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docs/en/yolov5/environments/docker_image_quickstart_tutorial.md

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+---
+comments: true
+description: Learn how to set up and run YOLOv5 in a Docker container. This tutorial includes the prerequisites and step-by-step instructions.
+keywords: YOLOv5, Docker, Ultralytics, Image Detection, YOLOv5 Docker Image, Docker Container, Machine Learning, AI
+---
+
+# 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](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>, [GCP Deep Learning VM](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial), and [Amazon AWS](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial).
+
+## Prerequisites
+
+1. **Nvidia Driver**: Version 455.23 or higher. Download from [Nvidia's website](https://www.nvidia.com/Download/index.aspx).
+2. **Nvidia-Docker**: Allows Docker to interact with your local GPU. Installation instructions are available on the [Nvidia-Docker GitHub repository](https://github.com/NVIDIA/nvidia-docker).
+3. **Docker Engine - CE**: Version 19.03 or higher. Download and installation instructions can be found on the [Docker website](https://docs.docker.com/install/).
+
+## Step 1: Pull the YOLOv5 Docker Image
+
+The Ultralytics YOLOv5 DockerHub repository is available at [https://hub.docker.com/r/ultralytics/yolov5](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:
+
+```bash
+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:
+
+```bash
+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:
+
+```bash
+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:
+
+```bash
+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:
+
+```bash
+# 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
+```
+
+<p align="center"><img width="1000" src="https://user-images.githubusercontent.com/26833433/142224770-6e57caaf-ac01-4719-987f-c37d1b6f401f.png" alt="GCP running Docker"></p>