ultralytics/docs/yolov5/environments/google_cloud_quickstart_tutorial.md

Run YOLOv5 🚀 on Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) ⭐

This tutorial will guide you through the process of setting up and running YOLOv5 on a GCP Deep Learning VM. New GCP users are eligible for a $300 free credit offer.

You can also explore other quickstart options for YOLOv5, such as our Colab Notebook , Amazon AWS and our Docker image at Docker Hub . Updated: 21 April 2023.

Last Updated: 6 May 2022

Step 1: Create a Deep Learning VM

1. Go to the GCP marketplace and select a Deep Learning VM.
2. Choose an n1-standard-8 instance (with 8 vCPUs and 30 GB memory).
3. Add a GPU of your choice.
4. Check 'Install NVIDIA GPU driver automatically on first startup?'
5. Select a 300 GB SSD Persistent Disk for sufficient I/O speed.
6. Click 'Deploy'.

The preinstalled Anaconda Python environment includes all dependencies.

Step 2: Set Up the VM

Clone the YOLOv5 repository and install the requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7. Models and datasets will be downloaded automatically from the latest YOLOv5 release.

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Step 3: Run YOLOv5 🚀 on the VM

You can now train, test, detect, and export YOLOv5 models on your VM:

python train.py  # train a model
python val.py --weights yolov5s.pt  # validate a model for Precision, Recall, and mAP
python detect.py --weights yolov5s.pt --source path/to/images  # run inference on images and videos
python export.py --weights yolov5s.pt --include onnx coreml tflite  # export models to other formats