Get Started with YOLOv5 🚀 in Docker

This tutorial will guide you through the process of setting up and running YOLOv5 in a Docker container, providing comprehensive instructions for both CPU and GPU environments.

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

Prerequisites

Docker: Install Docker from the official Docker website.
NVIDIA Driver (for GPU support): Version 455.23 or higher. Download from NVIDIA's website.
NVIDIA Docker Runtime (for GPU support): Allows Docker to interact with your local GPU. Follow the installation instructions below.

Setting up NVIDIA Docker Runtime

Verify that your NVIDIA drivers are properly installed:

nvidia-smi

Install the NVIDIA Docker runtime:

# Add NVIDIA package repositories
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
distribution=$(lsb_release -cs)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# Install NVIDIA Docker runtime
sudo apt-get update
sudo apt-get install -y nvidia-docker2

# Restart Docker service
sudo systemctl restart docker

Verify the NVIDIA runtime is available:

docker info | grep -i runtime

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.

# Set image name as a variable
t=ultralytics/yolov5:latest

# Pull the latest image
sudo docker pull $t

Step 2: Run the Docker Container

Using CPU Only

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

# Run without GPU
sudo docker run -it --ipc=host $t

Using GPU

# Run with all GPUs
sudo docker run -it --ipc=host --gpus all $t

# Run with specific GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t

Mounting Local Directories

To access files on your local machine within the container:

# Mount a local directory into the container
sudo docker run -it --ipc=host --gpus all -v /path/on/host:/path/in/container $t

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