HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Add Chinese Modes and Tasks Docs (#6274)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2023-11-12 02:49:37 +01:00 • committed by GitHub
parent 795b95bdcb
commit e3a538bbde
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
293 changed files with 3681 additions and 736 deletions
Download patch file Download diff file Expand all files Collapse all files

88
docs/en/yolov5/environments/aws_quickstart_tutorial.md Normal file
View file

@ -0,0 +1,88 @@
---
comments: true
description: Step-by-step guide to run YOLOv5 on AWS Deep Learning instance. Learn how to create an instance, connect to it and train, validate and deploy models.
keywords: AWS, YOLOv5, instance, deep learning, Ultralytics, guide, training, deployment, object detection
---
# YOLOv5 🚀 on AWS Deep Learning Instance: A Comprehensive Guide
This guide will help new users run YOLOv5 on an Amazon Web Services (AWS) Deep Learning instance. AWS offers a [Free Tier](https://aws.amazon.com/free/) and a [credit program](https://aws.amazon.com/activate/) for a quick and affordable start.
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](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial), 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>. *Updated: 21 April 2023*.
## 1. AWS Console Sign-in
Create an account or sign in to the AWS console at [https://aws.amazon.com/console/](https://aws.amazon.com/console/) and select the **EC2** service.
![Console](https://user-images.githubusercontent.com/26833433/106323804-debddd00-622c-11eb-997f-b8217dc0e975.png)
## 2. Launch Instance
In the EC2 section of the AWS console, click the **Launch instance** button.
![Launch](https://user-images.githubusercontent.com/26833433/106323950-204e8800-622d-11eb-915d-5c90406973ea.png)
### Choose an Amazon Machine Image (AMI)
Enter 'Deep Learning' in the search field and select the most recent Ubuntu Deep Learning AMI (recommended), or an alternative Deep Learning AMI. For more information on selecting an AMI, see [Choosing Your DLAMI](https://docs.aws.amazon.com/dlami/latest/devguide/options.html).
![Choose AMI](https://user-images.githubusercontent.com/26833433/106326107-c9e34880-6230-11eb-97c9-3b5fc2f4e2ff.png)
### Select an Instance Type
A GPU instance is recommended for most deep learning purposes. Training new models will be faster on a GPU instance than a CPU instance. Multi-GPU instances or distributed training across multiple instances with GPUs can offer sub-linear scaling. To set up distributed training, see [Distributed Training](https://docs.aws.amazon.com/dlami/latest/devguide/distributed-training.html).
**Note:** The size of your model should be a factor in selecting an instance. If your model exceeds an instance's available RAM, select a different instance type with enough memory for your application.
Refer to [EC2 Instance Types](https://aws.amazon.com/ec2/instance-types/) and choose Accelerated Computing to see the different GPU instance options.
![Choose Type](https://user-images.githubusercontent.com/26833433/106324624-52141e80-622e-11eb-9662-1a376d9c887d.png)
For more information on GPU monitoring and optimization, see [GPU Monitoring and Optimization](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-gpu.html). For pricing, see [On-Demand Pricing](https://aws.amazon.com/ec2/pricing/on-demand/) and [Spot Pricing](https://aws.amazon.com/ec2/spot/pricing/).
### Configure Instance Details
Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 70% discount compared to On-Demand prices. We recommend a persistent spot instance, which will save your data and restart automatically when spot instance availability returns after spot instance termination. For full-price On-Demand instances, leave these settings at their default values.
![Spot Request](https://user-images.githubusercontent.com/26833433/106324835-ac14e400-622e-11eb-8853-df5ec9b16dfc.png)
Complete Steps 4-7 to finalize your instance hardware and security settings, and then launch the instance.
## 3. Connect to Instance
Select the checkbox next to your running instance, and then click Connect. Copy and paste the SSH terminal command into a terminal of your choice to connect to your instance.
![Connect](https://user-images.githubusercontent.com/26833433/106325530-cf8c5e80-622f-11eb-9f64-5b313a9d57a1.png)
## 4. Run YOLOv5
Once you have logged in to your instance, clone the repository and install the dependencies in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
Then, start training, testing, detecting, and exporting YOLOv5 models:
```bash
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
```
## Optional Extras
Add 64GB of swap memory (to `--cache` large datasets):
```bash
sudo fallocate -l 64G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
free -h # check memory
```
Now you have successfully set up and run YOLOv5 on an AWS Deep Learning instance. Enjoy training, testing, and deploying your object detection models!

