Ubuntu 18.04 Environmental settings

Basic setting

Ubuntu installation
Network connection
Software updater
Graphic card driver
Language setting
Anaconda

Tensorflow

CUDA
CUDNN
Tensorflow

Pytorch

Pytorch

ETC

Pycharm
Pytorch tutorial
Other programs
Useful command
Teamviewer
KakaoTalk
Wheel speed

Useful command

Ubuntu installation

Version : 18.04

[reference]

Download ubuntu 18.04 [Link]
Prepare USB
Create booting disk [Link(Korean)]
Turn off Windows Quick Start [Link(Korean)]
BIOS
- Boot Ubuntu USB
Install Ubuntu
- Welcome : English
- Keyboard layout : ENG(US)
- Updates and other software : Normal installation / Download updates while installing Ubuntu
- Installation type : something else
  - I installed it on the remaining hard disk.
  - And, I partitioned it into 'partition swap' and 'ext4'. [Link]
- Where are you : seoul
- Who are you : write your information

Network connection

Settings
Network
Wired option
IPv4
- Input (Address, Netmask, Gateway, DNS)

Software updater

Searching window
software update

Graphic card driver

[reference]

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo ubuntu-drivers autoinstall
sudo reboot

(after reboot)

sudo nvidia-settings

Language setting

(Korean only)

[reference]

Settings
Region & Language
Input sources
- Add (+ button) Korean(Hangul) <- If 'Korean(Hangul)' (not 'Korean') does not exist, click 'Manage Installed Languages' and install it
- Delete (- button) English

(Optional - shortcut setting)

Input sources
Click Korean(Hangul)
Option button (It exists only in 'Korean(Hangul)' not ' Korean')
Hangul toggle key
- Add HAN/ENG key (Alt-R or Hangul)

Anaconda

Version : Python 3.7 version (64bit)

[reference]

Download anaconda (click the reference)

cd
cd Downloads/
bash Anaconda3-5.3.0-Linux-x86_64.sh

License agreement
Confirm install location
/root/.bachrc? [yes]
VSCode? [No]

(after reboot)

conda --version

CUDA

Version 9.0

[reference]

Install CUDA 9.0 [Link]
- Linux / x86_64 / Ubuntu / 17.04 (18.04 is not supported) / runfile (local)
- Base Installer (Download 1.6GB)

cd
cd Downloads/
sudo chmod +x cuda_9.0.176_384.81_linux.run
./cuda_9.0.176_384.81_linux.run --override

EULA? [accept]
Unsupported configuration? [yes]
Graphic driver? [no]
Cuda toolkit? [yes]
Confirm toolkit location
run with 'sudo'? [yes]
symbolic link? [no]
Cuda samples? [no]
Confirm sample location

nvcc --version

Version 10.2

Install CUDA 10.2 [Link]
- wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
- sudo sh cuda_10.2.89_440.33.01_linux.run
- Graphic driver? [no] (important) / symbolic link ? [no] (directly connect individual version by bashrc)

(Optional - install multiple CUDA versions)

When installing one version according to the above procedure and installing another one, the following error message may occur. (ex: Ubuntu 18.04 + CUDA8.0)

# Command lines
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installation Failed
Samples: Installation Failed

Logfile is /tmp/cuda_install_13486.log
Signal caught, cleaning up

# Log file
Uncompressing NVIDIA CUDA....................................................................................

Can't locate InstallUtils.pm in @INC (you may need to install the InstallUtils module) (@INC contains: /etc/perl /usr/local/lib/x86_64-linux-gnu/perl/5.26.1 /usr/local/share/perl/5.26.1 /usr/lib/x86_64-linux-gnu/perl5/5.26 /usr/share/perl5 /usr/lib/x86_64-linux-gnu/perl/5.26 /usr/share/perl/5.26 /usr/local/lib/site_perl /usr/lib/x86_64-linux-gnu/perl-base) at ./install-linux.pl line 6.

