89 lines
2.4 KiB
Plaintext
89 lines
2.4 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~=3.2.0
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matplotlib~=3.5.0
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numpy~=1.22.0
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pandas~=1.4.0
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scipy~=1.8.0
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##### Machine Learning packages
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scikit-learn~=1.0.2
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# Optional: the XGBoost library is only used in chapter 7
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xgboost~=1.5.0
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# Optional: the transformers library is only using in chapter 16
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transformers~=4.16.2
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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~=2.8.0
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# Optional: the TF Serving API library is just needed for chapter 18.
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tensorflow-serving-api~=2.8.0 # or tensorflow-serving-api-gpu if gpu
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tensorboard~=2.8.0
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tensorboard-plugin-profile~=2.5.0
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tensorflow-datasets~=4.5.2
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tensorflow-hub~=0.12.0
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# Optional: used in chapters 11 & 16 (for AdamW & seq2seq)
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tensorflow-addons~=0.16.1
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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]~=0.21.0
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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~=9.0.0
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graphviz~=0.19.1
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pyglet~=1.5.21
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#pyvirtualdisplay # needed in chapter 18, if on a headless server
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# (i.e., without screen, e.g., Colab or VM)
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##### Additional utilities
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# Efficient jobs (caching, parallelism, persistence)
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joblib~=1.1.0
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# Easy http requests
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requests~=2.27.0
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# Nice utility to diff Jupyter Notebooks.
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nbdime~=3.1.0
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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~=2.8.0
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# Optional: these libraries can be useful in chapter 3, exercise 4.
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nltk~=3.6.5
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urlextract~=1.5.0
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# Optional: these libraries are only used in chapter 16
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ftfy~=5.5.0
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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~=4.62.3
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ipywidgets~=7.6.5
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