# 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~=3.4.3
numpy~=1.19.5
pandas~=1.3.3
scipy~=1.7.1

##### Machine Learning packages
scikit-learn~=1.0

# Optional: the XGBoost library is only used in chapter 6
xgboost~=1.4.2

# Optional: the transformers library is only using in chapter 15
transformers~=4.11.3

##### 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~=2.6.0
# Optional: the TF Serving API library is just needed for chapter 18.
tensorflow-serving-api~=2.6.0 # or tensorflow-serving-api-gpu if gpu

tensorboard~=2.7.0
tensorboard-plugin-profile~=2.5.0
tensorflow-datasets~=4.4.0
tensorflow-hub~=0.12.0
tensorflow-probability~=0.14.1

# Optional: only used in chapter 12.
tfx~=1.3.0

# Optional: only used in chapter 15.
tensorflow-addons~=0.14.0

##### Reinforcement Learning library (chapter 17)

# 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

# On Windows, install atari_py using:
# pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py

tf-agents~=0.10.0

##### Image manipulation
Pillow~=8.4.0
graphviz~=0.17
opencv-python~=4.5.3.56
pyglet~=1.5.21

#pyvirtualdisplay # needed in chapter 17, if on a headless server
                  # (i.e., without screen, e.g., Colab or VM)


##### Additional utilities

# Efficient jobs (caching, parallelism, persistence)
joblib~=0.14.1

# Easy http requests
requests~=2.26.0

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

# May be useful with Pandas for complex "where" clauses (e.g., Pandas
# tutorial).
numexpr~=2.7.3

# Optional: these libraries can be useful in the chapter 3, exercise 4.
nltk~=3.6.5
urlextract~=1.4.0

# Optional: these libraries are only used in chapter 15
ftfy~=6.0.3

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