BEGIN failed--compilation aborted at ./install-linux.pl line 6.

Verifying archive integrity... All good.

Uncompressing NVIDIA CUDA Samples.......................................................................................

Can't locate InstallUtils.pm in @INC (you may need to install the InstallUtils module) (@INC contains: /etc/perl /usr/local/lib/x86_64-linux-gnu/perl/5.26.1 /usr/local/share/perl/5.26.1 /usr/lib/x86_64-linux-gnu/perl5/5.26 /usr/share/perl5 /usr/lib/x86_64-linux-gnu/perl/5.26 /usr/share/perl/5.26 /usr/local/lib/site_perl /usr/lib/x86_64-linux-gnu/perl-base) at ./install-sdk-linux.pl line 6.

BEGIN failed--compilation aborted at ./install-sdk-linux.pl line 6.
'uninstall_cuda_8.0.pl' -> '/usr/local/cuda-8.0/bin/uninstall_cuda_8.0.pl'

Type the following command and reinstall the CUDA.
- Unpack .run file ./cuda*.run --tar mxvf
- Copy InstallUtils.pm file cp InstallUtils.pm /usr/lib/x86_64-linux-gnu/perl-base
- export $PERL5LIB

(optional - install CUDA 10.0 + CUDNN 7.5)

CUDNN

Version 7.0.5

[reference]

Download this “cuDNN v7.0.5 Library for Linux”
Unpack the archive and move it the contents into the directory where you install CUDA 9.0

# Unpack the archive
tar -zxvf cudnn-9.0-linux-x64-v7.tgz
# Move the unpacked contents to your CUDA directory
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-9.0/lib64/
sudo cp  cuda/include/cudnn.h /usr/local/cuda-9.0/include/
# Give read access to all users
sudo chmod a+r /usr/local/cuda-9.0/include/cudnn.h /usr/local/cuda-9.0/lib64/libcudnn*

Install libcupti

sudo apt-get install libcupti-dev

Do the CUDA post-install actions

gedit ~/.bashrc

Write the below commands

export PATH="/usr/local/cuda-9.0/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH"

Restart ! or source ~/.bashrc

(Optional - Other version)

Other version [reference]

(Optional - CUDNN PATH)

Download CUDNN and UNPACK
move "lib64 and include folders" to /home/$pID/cudnn/$version
Add the below commands export LD_LIBRARY_PATH="/home/$pID/cudnn/$version/lib64:$LD_LIBRARY_PATH"

Tensorflow

[reference]

Create virtual environment for Tensorflow by Anaconda

conda create -n py36_tensorflow python=3.6
conda activate py36_tensorflow
conda deactivate

Install tensorflow (latest version)

conda activate py36_tensorflow
pip install --upgrade tensorflow-gpu
python -c "import tensorflow as tf; print(tf.__version__)"

Download other versions

# example
pip install tensorflow-gpu==1.4.0

Pytorch

[reference]

Create virtual environment for Pytorch by Anaconda
Install pytorch (various versions)

conda create -n seokeon_py36_torch041 python=3.6
source activate seokeon_py36_torch041
conda install pytorch=0.4.1 cuda90 -c pytorch
source deactivate

conda create -n seokeon_py27_torch041 python=2.7
source activate seokeon_py27_torch041
conda install pytorch=0.4.1 cuda90 -c pytorch
source deactivate

conda create -n seokeon_py36_torch031 python=3.6
source activate seokeon_py36_torch031
conda install pytorch=0.3.1 cuda90 -c pytorch
source deactivate

conda create -n seokeon_py27_torch031 python=2.7
source activate seokeon_py27_torch031
conda install pytorch=0.3.1 cuda90 -c pytorch
source deactivate

Install pytorch (latest version)

conda install pytorch-cpu torchvision-cpu -c pytorch
conda install pytorch torchvision cudatoolkit=8.0 -c pytorch
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch

Pycharm

[reference]

Install pycharm using Snaps

sudo snap install pycharm-community --classic 
# or sudo snap install pycharm-professional --classic

pop up the message "pycharm-community 2017.3.3 from 'jetbrains' installed"

pycharm-community

Make new project
Environmental settings
- File
- Settings
- Project interpreter
- Select the virtual environment where you installed pytorch
Change pycharm keymap
- File
- Settings
- Keymap
  - comment : ctrl+R
  - run : F5
  - debug : F6
  - resume program : F7
  - close (Editor Tabs) : ctrl+W
  - Quick Evaluate Expression : Shift+F8
  - Evaluate Expression : F8
  - step over : F10
  - step into : F11
  - step out : shift + F11
  - Toggle line breakpoint : F12
  - Code->Folding->Collapse All: Ctrl+Alt+minus
Run/Debug configurations
- Python interpreter (python directory in the virtual environment)
- Environment variables (optional.. for TensorFlow)
  - Add (Name : LD_LIBRARY_PATH / Value : /usr/local/cuda-9.0/lib64)
- Working directory
  - /home/user_name/PycharmpProjects/your_project/

Pytorch tutorial

[reference]

Other programs

In the vitual environment (anaconda)

conda activate pytorch36
pip install scipy
pip install sacred
pip install matplotlib
pip install opencv-python
pip install pillow
pip install numpy
conda install -c pytorch torchvision
conda install -c anaconda pyyaml
conda deactivate

Teamviewer

[reference]

KakaoTalk

[reference]

Caution! An error may occur
Install Wine and environmental settings

sudo apt install wine-stable cabextract
WINEARCH=win32 WINEPREFIX=~/.wine wine wineboot
wget  https://raw.githubusercontent.com/Winetricks/winetricks/master/src/winetricks
chmod +x winetricks
./winetricks --optout

Select the default winprefix
Install a Windows DLL or component
Select (gdiplus, riched30, wmp9, msxml6)
Copy Gulim font (window to ubuntu)
copy C:/Windows/Fonts/gulim.ttf (or ttc) -> ~/.wine/drive_c/windows/Fonts (using cp -i or something else)

chmod 644 ~/.wine/drive_c/windows/Fonts/gulim.ttf
gedit ~/.wine/system.reg

Change from

"MS Shell Dlg"="Tahoma"
"MS Shell Dlg 2"="Tahoma"

"MS Shell Dlg"="Gulim"
"MS Shell Dlg 2"="Gulim"

Install kakaoTalk

winecfg

Confirm the version of window
Download the file (version check) [Download]
Install it

wine-stable KakaoTalk_Setup.exe

Change language setting

gedit ~/.local/share/applications/wine/Programs/KakaoTalk/KakaoTalk.desktop

Change from

Exec=env WINEPREFIX="/home/ubuntu/.wine" wine-stable C:\\\\windows\\\\command\\\\start.exe /Unix /home/ubuntu/.wine/dosdevices/c:/ProgramData/Microsoft/Windows/Start\\ Menu/Programs/KakaoTalk/KakaoTalk.lnk

Exec=env WINEPREFIX="/home/ubuntu/.wine" LANG="ko_KR.UTF-8" wine-stable C:\\\\windows\\\\command\\\\start.exe /Unix /home/ubuntu/.wine/dosdevices/c:/ProgramData/Microsoft/Windows/Start\\ Menu/Programs/KakaoTalk/KakaoTalk.lnk

Notably, LANG="ko_KR.UTF-8" is Only added
Solving the problem of broken font

cd "/home/ubuntu/.wine/dosdevices/c:/Program Files/Kakao/KakaoTalk"
LANG="ko_KR.UTF-8" wine-stable KakaoTalk.exe

Setting system tray

sudo apt install gnome-shell-extension-top-icons-plus

Extensions
- Topicons plus (check!)

