-
$ openssl aes-256-cbc -salt -pbkdf2 -in inkyung.zip -out inkyung.zip.aes
-
$ echo "a_string" | openssl enc -e -aes-256-cbc -a -salt -pbkdf2
find . -maxdepth 2 -name "*.mp4" -exec cp {} . \;
Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:
TensorFlow is now distributed under an Apache v2 open source license on GitHub.
On Ubuntu 16.04LTS+:
Step 1. Install NVIDIA CUDA:
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:
in your host --
cd ~ && mkdir tf && cd $_
curl -fsSL https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu-jupyter.Dockerfile -o ./Dockerfile
git clone tensorflow
curl -fsSL https://raw.githubusercontent.com/minlaxz/scripts/master/laxz.bashrc -o ./bash.bashrc
docker build -f ./Dockerfile -t yourRepo/repo:tag .
this Dockerfile will download a bunch of cuda11.0 and cudnn8 libs includes jupyter
- To get started, install expo ->
npm install -g expo-cli - Login Expo with
expo login - Generate a test app with
expo init MyApp - Edit
App.jsfile and start project withexpo startThis will start up Metro Server in your development machine. - To start and run on android
expo start --android.
Deploying to GitHub Pages:
- Install Docker Guide
- Test Docker
$ sudo usermod -aG docker $USER. (Add USER to Docker Group)$ docker run hello-world
- Setting up NVIDIA Container Toolkit
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)$ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -$ curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
- Install Nvidia-Docker2
$ sudo apt install -y nvidia-docker2
let's assume a service is listening on remote machine @remote_localhost at port 5000.
NOTE: remote mahcine is not listening on 0.0.0.0 just localhost privately.
I want to forward that remote machine service to me (my local machine).
on my machine
$ pv TensorRT-7.2.1.6.Ubuntu-18.04.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz | nc -w 3 172.17.0.2 6969
nc -l -p 6969 > TensorRT-7.2.1.6.Ubuntu-18.04.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz