95 lines
2.3 KiB
Plaintext
95 lines
2.3 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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jupyter==1.0.0
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matplotlib==3.1.2
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numpy==1.17.3
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pandas==0.25.3
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scipy==1.3.1
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##### Machine Learning packages
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scikit-learn==0.22
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# Optional: the XGBoost library is only used in chapter 7
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xgboost==0.90
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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 with tensorflow-gpu, and replace tensorflow-serving-api
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# 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==2.0.1
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#tensorflow-gpu==2.0.0
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# Optional: the TF Serving API library is just needed for chapter 19.
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tensorflow-serving-api==2.0.0
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#tensorflow-serving-api-gpu==2.0.0
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tensorboard==2.0.0
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tensorflow-datasets==1.3.0
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tensorflow-hub==0.6.0
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tensorflow-probability==0.7
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# Optional: only used in chapter 13.
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# NOT AVAILABLE ON WINDOWS
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tfx==0.15.0
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# Optional: only used in chapter 16.
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# NOT AVAILABLE ON WINDOWS
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tensorflow-addons==0.6.0
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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[atari]==0.15.4
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# On Windows, install atari_py using:
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# pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py
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tf-agents==0.3.0rc0
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##### Image manipulation
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imageio==2.6.1
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Pillow==6.2.1
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scikit-image==0.16.2
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graphviz
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pydot==1.4.1
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opencv-python==4.1.2.30
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pyglet==1.3.2
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#pyvirtualdisplay # needed in chapter 16, 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==0.14.0
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# Easy http requests
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requests==2.22.0
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# Nice utility to diff Jupyter Notebooks.
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nbdime==1.1.0
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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==2.7.0
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# Optional: these libraries can be useful in the classification chapter,
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# exercise 4.
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nltk==3.4.5
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urlextract==0.13.0
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# Optional: tqdm displays nice progress bars, ipywidgets for tqdm's notebook support
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tqdm==4.40.0
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ipywidgets==7.5.1
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