Wheel speed

[reference]

sudo apt-get install imwheel
imwheel
sudo gedit /etc/X11/imwheel/startup.conf

Change ‘IMWHEEL_START=0’ to ‘IMWHEEL_START=1’

gedit ~/.imwheelrc

Copy all the contents in [ref]
Add below commands (the number 3 means wheel speed)

".*"
None,      Up,   Button4, 3 
None,      Down, Button5, 3

imwheel -k

Useful command

compress files : zip -r zipname.zip filename
unzip : tar -xvzf "file name"
unzip all 'zip' files :

for file in `ls *.zip`; do unzip "${file}" -d "${file:0:-4}"; done
for file in `ls *.zip`; do unzip "${file}" -d "./"; done
for file in `ls *.rar`; do unrar e "${file}"; done

zip by 7z:
- sudo apt-get install p7zip-full
- 7z a data.7z data.txt (zip)
- 7z x data.7z (unzip, )
remove folder : rm -rf "folder name"
remove 해당 디렉토리 내의 특정파일 삭제: find . -type f -name "*.zip" -exec rm {} ; link
make folder : mkdir "folder name"
copy folder : cp -r "folder a" "folder b"
copy folder (w/o overwrite) : rsync -a -v --ignore-existing src dst
move folder : mv "folder a" "folder b"
list subdirectories : tree -d -L 1 find src -mindepth 2 -maxdepth 3 -type d > list.txt
Visualize gpu situation (auto update)

nvidia-smi -l 1

Caution! Removing "Alt" function from "Han/Eng" key on the keyboard. [reference]

xmodmap -e 'remove mod1 = Alt_R'
xmodmap -e 'keycode 108 = Hangul'

Option -> Region & Language -> Korean (Hangul) -> Option -> Shortkey (Alt+R -> Hangul)

Chrome auto scroll [reference]
Symbolic (soft) link : ln -s target_path(old) link_path(new)
- ln -s ../../../DB/reid/old_DB ./ 하면 ./ 위치에 old_DB 라는 폴더 생성
- ln -sf ../../../DB/reid/old_DB ./new_DB 하면 ./ 위치에 new_DB 라는 폴더 생성
Count the number of files in the certain path find /path/to -type f | wc -l
Count the number of files in the present path find . -type f | wc -l
Remove conda env conda env remove -n ENV_NAME
Personal PATH export PPATH="/path/to" in ~/.bashrc
Add 'new document' option when right clicking touch ~/Templates/Empty\ Document
Change ":" to "," in filename find . -name "*:*" -exec rename 's|:|,|g' {} \;
- sudo apt install rename
GPU temperature
- nvidia-smi -q -d temperature -l 1
우분투 APT repository 제거하기 [ref]
- sudo add-apt-repository --remove ppa:~~~~~ (지우길 원하는 프로그램 이름명)
터미널 열었을 때 (base) 있는 경우
- conda config --show | grep auto_activate_base
- conda config --set auto_activate_base False
디스크 용량 체크
- df -h
폴더 내의 용량 체크
- du -h
빠른 삭제
- sudo rm -r -f /path/
Compression
- (install) sudo apt-get install p7zip-full
- (compress) 7z a /created_file_name/ /folder_name or */
- (check) 7z l /7z_file/
- (extract) 7z e /7z_file/
Memory check
- watch -d free / watch -n 1 free
파일 수 세기
- find . -type f | wc -l
해당 조건 파일 옮기기
- find path_A -name '*.jpg' -exec mv -t path_B {} +
- maxdepth 1
Pycharm deployment (다른 컴퓨터의 pycharm 에서 코드 돌리는 법)
- original code git commit (or copy your code to new com)
- git clone repo (or paste the code)
- Settings -> Build, Execution, Deployment -> new connection (+)
  - Connection: SFTP -> SSH Configuration (make new IP) -> Test connection
  - Mappings: Local path (new com), Deployment path (server com) => same folder
  - If failed at Test connection, in server com or apply VPN (university)
    - sudo apt update -y
    - sudo apt-get install openssh-server
    - sudo service ssh start
    - sudo service openssh-server start
- Project -> Python Interpreter -> add (+) -> SSH Interpreter -> Existing server configuration -> Interpreter (server -> anaconda -> env -> bin -> python) -> sudo -> Sync folder (setting remote path) -> check automatically upload
- Setting configuration
pip 이용한 설치중 Cannot uninstall '~~~' 에러발생
- sudo pip install pwntools 대신에 sudo pip install --ignore-installed pwntools
ppt FHD 동영상 저장 방법 (https://m.blog.naver.com/PostView.nhn?blogId=radiobj5&logNo=220345624061&proxyReferer=https:%2F%2Fwww.google.com%2F)
- 동영상 녹음 후에
- Alt + F11
- 삽입 -> 모듈

