kim4375731/clode

•Updated over 4 years ago

Term project of AI618 Group 7 "Conditional Latent ODEs for Motion Prediction in Autonomous Driving"

Image

206

kim4375731/clode repository overview

⁠Conditional Latent ODEs for Motion Prediction in Autonomous Driving (cLODE)

This image is for term project of AI618 2022 Group 7
"Conditional Latent ODEs for Motion Prediction in Autonomous Driving (cLODE)"
by Truong G. Khang, Yongjae Kim and Andrea Finazzi.

⁠Requirements

GPU that is compatible with PyTorch 1.8.0 + CUDA 11.2 (if not applicable, you should run the codes with CPU)
Docker environment
46.3GB free space (to pull the image on your docker workspace)

⁠Instruction to setup a docker environment (example)

Prerequisites
- Linux kernel >= 3.10 (GNU/Linux x86_64)
- Nvidia driver >= 418.81.07
- Docker >= 19.03
- Nvidia GPU Architecture >= Kepler
Repository setups

sudo apt-get update
sudo apt-get install \
    ca-certificates \
    curl \
    gnupg \
    lsb-release

Add Docker’s official GPG key

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg

Set the stable repository

echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

Install Docker Engine

sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io

Add docker group (to use docker command without sudo)

# Create the docker group.
sudo groupadd docker

# Add your user to the docker group.
sudo usermod -aG docker $USER
sudo usermod -aG docker {other user}

# reboot or type below for activate new group
newgrp docker

Docker test

docker run hello-world

Setup Nvidia Container Toolkit

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
   && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
   && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

Install nvidia-docker

sudo apt-get update
sudo apt-get install -y nvidia-docker2

Restart docker daemon

sudo systemctl restart docker

CUDA container test

sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

# Results should be like this:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.86       Driver Version: 470.86       CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA RTX A6000    Off  | 00000000:0B:00.0 Off |                  Off |
| 30%   40C    P8    24W / 300W |     17MiB / 48685MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA RTX A6000    Off  | 00000000:0C:00.0 Off |                  Off |
| 30%   36C    P8    20W / 300W |      5MiB / 48685MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

⁠How to run a docker container of 'cLODE'

Pull the latest image

docker pull kim4375731/clode:latest

Run a container
- Designate your own path to connect your host environment's path with the container's path: [host-path]:[container-path].
  
  ex. /home/yongjae/sharehouse:/root/sharehouse
- Designate your container name: [container-name].
  
  ex. clode_test
- Before running, please add docker as a xhost client with this CLI command: xhost +local:docker

docker run -it --privileged -v [host-path]:[container-path] -v /etc/localtime:/etc/localtime:ro -v /dev/input:/dev/input:ro --gpus all --net=host --env="DISPLAY" --env="QT_X11_NO_MITSHM=1" --env XAUTHORITY=$XAUTH --env=NVIDIA_DRIVER_CAPABILITIES=compute,graphics,utility --shm-size=8G --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" --name [container_name] kim4375731/clode:latest

An example command:

docker run -it --privileged -v /home/yongjae/sharehouse:/root/sharehouse -v /etc/localtime:/etc/localtime:ro -v /dev/input:/dev/input:ro --gpus all --net=host --env="DISPLAY" --env="QT_X11_NO_MITSHM=1" --env XAUTHORITY=$XAUTH --env=NVIDIA_DRIVER_CAPABILITIES=compute,graphics,utility --shm-size=8G --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" --name clode_test kim4375731/clode:latest

With the command above, you will start a container with the name of clode_test of the docker image kim4375731/clode:latest

Please try to run /root/cLODE/notebooks/data_analysis.ipynb in the container. This helps you reproduce the records and figures in our project report.
To train and test cLODE model, please refer our project repository: https://github.com/TruongKhang/cLODE⁠

⁠Frequently used docker commands

To stop the container, at host CLI:

docker stop [container-name]

To confirm the list of running containers:

docker ps

To confirm the full list of containers including stopped ones:

docker ps -a

To start a stopped container:

docker start -i [container-name]

To open a new CLI session of a container while running the container:

docker exec -it [container-name] bash

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Size

20.8 GB

Last updated

over 4 years ago

docker pull kim4375731/clode