Use Korean in Ubuntu 16.04
sudo apt-get install fonts-nanum*
sudo apt-get install nabi
sudo apt-get install im-config
im-config
Change 'im-config' setting to Hangul and reboot!
sudo apt-get install fonts-nanum*
sudo apt-get install nabi
sudo apt-get install im-config
im-config
sudo service lightdm stop
chmod +x ./NVIDIA-Linux-x86_64-410.93
sudo ./NVIDIA-Linux-x86_64-410.93
sudo reboot
# Uninstall Old Version
sudo apt-get purge cuda
sudo apt-get purge libcudnn6
sudo apt-get purge libcudnn6-dev
# Install CUDA toolkit 9.0 and cuDNN 7.0
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl2_2.1.4-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl-dev_2.1.4-1+cuda9.0_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo dpkg -i libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libnccl2_2.1.4-1+cuda9.0_amd64.deb
sudo dpkg -i libnccl-dev_2.1.4-1+cuda9.0_amd64.deb
sudo apt-get update
sudo apt-get install cuda=9.0.176-1
sudo apt-get install libcudnn7-dev
sudo apt-get install libnccl-dev
gedit .bashrc
export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
source .bashrc
reboot
jihyo@jihyo-desktop:~$ nvidia-smi
Wed Jan 30 15:19:03 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.48 Driver Version: 410.48 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2070 Off | 00000000:01:00.0 On | N/A |
| 0% 49C P8 25W / 215W | 737MiB / 7944MiB | 2% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1021 G /usr/lib/xorg/Xorg 550MiB |
| 0 1581 G compiz 125MiB |
| 0 1980 G ...uest-channel-token=15398769083618213155 59MiB |
+-----------------------------------------------------------------------------+
jihyo@jihyo-desktop:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Tue_Aug_11_14:27:32_CDT_2015
Cuda compilation tools, release 7.5, V7.5.17
wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
sudo dpkg -i google-chrome-stable_current_amd64.deb
# Kalman Filter 1Ddef correction(mean, var, sensor_mean, sensor_var): new_mean = (sensor_mean*var + mean*sensor_var)/(var + sensor_var) new_var = var*sensor_var/(var + sensor_var) return new_mean, new_var def prediction(mean, var, motion_mean, motion_var): new_mean = mean + motion_mean new_var = var + motion_var return new_mean, new_var
sensor = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] sensor_var = 2 motion = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] motion_var = 1 mean = 0var = 100
for i in range(len(sensor)): # Step 1: State Prediction mean, var = prediction(mean, var, motion[i], motion_var) print i print "- prediction -" print "mean:%d var:%d" % (mean, var) # Step 2 : Correction mean, var = correction(mean, var, sensor[i], sensor_var) print "- correction -" print "mean:%d var:%d" % (mean, var) print '-------------------------------------------'
new blog https://jihyo-jeon.github.io/