AI Deploy - Getting started

Last updated 3rd November, 2022.

Objective

OVHcloud provides a set of managed AI tools designed for building your machine learning projects.

This guide explains how to get started with OVHcloud AI Deploy by deploying your first application through the Control Panel.

Requirements

• access to the OVHcloud Control Panel
• a Public Cloud project in your OVHcloud account

Instructions

Subscribe to AI Deploy

Log in to your OVHcloud Control Panel and switch to Public Cloud in the top navigation bar. After selecting your Public Cloud project, click on AI Deploy in the left-hand navigation bar under AI & Machine Learning.

Click on the Deploy an AI Deploy app button and accept terms and conditions if any.

Once clicked, you will be redirected to the creation process detailed below.

Step 1: Select the location

Select where your AI Deploy app will be hosted, meaning the physical location.

Step 2: Select the application to deploy

AI Deploy allows a user to deploy applications from two sources:

• From an OVHcloud catalog with already built-in AI models and applications.
• From your own Docker container, giving you the full flexibility to deploy what you want.

In this tutorial, we will select an OVHcloud Docker image to deploy your first AI Deploy app.

Enter the name of the image: ovhcom/ai-deploy-hello-world

The objective is to deploy and call a simple Flask API for a kind of Hello World. The API welcomes you on your first AI Deploy app by sending back Hello followed by the name you sent and the end of the sentence.

Step 3: Assign compute resources

An AI Deploy app needs compute resources to be deployed, from a minimum of one instance to a maximum of ten. Each instance will be billed based on the amount of running time, whether or not you use your AI Deploy app.

Each node is composed of:

• CPU or GPU resources
• Local storage
• Network bandwidth

You can modify the Resource Size to adjust the amount of CPU or GPU cores, RAM and Network Bandwidth. Then you can modify the Number of instances on which your AI Deploy apps will be deployed.

To benefit from High Availability, a minimum of 2 instances are required.

For our first deployment, we will select two instances.

Step 4: Configure your AI Deploy app

First, choose a name for your AI Deploy app or keep the automatically generated name.

Then, you have the option to add some Key/Value labels to filter or organize your AI Deploy app access.

As an example, add a label with Key=owner and Value=elea.

The last parameter to select is the Access rule. Either you restrict your AI Deploy app access to selected tokens, or you allow any access. Use this option carefully; usually a Public access is used for a test, but not in production since everyone will be able to use it.

We will select Restricted Access for this deployment.

Step 5: Review and launch your AI Deploy app

This final step is a summary about your AI Deploy app deployment. You can review the previously selected options and parameters. Launch your AI Deploy app by clicking on Deploy an AI Deploy app

Congratulations, your first AI Deploy app is now running on production!

Connect to your first AI Deploy app

AI Deploy can be your own Docker container or applications proposed by the OVHcloud ecosystem.

In this tutorial, we deployed a Flask API allowing us to return. There is no web interface. What is given is an API endpoint that you can reach via HTTP.

Step 1: Check your AI Deploy app status

First, go check your app details and verify that your AI Deploy app has a running status. Please also verify that your labels are clearly mentioned.

In this example, we added the label owner=elea and two labels were automatically added by OVHcloud.

Step 2: Generate a security token

During the AI Deploy apps deployment process, we selected "restricted access". To query your app, you first need a valid security token.

In your OVHcloud Control Panel, go to your AI Deploy list. Select the Token tab.

Click on the New token button then fill in a name, a label selector, a role and region as below:

A few explanations:

• Label selector: you can restrict the token granted by labels. You can note a specific id, a type, or any previously created label such as owner=elea in our case.
• Role: AI Platform Operator can read and manage your AI Deploy app. AI Platform Read only can only read your AI Deploy app.
• Region: tokens are regionalized. Select the region related to your AI Deploy app.

Generate your first curl query

Now that your AI Deploy app is running and token generated, you are ready for your first query.

You can find the cURL example in the bottom part of your AI Deploy app, with standard parameters and JSON output format.

Since we are on restricted access, you need to specify the authentication token in the header following this format:

-H "Authorization: Bearer $YOURTOKENHERE"

In our case, the exact cURL code is:

 curl --request POST
    --url https://9b5b651e-8514-43d0-ae68-af801771542f.app.gra.training.ai.cloud.ovh.net
    -H "Authorization: Bearer WixOC/dmSoK3d0YSd20UvLMzbSVxMTMosnz6FcBQOQDlqxAsR5BezCVKtYfu18e"
    --header 'Content-Type: application/json'
    --data ' "Elea" '

Giving us

 "Hello Elea. Congratulations, you have launched your first AI App!"

If you see this message with the name you provided, you have successfully launched your first app!

Generate your first Python query

If you want to query this API with Python, this code sample with Python Request library may suit you:

import requests
import json
from requests.structures import CaseInsensitiveDict

url = "https://9b5b651e-8514-43d0-ae68-af801771542f.app.gra.training.ai.cloud.ovh.net"

headers = CaseInsensitiveDict()
headers = {'content-type': 'application/json',
           'Accept-Charset': 'UTF-8',
           'Authorization': 'Bearer WixOC/dmSoK3d0YSd20UvLMzbSVxMTMosnz6FcBQOQDlqxAsR5BezCVKtYfu18eC'}

data = "Elea"
j_data = json.dumps(data)

r = requests.post(url, data = j_data, headers = headers)

print(r.status_code)
print(r.text)

Result:

200
 "Hello Elea. Congratulations, you have launched your first AI App!"

That's it!

Stop and delete your AI Deploy app

You can keep your AI Deploy app running as long as you want. At any time, you may stop your application in your AI Deploy app details:

Once stopped, your AI Deploy app will free up the previously allocated compute resources. Your endpoint is kept and if you restart your AI Deploy app, the same endpoint can be reused seamlessly. Also, when you stop your app, you don't book compute resources anymore which means you don't have expenses for this part. Only expenses for attached storage may occur.

If you want to completely delete your AI Deploy app, just click on the delete action. Be sure to also delete your Object Storage data if you don't need it anymore.

Go further

• You can imagine deploying an AI model for sketch recognition thanks to AI Deploy. Refer to this tutorial.
• Do you want to use Streamlit in order to create an app? Here it is.

Feedback

Please feel free to send us your questions, feedback and suggestions to help our team improve the service on the OVHcloud Discord server!

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