# See https://github.com/pyenv/pyenv/wiki#suggested-build-environment
build-essential
curl
libbz2-dev
libffi-dev
liblzma-dev
libncurses5-dev
libreadline-dev
libsqlite3-dev
libssl-dev
libxml2-dev
libxmlsec1-dev
llvm
make
openssl
tk-dev
wget
xz-utils
zlib1g-dev
git

# Packages needed to build devtoolset
file
flex
g++
make
patch
rpm2cpio
unar
wget

# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn8=8.9.6.50-1+cuda12.2
libcudnn8-dev=8.9.6.50-1+cuda12.2

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.17.0.a

# Test dependencies
grpcio >= 1.24.3, < 2.0
portpicker == 1.6.0
scipy == 1.11.3
requests == 2.31.0
packaging==23.2
setuptools==68.2.2

clang-format-12
lld-17
colordiff
curl
ffmpeg
gdb
git
jq
less
libcurl3-dev
libcurl4-openssl-dev
libfreetype6-dev
libhdf5-serial-dev
libomp-17-dev
libssl-dev
libtool
libxml2-dev
libxslt1-dev
libzmq3-dev
mlocate
moreutils
openjdk-11-jdk
openjdk-11-jre-headless
openssl
patchelf
pkg-config
python3-dev
python3-setuptools
rsync
software-properties-common
sudo
swig
unzip
vim

    how we can reproduce your findings. For memory corruption triggerable in
    TensorFlow models, please demonstrate an exploit against one of Alphabet's
    models in <https://tfhub.dev/>
  - An explanation of who can exploit this vulnerability, and what they gain
    when doing so. Write an attack scenario that demonstrates how your issue