deeplearning_tutorial

Syllabus

1. Machine Learning Basic
2. Neural Network & Training Techniques
3. Convlutional Neural Network
4. Transfer Learnig (CNN)
5. Recurrent Neural Network
6. Reinforcement Learning (DQN)

Docker Setup

Docker on Ubuntu with CPU

docker run --name pytorch --rm \
    -v "/tmp/.X11-unix:/tmp/.X11-unix:rw" \
    -v "$HOME/.Xauthority:/root/.Xauthority:rw" \
    -e QT_X11_NO_MITSHM=1 \
    -e DISPLAY=$DISPLAY \
    -p 8888:8888 \
    --net=host \
    --mount 'type=bind,src=/home/ghryou/Workspace/deeplearning_tutorial,dst=/app' \
    -it ghryou/pytorch:cpu bash

jupyter notebook --allow-root

Docker on Ubuntu with GPU

docker run --name pytorch --rm \
   --runtime=nvidia \
   -v "/tmp/.X11-unix:/tmp/.X11-unix:rw" \
   -v "$HOME/.Xauthority:/root/.Xauthority:rw" \
   -e QT_X11_NO_MITSHM=1 \
   -e DISPLAY=$DISPLAY \
   --net=host \
   --mount 'type=bind,src=/home/naverlabs/Workspace/deeplearning_tutorial,dst=/app' \
   -it ghryou/pytorch:gpu bash​

Docker on Windows

1. setup shared memory at docker settings Right click docker icon -> Settings -> Shared Drives -> Check C Drive -> Apply -> Type password -> Restart docker

2. Install Xming X Server 3. Follow this link to setup Xming X Server

4. Run Xming 5. Open windows 10 powershell & Run this code

docker run --name pytorch --rm `
-p 8888:8888 `
-p 6006:6006 `
-e DISPLAY:<IP_Address>:0 `
--mount type=bind,src=C:\USERS\naverlabs\Desktop\deeplearning_tutorial,dst=/app `
-it ghryou/pytorch:cpu bash​

Docker on Mac

1. Install homebrew
2. brew install xquartx
3. Follow this link for X11 security Setup
4. Run this code

open -a XQuartz
./docker/run.mac.sh ghryou/pytorch:cpu bash

Ubuntu Commands

sudo chown <User name> -R <directory path>

Docker Cheat Sheet

docker build -t <tag name> -f <Dockerfile path> <Dockerfile directory>
docker exec -it pytorch bash
docker images
docker ps -al
docker stop <image id>
docker rmi <image id>
docker system prune

Push images to Docker Cloud

Jetson Setup

1. Download JetPack from Nvidia website
2. Makes run files executable with sudo chmod +x JetPack-L4T-3.3-linux-x64_b39.run
3. Run ./JetPack-L4T-3.3-linux-x64_b39.run
4. Follow the Commands of JetPack Installer (Install every package)
5. Select Device accesses Internet via Router/Switch
6. Connect jetson with host PC and follow the instructions
7. Connect display to jetson and setup internet connection on jetson
8. Check IP address of jetson with ifconfig
9. Run ./JetPack-L4T-3.3-linux-x64_b39.run again
10. Disable Flash OS Image to Target
11. Select Device accesses Internet via Router/Switch
12. Enter jetson's IP address and follow the instructions

Jetson Pytorch Setup

0. Operate fan

sudo /home/nvidia/jetson_clocks.sh

1. Insstall pytorch(will take 2hour)

 git clone --recursive https://github.com/pytorch/pytorch
 cd pytorch
 python3 setup.py install

never mind denied error, it will work on next step

2. build pytorch

python3 setup.py build_deps
sudo python3 setup.py develop

3. Verify CUDA and pytorch(from python3 interactive terminal)

python3
import torch
print(torch.__version__)
print(torch.cuda.is_available())

result :  True

Reference: https://gist.github.com/dusty-nv/ef2b372301c00c0a9d3203e42fd83426

4. Install opencv(it will take 1.5hour)

git clone https://github.com/jetsonhacks/buildOpenCVTX2
cd buildOpenCVTX2/
 ./buildOpenCV.sh -s

5. Verify opencv

python3
import cv2
cv2.__version__

result :'3.4.1-dev'

6. Install ROS

   follow webpage : http://wiki.ros.org/kinetic/Installation/Ubuntu

sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
sudo apt-key adv --keyserver hkp://ha.pool.sks-keyservers.net:80 --recv-key 421C365BD9FF1F717815A3895523BAEEB01FA116 
sudo apt-get update
sudo apt-get install ros-kinetic-desktop
sudo rosdep init
rosdep update
echo "source /opt/ros/kinetic/setup.bash" >> ~/.bashrc

7. Verify ROS

roscore