Docker Quickstart

To get started using this repo quickly using our latest Docker Image follow the instructions below. Other quickstart options for this repo include our Jupyter Notebook and a GCP Deep Learning VM.

1. Install Docker and Nvidia-Docker

Docker images come with all dependencies preinstalled, however Docker itself requires installation, and relies of nvidia driver installations in order to interact properly with local GPU resources. The requirements are:

• Nvidia Driver >= 455.23 https://www.nvidia.com/Download/index.aspx
• Nvidia-Docker https://github.com/NVIDIA/nvidia-docker
• Docker Engine - CE >= 19.03 https://docs.docker.com/install/

2. Pull Image

The Ultralytics YOLOv3 DockerHub is https://hub.docker.com/r/ultralytics/yolov3 . Docker Autobuild is used to automatically build images from the latest repository commits, so the ultralytics/yolov3:latest image hosted on the DockerHub will always be in sync with the most recent repository commit. To pull this image:

sudo docker pull ultralytics/yolov3:latest

3. Run Container

Run an interactive instance of this image (called a "container") using -it:

sudo docker run --ipc=host -it ultralytics/yolov3:latest

Run a container with local file access (like COCO training data in /coco) using -v:

sudo docker run --ipc=host -it -v "$(pwd)"/coco:/usr/src/coco ultralytics/yolov3:latest

Run a container with GPU access using --gpus all:

sudo docker run --ipc=host --gpus all -it ultralytics/yolov3:latest

4. Run Commands

Once the container is launched and you are inside it, you will have a terminal window in which you can run all regular bash commands, such as:

• ls .
• ls ../coco
• python train.py
• python test.py
• python detect.py