99 lines
2.4 KiB
Plaintext
99 lines
2.4 KiB
Plaintext
# First make sure to update pip:
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# $ sudo pip install --upgrade pip
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#
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# Then you probably want to work in a virtualenv (optional):
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# $ sudo pip install --upgrade virtualenv
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# Or if you prefer you can install virtualenv using your favorite packaging
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# system. E.g., in Ubuntu:
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# $ sudo apt-get update && sudo apt-get install virtualenv
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# Then:
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# $ cd $my_work_dir
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# $ virtualenv my_env
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# $ . my_env/bin/activate
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#
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# Next, optionally uncomment the OpenAI gym lines (see below).
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# If you do, make sure to install the dependencies first.
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# If you are interested in xgboost for high performance Gradient Boosting, you
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# should uncomment the xgboost line (used in the ensemble learning notebook).
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#
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# Then install these requirements:
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# $ pip install --upgrade -r requirements.txt
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#
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# Finally, start jupyter:
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# $ jupyter notebook
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#
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##### Core scientific packages
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jupyter==1.0.0
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matplotlib==3.0.3
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numpy==1.16.2
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pandas==0.24.1
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scipy==1.2.1
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##### Machine Learning packages
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scikit-learn==0.20.3
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# Optional: the XGBoost library is only used in the ensemble learning chapter.
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xgboost==0.82
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##### Deep Learning packages
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# Replace tensorflow with tensorflow-gpu if you want GPU support. If so,
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# you need a GPU card with 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
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tf-nightly-2.0-preview
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##tensorflow-gpu
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#tf-nightly-gpu-2.0-preview
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#tensorboard
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tb-nightly
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#tensorflow-datasets
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tfds-nightly
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tensorflow-hub
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#tensorflow-probability
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tfp-nightly
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tensorflow-transform
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# Optional: OpenAI gym is only needed for the Reinforcement Learning chapter.
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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[all]==0.10.9
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# If you only want to install the Atari dependency, uncomment this line instead:
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#gym[atari]==0.10.9
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##### Image manipulation
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imageio==2.5.0
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Pillow==5.4.1
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scikit-image==0.14.2
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##### Extra packages (optional)
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# Nice utility to diff Jupyter Notebooks.
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#nbdime==1.0.5
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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==2.6.9
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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
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urlextract==0.9
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# Optional: tqdm displays nice progress bars, ipywidgets for tqdm's notebook support
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tqdm==4.31.1
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ipywidgets==7.4.2
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