72 lines
3.3 KiB
Markdown
72 lines
3.3 KiB
Markdown
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---
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comments: true
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description: 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.
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keywords: YOLOv5, Docker, Ultralytics, setup, guide, tutorial, machine learning, deep learning, AI, GPU, NVIDIA, container
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---
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# Get Started with YOLOv5 🚀 in Docker
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This tutorial will guide you through the process of setting up and running YOLOv5 in a Docker container.
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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](./google_cloud_quickstart_tutorial.md), and [Amazon AWS](./aws_quickstart_tutorial.md).
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## Prerequisites
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1. **NVIDIA Driver**: Version 455.23 or higher. Download from [Nvidia's website](https://www.nvidia.com/Download/index.aspx).
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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).
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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/).
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## Step 1: Pull the YOLOv5 Docker Image
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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:
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```bash
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sudo docker pull ultralytics/yolov5:latest
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```
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## Step 2: Run the Docker Container
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### Basic container:
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Run an interactive instance of the YOLOv5 Docker image (called a "container") using the `-it` flag:
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```bash
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sudo docker run --ipc=host -it ultralytics/yolov5:latest
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```
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### Container with local file access:
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To run a container with access to local files (e.g., COCO training data in `/datasets`), use the `-v` flag:
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```bash
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sudo docker run --ipc=host -it -v "$(pwd)"/datasets:/usr/src/datasets ultralytics/yolov5:latest
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```
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### Container with GPU access:
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To run a container with GPU access, use the `--gpus all` flag:
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```bash
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sudo docker run --ipc=host -it --gpus all ultralytics/yolov5:latest
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```
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## Step 3: Use YOLOv5 🚀 within the Docker Container
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Now you can train, test, detect, and export YOLOv5 models within the running Docker container:
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```bash
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# Train a model on your data
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python train.py
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# Validate the trained model for Precision, Recall, and mAP
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python val.py --weights yolov5s.pt
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# Run inference using the trained model on your images or videos
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python detect.py --weights yolov5s.pt --source path/to/images
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# Export the trained model to other formats for deployment
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python export.py --weights yolov5s.pt --include onnx coreml tflite
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```
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<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>
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