# 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~=3.2.0
matplotlib~=3.5.0
numpy~=1.22.0
pandas~=1.4.0
scipy~=1.8.0

##### Machine Learning packages
scikit-learn~=1.0.2

# Optional: the XGBoost library is only used in chapter 7
xgboost~=1.5.0

# Optional: the transformers library is only using in chapter 16
transformers~=4.16.2

##### 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~=2.7.1
# Optional: the TF Serving API library is just needed for chapter 18.
tensorflow-serving-api~=2.7.0 # or tensorflow-serving-api-gpu if gpu

tensorboard~=2.8.0
tensorboard-plugin-profile~=2.5.0
tensorflow-datasets~=4.5.2
tensorflow-hub~=0.12.0

# Optional: used in chapters 11 & 16 (for AdamW & seq2seq)
tensorflow-addons~=0.15.0

##### Reinforcement Learning library (chapter 18)

# There are a few dependencies you need to install first, check out:
# https://github.com/openai/gym#installing-everything
gym[Box2D,atari,accept-rom-license]~=0.21.0

# 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~=9.0.0
graphviz~=0.19.1
pyglet~=1.5.21

#pyvirtualdisplay # needed in chapter 18, if on a headless server
                  # (i.e., without screen, e.g., Colab or VM)


##### Additional utilities

# Efficient jobs (caching, parallelism, persistence)
joblib~=1.1.0

# Easy http requests
requests~=2.27.0

# Nice utility to diff Jupyter Notebooks.
nbdime~=3.1.0

# May be useful with Pandas for complex "where" clauses (e.g., Pandas
# tutorial).
numexpr~=2.8.0

# Optional: these libraries can be useful in chapter 3, exercise 4.
nltk~=3.6.5
urlextract~=1.5.0

# Optional: these libraries are only used in chapter 16
ftfy~=5.5.0

# Optional: tqdm displays nice progress bars, ipywidgets for tqdm's notebook
#           support
tqdm~=4.62.3
ipywidgets~=7.6.5