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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: RainRat <rainrat78@yahoo.ca>
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docs/en/yolov5/environments/google_cloud_quickstart_tutorial.md
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@ -6,15 +6,15 @@ keywords: YOLOv5, Google Cloud Platform, GCP, Deep Learning VM, ML model trainin
# Mastering YOLOv5 🚀 Deployment on Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) ⭐
Embarking on the journey of artificial intelligence and machine learning can be exhilarating, especially when you leverage the power and flexibility of a cloud platform. Google Cloud Platform (GCP) offers robust tools tailored for machine learning enthusiasts and professionals alike. One such tool is the Deep Learning VM that is preconfigured for data science and ML tasks. In this tutorial, we will navigate through the process of setting up YOLOv5 on a GCP Deep Learning VM. Whether you’re taking your first steps in ML or you’re a seasoned practitioner, this guide is designed to provide you with a clear pathway to implementing object detection models powered by YOLOv5.
Embarking on the journey of artificial intelligence and machine learning can be exhilarating, especially when you leverage the power and flexibility of a cloud platform. Google Cloud Platform (GCP) offers robust tools tailored for machine learning enthusiasts and professionals alike. One such tool is the Deep Learning VM that is preconfigured for data science and ML tasks. In this tutorial, we will navigate through the process of setting up YOLOv5 on a GCP Deep Learning VM. Whether you're taking your first steps in ML or you're a seasoned practitioner, this guide is designed to provide you with a clear pathway to implementing object detection models powered by YOLOv5.
🆓 Plus, if you're a fresh GCP user, you’re in luck with a [$300 free credit offer](https://cloud.google.com/free/docs/gcp-free-tier#free-trial) to kickstart your projects.
🆓 Plus, if you're a fresh GCP user, you're in luck with a [$300 free credit offer](https://cloud.google.com/free/docs/gcp-free-tier#free-trial) to kickstart your projects.
In addition to GCP, explore other accessible quickstart options for YOLOv5, like our [Colab Notebook](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"> for a browser-based experience, or the scalability of [Amazon AWS](./aws_quickstart_tutorial.md). Furthermore, container aficionados can utilize our official Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) <img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"> for an encapsulated environment.
## Step 1: Create and Configure Your Deep Learning VM
Let’s begin by creating a virtual machine that’s tuned for deep learning:
Let's begin by creating a virtual machine that's tuned for deep learning:
1. Head over to the [GCP marketplace](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning) and select the **Deep Learning VM**.
2. Opt for a **n1-standard-8** instance; it offers a balance of 8 vCPUs and 30 GB of memory, ideally suited for our needs.