Sub MkVideo()
    If ActivePresentation.CreateVideoStatus <> ppMediaTaskStatusInProgress Then
    ActivePresentation.CreateVideo FileName:=Environ("USERPROFILE") & "\Desktop\test.mp4", _
    UseTimingsAndNarrations:=True, _
    VertResolution:=1080, _
    FramesPerSecond:=25, _
    Quality:=100
    Else:
    MsgBox "There is another conversion to video in progress"
    End If
    End Sub

F5
server 관리
- ssh 명령어 short-cut관리: gedit ~/.ssh/config
- ssh 바로 접근: ssh username@ip_adress
- id 생성
  - 서버 root계정으로 로그인
  - sudo adduser id_name
- CUDA 설정
  - vim ~/.bashrc
    - Cuda path
      - export PATH="/usr/local/cuda-10.0/bin:$PATH"
      - export LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH"
    - Anaconda path
      - export PATH="/home/ROOT_NAME/anaconda3/bin:$PATH"
      - export LD_LIBRARY_PATH="/home/ROOT_NAME/anaconda3/lib64:$LD_LIBRARY_PATH"
  - source ~/.bashrc
- error control
  - anaconda 가상환경 설정시 permission error: sudo chmod -R 777 anaconda3
  - slurm에서 sbatch 안먹을때 (sinfo 입력했을때 drain인경우)
    - 돌아가는 job 있을때: scontrol update nodename=node10 state=resume
    - 돌아가는 job 없을때: scontrol update nodename=node10 state=idle
- 서버에서 다른 cuda version 쓰고 싶을 때 (10.2 기준)
  - CUDA 파일 다운로드: wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
  - CUDA 설치: sudo sh cuda_10.2.89_440.33.01_linux.run
    - EULA? [accept]
    - Unsupported configuration? [yes]
    - Graphic driver? [no] (important)
    - Cuda toolkit? [yes]
    - run with 'sudo'? [yes]
    - symbolic link? [no] (directly connect individual version by bashrc)
    - Cuda samples? [no]
  - CUDNN 파일 다운로드
  - CUDNN 설치
  - CUDNN 이동
    - move "lib64 and include folders" to /home/choi/cudnn/$version
  - link
    - vim ~/.bashrc
      - export PATH="/usr/local/cuda-10.2/bin:$PATH"
      - export LD_LIBRARY_PATH="/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH"
      - export LD_LIBRARY_PATH="/home/choi/cudnn/$version/lib64:$LD_LIBRARY_PATH"
    - source ~/.bashrc
- 남은 용량 확인 (현재 폴더에서 각각 폴더가 차지하고 있는 양 확인)
  - du -sh *
conda activate 가 안되고 source activate만 되는 경우
- source ~/anaconda3/etc/profile.d/conda.sh
nvidia graphic driver 다른 버전 쓰고 싶을 때 (https://codechacha.com/ko/install-nvidia-driver-ubuntu/)
- 자동 버전 설치
  - sudo add-apt-repository ppa:graphics-drivers/ppa
  - sudo apt update
  - sudo ubuntu-drivers autoinstall
  - sudo reboot
- 기존 삭제(만약 기존 설치된 프로그램과 출동한다면): sudo apt --purge autoremove nvidia*
Automatic mixed precision 쓰는 법 (https://hoya012.github.io/blog/Image-Classification-with-Mixed-Precision-Training-PyTorch-Tutorial/)
- CUDA10.1 버전 이상만 pytorch1.6 지원
- CUDA10.1 버전 이상 설치 / CUDNN 설치 (필수인지는 확실하지 않음)
- conda create -n seokeon_torch16 python=3.6
- conda activate seokeon_torch16
- conda install pytorch torchvision cudatoolkit=10.1 -c pytorch (10.2도 가능)
- nvidia graphic driver 업그레이드
- 코드에서 변경해야 하는 부분
  - AMP_flag = True
  - if AMP_flag:
    - self.scaler = torch.cuda.amp.GradScaler() [추가된부분]
  - dataloader iteration 내부에서
    - if AMP_flag:
      - with torch.cuda.amp.autocast(): [추가된부분]
        
