    - tensorflow-hub~=0.15.0  # trained ML models repository, ready to use
+    - tensorflow-serving-api~=2.14.0  # or tensorflow-serving-api-gpu if gpu
+    - tensorflow~=2.14.0  # Deep Learning library
+    - transformers~=4.35.0  # Natural Language Processing lib for TF or PyTorch
+    - urlextract~=1.8.0  # optionally used in chapter 3, exercise 4
+    - gym[classic_control,atari,accept-rom-license]  # used only in ch18
+    - google-cloud-aiplatform~=1.36.2  # used only in chapter 19
+    - google-cloud-storage~=2.13.0  # used only in chapter 19

requirements.txt

@@ -4,20 +4,20 @@
 
 
 ##### Core scientific packages
-jupyterlab~=3.4.6
-matplotlib~=3.5.3
-numpy~=1.23.3
-pandas~=1.4.4
-scipy~=1.9.1
+jupyterlab~=4.0.8
+matplotlib~=3.8.1
+numpy~=1.26.2
+pandas~=2.1.3
+scipy~=1.11.3
 
 ##### Machine Learning packages
-scikit-learn~=1.1.2
+scikit-learn~=1.3.2
 
 # Optional: the XGBoost library is only used in chapter 7
-xgboost~=1.6.2
+xgboost~=2.0.2
 
 # Optional: the transformers library is only used in chapter 16
-transformers~=4.21.3
+transformers~=4.35.0
 
 ##### TensorFlow-related packages
 
@@ -27,26 +27,24 @@ transformers~=4.21.3
 # you must install CUDA, cuDNN and more: see tensorflow.org for the detailed
 # installation instructions.
 
-tensorflow~=2.10.0
+tensorflow~=2.14.0
+keras-core
 # Optional: the TF Serving API library is just needed for chapter 18.
-tensorflow-serving-api~=2.10.0  # or tensorflow-serving-api-gpu if gpu
+tensorflow-serving-api~=2.14.0  # or tensorflow-serving-api-gpu if gpu
 
-tensorboard~=2.10.0
-tensorboard-plugin-profile~=2.8.0
-tensorflow-datasets~=4.6.0
-tensorflow-hub~=0.12.0
+tensorboard~=2.14.1
+tensorboard-plugin-profile~=2.14.0
+tensorflow-datasets~=4.9.3
+tensorflow-hub~=0.15.0
 
 # Used in chapter 10 and 19 for hyperparameter tuning
-keras-tuner~=1.1.3
-
-# Optional: used in chapters 11 & 16 (for AdamW & seq2seq)
-tensorflow-addons~=0.17.1
+keras-tuner~=1.4.6
 
 ##### Reinforcement Learning library (chapter 18)
 
 # There are a few dependencies you need to install first, check out:
 # https://github.com/openai/gym#installing-everything
-gym[Box2D,atari,accept-rom-license]~=0.26.1
+gym[Box2D,atari,accept-rom-license]~=0.26.2
 
 # WARNING: on Windows, installing Box2D this way requires:
 # * Swig: http://www.swig.org/download.html
@@ -55,39 +53,40 @@ gym[Box2D,atari,accept-rom-license]~=0.26.1
 # It's much easier to use Anaconda instead.
 
 ##### Image manipulation
-Pillow~=9.2.0
+Pillow~=10.1.0
 graphviz~=0.20.1
 
 ##### Google Cloud Platform - used only in chapter 19
-google-cloud-aiplatform~=1.17.0
-google-cloud-storage~=2.5.0
+google-cloud-aiplatform~=1.36.2
+google-cloud-storage~=2.13.0
 
 ##### Additional utilities
 
 # Efficient jobs (caching, parallelism, persistence)
-joblib~=1.1.0
+joblib~=1.3.2
 
 # Easy http requests
-requests~=2.28.1
+requests~=2.31.0
 
 # Nice utility to diff Jupyter Notebooks.
-nbdime~=3.1.1
+nbdime~=3.2.1
 
 # May be useful with Pandas for complex "where" clauses (e.g., Pandas
 # tutorial).
-numexpr~=2.8.3
+numexpr~=2.8.7
 
 # Optional: these libraries can be useful in chapter 3, exercise 4.
-nltk~=3.7
-urlextract~=1.6.0
+nltk~=3.8.1
+urlextract~=1.8.0
 
 # Optional: tqdm displays nice progress bars, ipywidgets for tqdm's notebook
 #           support
-tqdm~=4.64.1
-ipywidgets~=8.0.2
+tqdm~=4.66.1
+ipywidgets~=8.1.1
 
 # Optional: pydot is only used in chapter 10 for tf.keras.utils.plot_model()
 pydot~=1.4.2
 
 # Optional: statsmodels is only used in chapter 15 for time series analysis
-statsmodels~=0.13.2
+statsmodels~=0.14.0
+