Steps to build docker image

Docker

Docker version 19.03 will be required.

For running with GPU

NVIDIA Drivers

This drivers should be installed in the host system.

For Ubuntu:

$ sudo apt-get install linux-headers-$(uname -r) gcc g++ make
$ wget http://in.download.nvidia.com/tesla/418.67/NVIDIA-Linux-x86_64-418.67.run
$ chmod 777 NVIDIA-Linux-x86_64-418.67.run
$ bash NVIDIA-Linux-x86_64-418.67.run

For CentOS

$ sudo yum -y install kernel-devel-$(uname -r) kernel-header-$(uname -r) gcc make
$ wget http://in.download.nvidia.com/tesla/418.67/NVIDIA-Linux-x86_64-418.67.run
$ chmod 777 NVIDIA-Linux-x86_64-418.67.run
$ bash NVIDIA-Linux-x86_64-418.67.run

NVIDIA Container Toolkit

This toolkit should be installed in the host system.

For Ubuntu

$ 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

$ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
$ sudo systemctl restart docker

For CentOS

$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
$ curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo

$ sudo yum install -y nvidia-container-toolkit
$ sudo systemctl restart docker

Building docker image

• Go to TKDNN/ then run following command. $ docker build -t baggageai:server -f docker/Dockerfile .

  Note:

1. have to copy weights into a TKDNN/config/ (tkdnn converted weights) current support api (fp32X4 and fp16X1)
2. setup number of classes accrding to weights in TKDNN/config/config.yml
3. give path of this weights into handler.cpp line number 117-121.

Run docker image

$ docker run --gpus all -p 8080:8080 -d <image_id>

Now server will be started in the container. You can check server is running or not using docker ps

Run docker image

$ docker run -p 8080:8080 -d <image_id>

Using docker-compose file

Docker Compose

Install docker-compose version 1.24.1 https://docs.docker.com/compose/install/

Create Volume

Create a volume named BAI_logs using following command:

$ docker volume create BAI_logs

Change permission of the directory of docker volume, so that logs can be written to that directory.

$ cd /var/lib/docker/volumes/BAI_logs
$ chmod 757 _data/

## For running with GPU
Install nvidia-container-runtime:

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

Add nvidia runtime in ``/etc/docker/daemon.json ``

{ "runtimes": { "nvidia": { "path": "/usr/bin/nvidia-container-runtime", "runtimeArgs": [] } }, "default-runtime": "nvidia"

}

After editing changes restart the docker.
``systemctl restart docker ``

Go to ``BaggageAI-Darknet-API/baggageai/dist/server/with-gpu`` and run:

$ docker-compose up -d

## Running containers in stack
First of all initialize a swarm.

$ docker swarm init

You can add a worker node using ``docker swarm join`` command displayed on terminal. 


Check the statsus using

$ docker service ls $ docker stack ls

### With GPU
Go to ``TKDNN/docker/`` and run:

$ docker stack deploy -c docker-compose.yml

# Calling the API 

curl -X POST http://localhost:8080?name=<file_name> --data-binary "@<absolute_path_of_image>"

Example,

curl -X POST http://localhost:8080?name=S0240628297_20180812164749_L-4_3.jpg
--data-binary "@/home/ubuntu/BaggageAI/S0240628297_20180812164749_L-4_3.jpg"