docker
https://linuxconfig.org/how-to-install-docker-on-ubuntu-18-04-bionic-beaver
sudo apt-get remove docker docker-engine docker.io
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
echo "deb [arch=amd64] https://download.docker.com/linux/ubuntu xenial edge" | sudo tee /etc/apt/sources.list.d/docker.list
sudo apt update
sudo apt-get install docker-ce=18.06.1~ce~3-0~ubuntu
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu16.04/amd64/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
apt-cache madison nvidia-docker2 nvidia-container-runtime
sudo apt-get install -y nvidia-docker2=2.0.3+docker18.06.1-1 nvidia-container-runtime=2.0.0+docker18.06.1-1
sudo docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 --net=host -it nvidia/cuda:8.0-devel
sudo docker run --gpus all -e NVIDIA_VISIBLE_DEVICES=0 --net=host -it nvidia/cuda:10.0-devel
# downloading image
https://medium.com/@maniac.tw/ubuntu-18-04-安裝-nvidia-driver-418-cuda-10-tensorflow-1-13-a4f1c71dd8e5
adduser ierosodin
apt update
apt install byobu sudo vim
adduser ierosodin sudo
su ierosodin
cp /etc/skel/.profile ~/.bash_profile
cp /etc/skel/.bashrc ~/.bashrc
sudo apt install ssh openssh-server
# change port to 7015
sudo vim /etc/ssh/sshd_config
commit image before exit (in another terminal):
sudo docker ps -a
sudo docker commit -m "v1" NAMES nvidia/cuda:8.0-devel
exit
vim docker_home/entry.sh
keyin:
#!/bin/bash
sudo /etc/init.d/ssh start
login again:
sudo docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 -e DISPLAY=$DISPLAY --net=host -it -v /home/ierosodin/docker_home:/home/ierosodin -v /mnt/hdd/shared_folder/ierosodin:/home/ierosodin/hdd -v /tmp/.X11-unix:/tmp/.X11-unix -e QT_X11_NO_MITSHM=1 --shm-size 32G -v /dev:/dev --cap-add=ALL -u ierosodin -w=/home/ierosodin nvidia/cuda:8.0-devel
sudo docker run --gpus all -e NVIDIA_VISIBLE_DEVICES=0 --net=host -it -v /home/ierosodin/hdd/back_18/docker_home:/home/ierosodin -v /mnt/hdd/shared_folder/ierosodin:/home/ierosodin/hdd -v /tmp/.X11-unix:/tmp/.X11-unix -e QT_X11_NO_MITSHM=1 --shm-size 32G -v /dev:/dev --cap-add=ALL -u ierosodin -w=/home/ierosodin nvidia/cuda:10.0-devel
change entry permission:
chmod +x entry.sh
commit then exit, now, it complete to set up environment.
sudo docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 -e DISPLAY=$DISPLAY --net=host -it -v /home/ierosodin/docker_home:/home/ierosodin -v /mnt/hdd/shared_folder/ierosodin:/home/ierosodin/hdd -v /tmp/.X11-unix:/tmp/.X11-unix -e QT_X11_NO_MITSHM=1 --shm-size 32G -v /dev:/dev --cap-add=ALL -u ierosodin -w=/home/ierosodin nvidia/cuda:8.0-devel /bin/bash -c "./entry.sh && byobu"
kill docker image which is exited
sudo docker rm $(sudo docker ps -a -q -f status=exited)
show all image which is used
sudo docker ps -a
kill image
sudo docker kill image id
sudo docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0,1 -e DISPLAY=$DISPLAY --net=host -it -v /home/robotics/docker_home:/home/robotics -v /mnt/hdd/shared_folder/robotics:/home/robotics/hdd -v /tmp/.X11-unix:/tmp/.X11-unix -e QT_X11_NO_MITSHM=1 --shm-size 32G -v /dev:/dev --cap-add=ALL -u robotics -w=/home/robotics nvidia/cuda /bin/bash -c "./entry.sh && byobu"
sudo docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0,1 -e DISPLAY=$DISPLAY --net=host -it -v /home/robotics/docker_home:/home/robotics -v /mnt/hdd/shared_folder/robotics:/home/robotics/hdd -v /tmp/.X11-unix:/tmp/.X11-unix -e QT_X11_NO_MITSHM=1 --shm-size 32G -v /dev:/dev --cap-add=ALL -u robotics -w=/home/robotics robotics_container /bin/bash -c "./entry.sh && sudo -H byobu"
sudo docker run –net=host -it -v /home/gapcmgr/upload/Tony:/home/ierosodin -u ierosodin -w=/home/ierosodin ubuntu:late
st /bin/bash -c "./entry.sh && byobu"
docker build -t repo:tag -f [Dockerfile path]
/etc/docker/daemon.json
{
"insecure-registries" : ["140.113.24.75"]
}
sudo docker run –gpus all -e NVIDIA_VISIBLE_DEVICES=0 –net=host -it -v /home/ierosodin/hdd/back_18/docker_home:/home/ierosodin -v /mnt/hdd/shared_folder/ierosodin:/home/ierosodin/hdd -v /tmp/.X11-unix:/tmp/.X11-unix -e QT_X11_NO_MITSHM=1 –shm-size 32G -v /dev:/dev –cap-add=ALL -u ierosodin -w=/home/ierosodin nvidia/cuda:10.0-devel /bin/bash -c "./entry.sh && byobu"
Organization contact [name= ierosodin] ==Back to Catalog== 一種求根的方法,利用方程是在某一點一次微分的值極為其切線斜率,推得: ${x_{i+1}\ =\ x_i\ -\ \frac{f(x_i)}{f'(x_i)}}$ Newton method in optimization 在推導前,必須要先認識泰勒展開式,以及為何使用泰勒展開式來做估計會是合理的。
Mar 29, 2022Organization contact [name= ierosodin] ==Back to Catalog== 這裡我們想要推導出高斯分佈,首先,我們希望有一個分佈滿足以下兩點: 離平均值等距的地方,其機率相等 帶入參數 ${\mu,\ \sigma^2}$ 後,分佈是唯一的 這裡我們會需要一項重要的積分工具:jacobian
Mar 27, 2022Organization contact [name= ierosodin] ==Back to Catalog== Frequentist and bayesian 我們再次提到 frequentist 與 bayesian,已經知道, frequentist 只看過去的數據,來決定一切,因此一個機率很低的事件,可能因為過去沒有發生過,而被認為是不可能發生。但就 bayesian 而言,又可能因為一個太差的 prior,導致結果離現實差距太大。 回想前面的 naive baye's classifier,我們只考慮了離散的情況,這樣同樣會有沒發生過而被認為不可能發生的問題,解決的一種方法,即為給予一個 distribution 的 prior,這樣機率就不只是離散的了。 然而當有了 distribution 後,我們必須計算 distribution 上每個 ${P(\theta)}$ 的 likelihood,最後還要做 normalize 得到 posterior,這樣的運算太麻煩且太大,因此我們希望找到一個分佈,是 prior 與 posterior 的形式相同的,可以直接由 prior 加上新的 data 得到 posterior,即稱為 conjugate。
Mar 27, 2022or
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