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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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6
docs/en/yolov5/environments/aws_quickstart_tutorial.md
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@ -6,7 +6,7 @@ keywords: YOLOv5, AWS, Deep Learning, Machine Learning, AWS EC2, YOLOv5 setup, D
# YOLOv5 🚀 on AWS Deep Learning Instance: Your Complete Guide
Setting up a high-performance deep learning environment can be daunting for newcomers, but fear not! 🛠️ With this guide, we'll walk you through the process of getting YOLOv5 up and running on an AWS Deep Learning instance. By leveraging the power of Amazon Web Services (AWS), even those new to machine learning can get started quickly and cost-effectively. The AWS platform's scalability is perfect for both experimentation and production deployment.
Setting up a high-performance deep learning environment can be daunting for newcomers, but fear not! 🛠️ With this guide, we'll walk you through the process of getting YOLOv5 up and running on an AWS Deep Learning instance. By leveraging the power of Amazon Web Services (AWS), even those new to [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) can get started quickly and cost-effectively. The AWS platform's scalability is perfect for both experimentation and production deployment.
Other quickstart options for YOLOv5 include 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 our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>.
@ -24,7 +24,7 @@ In the EC2 dashboard, you'll find the **Launch Instance** button which is your g
### Selecting the Right Amazon Machine Image (AMI)
Here's where you choose the operating system and software stack for your instance. Type 'Deep Learning' into the search field and select the latest Ubuntu-based Deep Learning AMI, unless your needs dictate otherwise. Amazon's Deep Learning AMIs come pre-installed with popular frameworks and GPU drivers to streamline your setup process.
Here's where you choose the operating system and software stack for your instance. Type '[Deep Learning](https://www.ultralytics.com/glossary/deep-learning-dl)' into the search field and select the latest Ubuntu-based Deep Learning AMI, unless your needs dictate otherwise. Amazon's Deep Learning AMIs come pre-installed with popular frameworks and GPU drivers to streamline your setup process.
![Choose AMI](https://github.com/ultralytics/docs/releases/download/0/choose-ami.avif)
@ -92,4 +92,4 @@ sudo swapon /swapfile # activate swap file
free -h # verify swap memory
```
And that's it! 🎉 You've successfully created an AWS Deep Learning instance and run YOLOv5. Whether you're just starting with object detection or scaling up for production, this setup can help you achieve your machine learning goals. Happy training, validating, and deploying! If you encounter any hiccups along the way, the robust AWS documentation and the active Ultralytics community are here to support you.
And that's it! 🎉 You've successfully created an AWS Deep Learning instance and run YOLOv5. Whether you're just starting with [object detection](https://www.ultralytics.com/glossary/object-detection) or scaling up for production, this setup can help you achieve your machine learning goals. Happy training, validating, and deploying! If you encounter any hiccups along the way, the robust AWS documentation and the active Ultralytics community are here to support you.

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docs/en/yolov5/environments/azureml_quickstart_tutorial.md
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@ -61,7 +61,7 @@ Train the YOLOv5 model:
python train.py
```
Validate the model for Precision, Recall, and mAP
Validate the model for [Precision](https://www.ultralytics.com/glossary/precision), [Recall](https://www.ultralytics.com/glossary/recall), and mAP
```bash
python val.py --weights yolov5s.pt

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docs/en/yolov5/environments/docker_image_quickstart_tutorial.md
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@ -36,7 +36,7 @@ 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:
To run a container with access to local files (e.g., COCO [training data](https://www.ultralytics.com/glossary/training-data) in `/datasets`), use the `-v` flag:
```bash
sudo docker run --ipc=host -it -v "$(pwd)"/datasets:/usr/src/datasets ultralytics/yolov5:latest

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docs/en/yolov5/environments/google_cloud_quickstart_tutorial.md
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@ -6,13 +6,13 @@ keywords: YOLOv5, Google Cloud Platform, GCP, Deep Learning VM, object detection
# 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](https://www.ultralytics.com/glossary/artificial-intelligence-ai) 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/free-cloud-features#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
## Step 1: Create and Configure Your [Deep Learning](https://www.ultralytics.com/glossary/deep-learning-dl) VM
Let's begin by creating a virtual machine that's tuned for deep learning:
@ -42,7 +42,7 @@ cd yolov5
pip install -r requirements.txt
```
This setup process ensures you're working with a Python environment version 3.8.0 or newer and PyTorch 1.8 or above. Our scripts smoothly download [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) rending from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases), making it hassle-free to start model training.
This setup process ensures you're working with a Python environment version 3.8.0 or newer and [PyTorch](https://www.ultralytics.com/glossary/pytorch) 1.8 or above. Our scripts smoothly download [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) rending from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases), making it hassle-free to start model training.
## Step 3: Train and Deploy Your YOLOv5 Models 🌐
@ -62,7 +62,7 @@ python detect.py --weights yolov5s.pt --source path/to/images
python export.py --weights yolov5s.pt --include onnx coreml tflite
```
With just a few commands, YOLOv5 allows you to train custom object detection models tailored to your specific needs or utilize pre-trained weights for quick results on a variety of tasks.
With just a few commands, YOLOv5 allows you to train custom [object detection](https://www.ultralytics.com/glossary/object-detection) models tailored to your specific needs or utilize pre-trained weights for quick results on a variety of tasks.
![Terminal command image illustrating model training on a GCP Deep Learning VM](https://github.com/ultralytics/docs/releases/download/0/terminal-command-model-training.avif)
@ -80,7 +80,7 @@ free -h # confirm the memory increment
### Concluding Thoughts
Congratulations! You are now empowered to harness the capabilities of YOLOv5 with the computational prowess of Google Cloud Platform. This combination provides scalability, efficiency, and versatility for your object detection tasks. Whether for personal projects, academic research, or industrial applications, you have taken a pivotal step into the world of AI and machine learning on the cloud.
Congratulations! You are now empowered to harness the capabilities of YOLOv5 with the computational prowess of Google Cloud Platform. This combination provides scalability, efficiency, and versatility for your object detection tasks. Whether for personal projects, academic research, or industrial applications, you have taken a pivotal step into the world of AI and [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) on the cloud.
Do remember to document your journey, share insights with the Ultralytics community, and leverage the collaborative arenas such as [GitHub discussions](https://github.com/ultralytics/yolov5/discussions) to grow further. Now, go forth and innovate with YOLOv5 and GCP! 🌟