        outputs = self.model(inputs)
        
        loss = self.criterion(outputs, labels)
        
        self.optimizer.zero_grad()
        
        self.scaler.scale(loss).backward() [변경된부분]
        
        self.scaler.step(self.optimizer) [변경된부분]
        
        self.scaler.update() [변경된부분]
    - else:
      - outputs = self.model(inputs)
      - loss = self.criterion(outputs, labels)
      - self.optimizer.zero_grad()
      - loss.backward()
      - self.optimizer.step()
Mount 완련!!
- bootloader가 켜지지 않고 grup gnu terminal 창만 나오는 경우ㅜ
  - ubuntu booting USB로 부팅(try ubuntu without installing)
  - 인터넷연결
  - sudo add-apt-repository ppa:yannubuntu/boot-repair
  - sudo apt-get update
  - sudo apt-get install -y boot-repair
  - boot-repair
  - Click Recommended repair
- gpt to mbr (https://www.linuxtopic.com/2017/02/convert-partition-table-gpt-to-mbr-in.html)
  - install gdisk
  - gdisk /dev/sda
  - command: r
  - Recovery/transformation command? g
  - (MBR command: p)
  - MBR command: w
  - coverted 1 paritions. Finalize and exit? (Y/N): y
  - (command: w)
  - reboot
- 4TB이상 하드를 사서 리눅스를 설치할꺼면? (http://blog.naver.com/PostView.nhn?blogId=5bpa&logNo=220460531819)
- 하드디스크 처음 마운트 (https://seongkyun.github.io/others/2019/03/05/hdd_mnt/)
  - sudo fdisk -l 에서 하드 확인
  - 용량이 2TB 이하인 경우
    - sudo fdisk /dev/sda
    - command: n
    - select: p
    - Partition number: 1
    - First sector: (enter)
    - Last sector: (enter) -> created a new partition ~~
    - command: p
    - command: w
  - format
    - sudo mkfs.ext4 /dev/sda1
  - uuid 확인
    - 해당 disk의 UUID 복사
  - mount
    - sudo mkdir /mnt/directory-to-mount
    - sudo vim /etc/fstab
      - UUID=~~~~~ /directory-to-mount ext4 defaults 0 0
      - 맨 아랫줄에 입력
    - sudo mount -a
    - df -h (마운트 확인)
  - symbolic link
    - sudo ln -s /directory-to-mount /home/choi/
    - cd ~/directory-to-mount
    - sudh chmod 777 ~/directory-to-mount
- Change mount position
```
lsblk # check disk position
sudo xdg-open /etc/fstab # change disk position
```
  Add /dev/sdc /mnt/hard1 ntfs-3g defaults 0 2 (??? not completed)
- Unrecognized mount option "default"
  - vim /etc/fstab
  - 에서 default라고 적힌것 defaults로
- hard가 read-only 인경우 (window caches 에 의해서)
  - https://askubuntu.com/questions/462381/cant-mount-ntfs-drive-the-disk-contains-an-unclean-file-system
  - https://hidekuma.github.io/ec2/ubuntu/linux/remount-ebs/
  - $ cat /proc/mounts 에서 해당 디스크 ro로 되어있는지 확인
  - $ sudo umount /dev/sdXY
  - $ sudo ntfsfix /dev/sdXY (두번?)
  - $ sudo mount -o rw /dev/sdXY
7zip 압축
- 7z a kernel.7z kernel/ -v50m
- 50mb 분할 압축
- kernel.7z.001, kernel.7z.002 파일 생성
7zip 해제
- 7z x kernel.7z.001 -aoa