95
docs/en/yolov5/environments/azureml_quickstart_tutorial.md Normal file
View file

@ -0,0 +1,95 @@
---
comments: true
description: Azure Machine Learning YOLOv5 quickstart
keywords: Ultralytics, YOLO, Deep Learning, Object detection, quickstart, Azure, AzureML
---
# YOLOv5 🚀 on AzureML
This guide provides a quickstart to use YOLOv5 from an AzureML compute instance.
Note that this guide is a quickstart for quick trials. If you want to unlock the full power AzureML, you can find the documentation to:
- [Create a data asset](https://learn.microsoft.com/azure/machine-learning/how-to-create-data-assets)
- [Create an AzureML job](https://learn.microsoft.com/azure/machine-learning/how-to-train-model)
- [Register a model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models)
## Prerequisites
You need an [AzureML workspace](https://learn.microsoft.com/azure/machine-learning/concept-workspace?view=azureml-api-2).
## Create a compute instance
From your AzureML workspace, select Compute > Compute instances > New, select the instance with the resources you need.
<img width="1741" alt="create-compute-arrow" src="https://github.com/ouphi/ultralytics/assets/17216799/3e92fcc0-a08e-41a4-af81-d289cfe3b8f2">
## Open a Terminal
Now from the Notebooks view, open a Terminal and select your compute.
![open-terminal-arrow](https://github.com/ouphi/ultralytics/assets/17216799/c4697143-7234-4a04-89ea-9084ed9c6312)
## Setup and run YOLOv5
Now you can, create a virtual environment:
```bash
conda create --name yolov5env -y
conda activate yolov5env
conda install pip -y
```
Clone YOLOv5 repository with its submodules:
```bash
git clone https://github.com/ultralytics/yolov5
cd yolov5
git submodule update --init --recursive # Note that you might have a message asking you to add your folder as a safe.directory just copy the recommended command
```
Install the required dependencies:
```bash
pip install -r yolov5/requirements.txt
pip install onnx>=1.10.0
```
Train the YOLOv5 model:
```bash
python train.py
```
Validate the model for Precision, Recall, and mAP
```bash
python val.py --weights yolov5s.pt
```
Run inference on images and videos:
```bash
python detect.py --weights yolov5s.pt --source path/to/images
```
Export models to other formats:
```bash
python detect.py --weights yolov5s.pt --source path/to/images
```
## Notes on using a notebook
Note that if you want to run these commands from a Notebook, you need to [create a new Kernel](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-terminal?view=azureml-api-2#add-new-kernels)
and select your new Kernel on the top of your Notebook.
If you create Python cells it will automatically use your custom environment, but if you add bash cells, you will need to run `source activate <your-env>` on each of these cells to make sure it uses your custom environment.
For example:
```bash
%%bash
source activate newenv
python val.py --weights yolov5s.pt
```

64
docs/en/yolov5/environments/docker_image_quickstart_tutorial.md Normal file
View file

@ -0,0 +1,64 @@
---
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). *Updated: 21 April 2023*.
## 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
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
```
<p align="center"><img width="1000" src="https://user-images.githubusercontent.com/26833433/142224770-6e57caaf-ac01-4719-987f-c37d1b6f401f.png"></p>

49
docs/en/yolov5/environments/google_cloud_quickstart_tutorial.md Normal file
View file

@ -0,0 +1,49 @@
---
comments: true
description: Step-by-step tutorial on how to set up and run YOLOv5 on Google Cloud Platform Deep Learning VM. Perfect guide for beginners and GCP new users!.
keywords: YOLOv5, Google Cloud Platform, GCP, Deep Learning VM, Ultralytics
---
# 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](https://cloud.google.com/free/docs/gcp-free-tier#free-trial).
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>, [Amazon AWS](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial) 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>. *Updated: 21 April 2023*.
**Last Updated**: 6 May 2022
## Step 1: Create a Deep Learning VM
1. Go to the [GCP marketplace](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning) 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](https://docs.anaconda.com/anaconda/packages/pkg-docs/) Python environment includes all dependencies.
<img width="1000" alt="GCP Marketplace" src="https://user-images.githubusercontent.com/26833433/105811495-95863880-5f61-11eb-841d-c2f2a5aa0ffe.png">
## Step 2: Set Up the VM
Clone the YOLOv5 repository and install the [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) will be downloaded automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```bash
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:
```bash
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
```
<img width="1000" alt="GCP terminal" src="https://user-images.githubusercontent.com/26833433/142223900-275e5c9e-e2b5-43f7-a21c-35c4ca7de87c.png">