第一次ssh到ubuntu發生WARNING: REMOTE HOST IDENTIFICATION HAS CHANGED!

ssh-keygen -R 伺服器端的IP或網址
$ ssh-keygen -R 140.112.182.162

參考

https://blog.allenchou.cc/warning-remote-host-identification-has-changed/

在server端執行

$ sudo apt-get install openssh-server

參考

http://fecbob.pixnet.net/blog/post/39286433-ubuntu%E4%B8%8A%E4%BD%BF%E7%94%A8ssh-

Particle Swarm Optimization (IV)

Particle Swarm Optimization (III)

Particle Swarm Optimization (II)

Particle swarm optimization (I)

ubuntu 16.04 install HooMD (install conda)

ubuntu 16.04 install lammps-gpu

ubuntu 16.04 install Pulse Secure

1. 下載

$ wget http://ccnet.ntu.edu.tw/vpn/Download/ps-pulse-linux-9.0r2.1-b819-ubuntu-debian-64-bit-installer.deb

2. 安裝

$ sudo dpkg --install ps-pulse-linux-9.0r2.1-b819-ubuntu-debian-64-bit-installer.deb

3. 安裝missing dependency packages
$ sudo apt-get install libc6-i386 

$ sudo apt-get install lib32z1

4. 啟動Pulse client

$ /usr/local/pulse/PulseClient_x86_64.sh -h sslvpn2.ntu.edu.tw -u <username> -p <password> -U https://sslvpn2.ntu.edu.tw/  -r "NTU EMail Account"

5. 建立VPN連線

$ /usr/local/pulse/pulsesvc -h sslvpn2.ntu.edu.tw -u <username> -p <password> -r "NTU EMail Account" -U https://sslvpn2.ntu.edu.tw/

6. 確認連線狀態

$ /usr/local/pulse/PulseClient_x86_64.sh -S

參考：
https://kb.pulsesecure.net/articles/Pulse_Secure_Article/KB40126
https://github.com/HenrikBengtsson/ucsf-vpn/wiki/64-bit-Pulse-Client-on-Ubuntu-16.04

ubuntu 16.04 不能用jupyter notebook

The Jupyter HTML Notebook.

這將啟動一個基於tornado的HTML筆記本服務器，它提供一個html5/
javascript筆記本客戶端。

Install CUDA with apt

ubuntu 16.04 install tensorflow

$ sudo apt update
$ sudo apt install python3-dev python3-pip
$ sudo pip3 install -U virtualenv

$ virtualenv --system-site-packages -p python3 ./venv

$ source ./venv/bin/activate

$ pip install --upgrade pip

$ pip list

$ pip install --upgrade tensorflow

$ python -c "import tensorflow as tf; print(tf.__version__)"

有顯示版本代表安裝完成
$ pip install tensorflow-gpu (要先安裝cuDNN，驗證版本cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2)

$ deactivate

TensorFlow from source in Ubuntu 18

https://medium.com/@isaaclascasas/tensorflow-from-source-in-ubuntu-18-4b5dcca910b9?fbclid=IwAR1-E-n7KStl8gPjriDEe4QZtXKZSJfQUK86q6DZRQLhp3Vj9_0qbwYA0MI

如果有顯示Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA，在跑CPU版本的tensorflow時要加入下面兩行API

$ import os
$ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

由於tensorflow默認分佈是在沒有CPU擴展的情況下構建的，例如SSE4.1，SSE4.2，AVX，AVX2，FMA等。默認版本（來自pip install tensorflow的版本）旨在與儘可能多的CPU兼容。若是用GPU版本的話，就不必在乎這個問題發生。

如果有顯示ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory

$ ~/~/.zshrc

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64/

$ source ~/.zshrc

如果出現Traceback (most recent call last):
  File "<string>", line 1, in <module>
AttributeError: module 'tensorflow' has no attribute 'enable_eager_execution'

$ pip install tf-nightly  

$ pip install tf-nightly-gpu

ubuntu 16.04 install cuDNN

1. https://developer.nvidia.com/cudnn 下載

cuDNN v7.3.1 Library for Linux

cuDNN v7.3.1 Runtime Library for Ubuntu16.04 (Deb)

cuDNN v7.3.1 Developer Library for Ubuntu16.04 (Deb)

cuDNN v7.3.1 Code Samples and User Guide for Ubuntu16.04 (Deb)

2. https://docs.nvidia.com/deeplearning/sdk/cudnn-install/

$ tar -xzvf cudnn-9.0-linux-x64-v7.tgz

$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include

(sudo cp cuda/targets/ppc64le-linux/include/cudnn.h /usr/local/cuda/include)

$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64

(sudo cp cuda/targets/ppc64le-linux/lib/libcudnn* /usr/local/cuda/lib64)

$ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

$ sudo dpkg -i libcudnn7_7.3.1.20-1+cuda10.0_amd64.deb

$ sudo dpkg -i libcudnn7-dev_7.3.1.20-1+cuda10.0_amd64.deb

$ sudo dpkg -i libcudnn7-doc_7.3.1.20-1+cuda10.0_amd64.deb

$ cp -r /usr/src/cudnn_samples_v7/ $HOME

$ cd  $HOME/cudnn_samples_v7/mnistCUDNN

$ make clean && make

$ ./mnistCUDNN

有出現Test passed!代表完成

ubuntu 16.04 install CUDA

ubuntu install for keras

在安裝keras之前，要先安裝其他套件:
1. 安裝numpy和scipy，並確保安裝了基本的線性代數函式庫（BLAS），以便模型在CPU上快速運行。
2. 安裝兩個額外的包，在使用Keras時非常方便:HDF5(用於保存大型神經網絡文件)和Graphviz(用於可視化神經網絡架構)。
3. 通過安裝CUDA驅動程序和cuDNN，確保GPU能夠運行深度學習代碼。
4. 安裝Keras的後端:TensorFlow、CNTK或Theano。

Lammps install for Mac