출처: https://ysh0222.tistory.com/26 [Sangho Yoon]

다른 서버 폴더 접근
- 폴더 gui에서
- connect to server
  - sftp://ID@ip
가상환경이나 현재 python에 pip으로 설치된 패키지 목력정보 만들기
- pip freeze > requirement.txt (문서생성)
- pip install -r requirements.txt (pip install)
Git 관련
- 명령어로 연동
  - git clone ~~~
  - 파일 수정
  - git add --all
  - git commit -m "Fix ~~ or Update ~~"
  - git push origin master
- pycharm 과 연동 link
  - Web에서 repository 생성
  - VCS>Get from Version Control
  - Github ID login
  - Repository 연결해서 원하는 폴더에 다운로드
  - 원하는 파일 옮겨닮기 (외부에서 옮기면 따로 pycharm에서 add해야하므로 pycharm의 프로젝트 창으로 파일 바로 옮겨줌)
  - commit
  - push
- 다른 github repository에 pull request를 하는 방법
  - Fork the repository
  - 1. local 작업
    - git clone repository
    - cd repository
    - Create a new branch
      - git branch new-branch
      - git checkout new-branch
      - or
      - git checkout -b new-branch
      - If you want to switch back to master
      - git checkout master
    - Make change locally
      - Modify an existing file or add a new file
      - git add filename.md or git add -A
      - git commit -m "Fixed documentation typos" or git config --global core.editor "nano" (장문을 쓰길 원하면, nano대신 vim가능)
      - git status (생략가능, verify 단계)
      - git push --set-upstream origin new-branch (forked repository가 변경되어 있을 것이다)
  - 1. In repositoy
    - 원하는 파일 수정
  - 1. Pull request (1 or 2 수행 이후)
    - Repository-> Pull request->New pull request
conda env 복사 붙여넣기 link
- anaconda version이 다르면 에러날수도
- conda activate 이름
- conda env export > environment.yaml
- python --version (Python 3.6.6) 인 경우
- conda create --name [이름] python=3.6 (environment.yml 에 있는 이름과 동일, 복사 하고자 하는 서버에 같은 파이선 버전 생성)
- conda activate [이름]
- conda env create environment.yml
- conda 생성 안하고 바로할경우
  - conda env create --prefix <your_conda_env_path> -f environment.yml (envs/이름) 까지

bismex / ubuntu-18.04-environmental-settings Goto Github PK

ubuntu-18.04-environmental-settings's Introduction

Ubuntu 18.04 Environmental settings

Table of Contents

Ubuntu installation

Version : 18.04

Network connection

Software updater

Graphic card driver

Language setting

(Korean only)

Anaconda

Version : Python 3.7 version (64bit)

CUDA

Version 9.0

Version 10.2

CUDNN

Version 7.0.5

Tensorflow

Pytorch

Pycharm

Pytorch tutorial

Other programs

Teamviewer

KakaoTalk

Wheel speed

Useful command

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