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requirements.txt
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requirements.txt
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# TensorFlow is much easier to install using Anaconda, especially
# on Windows or when using a GPU. Please see the installation
# instructions in INSTALL.md
##### Core scientific packages
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~=1.3.2
# Optional: the XGBoost library is only used in chapter 7
xgboost~=2.0.2
# Optional: the transformers library is only used in chapter 16
transformers~=4.35.0
##### TensorFlow-related packages
# If you have a TF-compatible GPU and you want to enable GPU support, then
# replace tensorflow-serving-api with tensorflow-serving-api-gpu.
# Your GPU must have CUDA Compute Capability 3.5 or higher support, and
# you must install CUDA, cuDNN and more: see tensorflow.org for the detailed
# installation instructions.
tensorflow~=2.14.0
# Optional: the TF Serving API library is just needed for chapter 18.
tensorflow-serving-api~=2.14.0 # or tensorflow-serving-api-gpu if gpu
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~=1.4.6
##### Reinforcement Learning library (chapter 18)
# There are a few dependencies you need to install first, check out:
# https://github.com/Farama-Foundation/Gymnasium
swig~=4.1.1
gymnasium[Box2D,atari,accept-rom-license]~=0.29.1
# WARNING: on Windows, installing Box2D this way requires:
# * Swig: http://www.swig.org/download.html
# * Microsoft C++ Build Tools:
# https://visualstudio.microsoft.com/visual-cpp-build-tools/
# It's much easier to use Anaconda instead.
##### Image manipulation
Pillow~=10.1.0
graphviz~=0.20.1
##### Google Cloud Platform - used only in chapter 19
google-cloud-aiplatform~=1.36.2
google-cloud-storage~=2.13.0
##### Additional utilities
# Efficient jobs (caching, parallelism, persistence)
joblib~=1.3.2
# Easy http requests
requests~=2.31.0
# Nice utility to diff Jupyter Notebooks.
nbdime~=3.2.1
# May be useful with Pandas for complex "where" clauses (e.g., Pandas
# tutorial).
numexpr~=2.8.7
# Optional: these libraries can be useful in chapter 3, exercise 4.
nltk~=3.8.1
urlextract~=1.8.0
# Optional: tqdm displays nice progress bars, ipywidgets for tqdm's notebook
# support
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~=0.14.0