ultralytics/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md

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true	Learn how to set up and run YOLOv5 in a Docker container with step-by-step instructions. Explore other quickstart options for an easy setup.	YOLOv5, Docker, Ultralytics, setup, guide, tutorial, machine learning, deep learning, AI, GPU, NVIDIA, container

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 , GCP Deep Learning VM, and Amazon AWS.

Prerequisites

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 https://hub.docker.com/r/ultralytics/yolov5. 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
python train.py

# Validate the trained model for Precision, Recall, and mAP
python val.py --weights yolov5s.pt

# Run inference using the trained model on your images or videos
python detect.py --weights yolov5s.pt --source path/to/images

# Export the trained model to other formats for deployment
python export.py --weights yolov5s.pt --include onnx coreml tflite