#!/bin/sh
## Example: a typical script with several problems
for f in $(ls *.m3u)
do
grep -qi hq.*mp3 $f \
&& echo -e 'Playlist $f contains a HQ file in mp3 format'
done
#!/bin/sh
## Example: The shebang says 'sh' so shellcheck warns about portability
## Change it to '#!/bin/bash' to allow bashisms
for n in {1..$RANDOM}
do
str=""
if (( n % 3 == 0 ))
then
str="fizz"
fi
if [ $[n%5] == 0 ]
then
str="$strbuzz"
fi
if [[ ! $str ]]
then
str="$n"
fi
echo "$str"
done
#!/bin/bash
## Example: ShellCheck can detect some higher level semantic problems
while getopts "nf:" param
do
case "$param" in
f) file="$OPTARG" ;;
v) set -x ;;
esac
done
case "$file" in
*.gz) gzip -d "$file" ;;
*.zip) unzip "$file" ;;
*.tar.gz) tar xzf "$file" ;;
*) echo "Unknown filetype" ;;
esac
if [[ "$$(uname)" == "Linux" ]]
then
echo "Using Linux"
fi
#!/bin/bash
## Example: ShellCheck can detect many different kinds of quoting issues
if ! grep -q backup=true.* "~/.myconfig"
then
echo 'Backup not enabled in $HOME/.myconfig, exiting'
exit 1
fi
if [[ $1 =~ "-v(erbose)?" ]]
then
verbose='-printf "Copying %f\n"'
fi
find backups/ \
-iname *.tar.gz \
$verbose \
-exec scp {} “myhost:backups” +
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#!/bin/bash
## This gist contains instructions about cuda v10.1 and cudnn 7.6 installation in Ubuntu 18.04 for Tensorflow 2.1.0
### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###
### If you have previous installation remove it first.
sudo apt-get purge nvidia*
sudo apt remove nvidia-*
sudo rm /etc/apt/sources.list.d/cuda*
sudo apt-get autoremove && sudo apt-get autoclean
sudo rm -rf /usr/local/cuda*
### to verify your gpu is cuda enable check
lspci | grep -i nvidia
### gcc compiler is required for development using the cuda toolkit. to verify the version of gcc install enter
gcc --version
# system update
sudo apt-get update
sudo apt-get upgrade
# install other import packages
sudo apt-get install g++ freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev
# first get the PPA repository driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list
sudo apt-get update
# installing CUDA-10.1
sudo apt-get -o Dpkg::Options::="--force-overwrite" install cuda-10-1 cuda-drivers
# setup your paths
echo 'export PATH=/usr/local/cuda-10.1/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig
# install cuDNN v7.6
# in order to download cuDNN you have to be regeistered here https://developer.nvidia.com/developer-program/signup
# then download cuDNN v7.6 form https://developer.nvidia.com/cudnn
CUDNN_TAR_FILE="cudnn-10.1-linux-x64-v7.6.5.32.tgz"
wget https://developer.nvidia.com/compute/machine-learning/cudnn/secure/7.6.5.32/Production/10.1_20191031/cudnn-10.1-linux-x64-v7.6.5.32.tgz
tar -xzvf ${CUDNN_TAR_FILE}
# copy the following files into the cuda toolkit directory.
sudo cp -P cuda/include/cudnn.h /usr/local/cuda-10.1/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-10.1/lib64/
sudo chmod a+r /usr/local/cuda-10.1/lib64/libcudnn*
# Finally, to verify the installation, check
nvidia-smi
nvcc -V
# install Tensorflow (an open source machine learning framework)
# I choose version 2.1.0 because it is stable and compatible with CUDA 10.1 Toolkit and cuDNN 7.6
sudo pip3 install --user tensorflow-gpu==2.1.0
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