93 lines
2.5 KiB
Plaintext
93 lines
2.5 KiB
Plaintext
# TensorFlow is much easier to install using Anaconda, especially
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# on Windows or when using a GPU. Please see the installation
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# instructions in INSTALL.md
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##### Core scientific packages
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jupyterlab~=4.0.8
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matplotlib~=3.8.1
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numpy~=1.26.2
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pandas~=2.1.3
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scipy~=1.11.3
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##### Machine Learning packages
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scikit-learn~=1.3.2
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# Optional: the XGBoost library is only used in chapter 7
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xgboost~=2.0.2
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# Optional: the transformers library is only used in chapter 16
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transformers~=4.35.0
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##### TensorFlow-related packages
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# If you have a TF-compatible GPU and you want to enable GPU support, then
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# replace tensorflow-serving-api with tensorflow-serving-api-gpu.
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# Your GPU must have CUDA Compute Capability 3.5 or higher support, and
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# you must install CUDA, cuDNN and more: see tensorflow.org for the detailed
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# installation instructions.
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tensorflow~=2.14.0
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keras-core
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# Optional: the TF Serving API library is just needed for chapter 18.
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tensorflow-serving-api~=2.14.0 # or tensorflow-serving-api-gpu if gpu
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tensorboard~=2.14.1
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tensorboard-plugin-profile~=2.14.0
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tensorflow-datasets~=4.9.3
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tensorflow-hub~=0.15.0
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# Used in chapter 10 and 19 for hyperparameter tuning
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keras-tuner~=1.4.6
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##### Reinforcement Learning library (chapter 18)
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# There are a few dependencies you need to install first, check out:
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# https://github.com/openai/gym#installing-everything
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gym[Box2D,atari,accept-rom-license]~=0.26.2
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# WARNING: on Windows, installing Box2D this way requires:
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# * Swig: http://www.swig.org/download.html
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# * Microsoft C++ Build Tools:
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# https://visualstudio.microsoft.com/visual-cpp-build-tools/
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# It's much easier to use Anaconda instead.
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##### Image manipulation
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Pillow~=10.1.0
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graphviz~=0.20.1
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##### Google Cloud Platform - used only in chapter 19
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google-cloud-aiplatform~=1.36.2
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google-cloud-storage~=2.13.0
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##### Additional utilities
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# Efficient jobs (caching, parallelism, persistence)
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joblib~=1.3.2
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# Easy http requests
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requests~=2.31.0
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# Nice utility to diff Jupyter Notebooks.
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nbdime~=3.2.1
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# May be useful with Pandas for complex "where" clauses (e.g., Pandas
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# tutorial).
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numexpr~=2.8.7
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# Optional: these libraries can be useful in chapter 3, exercise 4.
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nltk~=3.8.1
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urlextract~=1.8.0
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# Optional: tqdm displays nice progress bars, ipywidgets for tqdm's notebook
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# support
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tqdm~=4.66.1
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ipywidgets~=8.1.1
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# Optional: pydot is only used in chapter 10 for tf.keras.utils.plot_model()
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pydot~=1.4.2
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# Optional: statsmodels is only used in chapter 15 for time series analysis
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statsmodels~=0.14.0
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