Skip to content

Get Started with YOLOv5 🚀 in Docker

This tutorial will guide you through the process of setting up and running YOLOv5 in a Docker container.

You can also explore other quickstart options for YOLOv5, such as our Colab Notebook Open In Colab Open In Kaggle, GCP Deep Learning VM, and Amazon AWS.


  1. Nvidia Driver: Version 455.23 or higher. Download from Nvidia's website.
  2. Nvidia-Docker: Allows Docker to interact with your local GPU. Installation instructions are available on the Nvidia-Docker GitHub repository.
  3. Docker Engine - CE: Version 19.03 or higher. Download and installation instructions can be found on the Docker website.

Step 1: Pull the YOLOv5 Docker Image

The Ultralytics YOLOv5 DockerHub repository is available at Docker Autobuild ensures that the ultralytics/yolov5:latest image is always in sync with the most recent repository commit. To pull the latest image, run the following command:

sudo docker pull ultralytics/yolov5:latest

Step 2: Run the Docker Container

Basic container:

Run an interactive instance of the YOLOv5 Docker image (called a "container") using the -it flag:

sudo docker run --ipc=host -it ultralytics/yolov5:latest

Container with local file access:

To run a container with access to local files (e.g., COCO training data in /datasets), use the -v flag:

sudo docker run --ipc=host -it -v "$(pwd)"/datasets:/usr/src/datasets ultralytics/yolov5:latest

Container with GPU access:

To run a container with GPU access, use the --gpus all flag:

sudo docker run --ipc=host -it --gpus all ultralytics/yolov5:latest

Step 3: Use YOLOv5 🚀 within the Docker Container

Now you can train, test, detect, and export YOLOv5 models within the running Docker container:

# Train a model on your data

# Validate the trained model for Precision, Recall, and mAP
python --weights

# Run inference using the trained model on your images or videos
python --weights --source path/to/images

# Export the trained model to other formats for deployment
python --weights --include onnx coreml tflite

GCP running Docker

Created 2023-11-12, Updated 2023-12-03
Authors: glenn-jocher (3)