How to deploy and inference a managed compute deployment with code
The AI Studio model catalog offers over 1,600 models, and the most common way to deploy these models is to use the managed compute deployment option, which is also sometimes referred to as a managed online deployment.
Deployment of a large language model (LLM) makes it available for use in a website, an application, or other production environment. Deployment typically involves hosting the model on a server or in the cloud and creating an API or other interface for users to interact with the model. You can invoke the deployment for real-time inference of generative AI applications such as chat and copilot.
In this article, you learn how to deploy models using the Azure Machine Learning SDK. The article also covers how to perform inference on the deployed model.
Get the model ID
You can deploy managed compute models using the Azure Machine Learning SDK, but first, let's browse the model catalog and get the model ID you need for deployment.
Sign in to AI Studio and go to the Home page.
Select Model catalog from the left sidebar.
In the Deployment options filter, select Managed compute.
Select a model.
Copy the model ID from the details page of the model you selected. It looks something like this:
azureml://registries/azureml/models/deepset-roberta-base-squad2/versions/16
Deploy the model
Let's deploy the model.
First, you need to install the Azure Machine Learning SDK.
pip install azure-ai-ml
pip install azure-identity
Use this code to authenticate with Azure Machine Learning and create a client object. Replace the placeholders with your subscription ID, resource group name, and AI Studio project name.
from azure.ai.ml import MLClient
from azure.identity import InteractiveBrowserCredential
client = MLClient(
credential=InteractiveBrowserCredential,
subscription_id="your subscription name goes here",
resource_group_name="your resource group name goes here",
workspace_name="your project name goes here",
)
For the managed compute deployment option, you need to create an endpoint before a model deployment. Think of an endpoint as a container that can house multiple model deployments. The endpoint names need to be unique in a region, so in this example we're using the timestamp to create a unique endpoint name.
import time, sys
from azure.ai.ml.entities import (
ManagedOnlineEndpoint,
ManagedOnlineDeployment,
ProbeSettings,
)
# Make the endpoint name unique
timestamp = int(time.time())
online_endpoint_name = "customize your endpoint name here" + str(timestamp)
# Create an online endpoint
endpoint = ManagedOnlineEndpoint(
name=online_endpoint_name,
auth_mode="key",
)
workspace_ml_client.begin_create_or_update(endpoint).wait()
Create a deployment. You can find the model ID in the model catalog.
model_name = "azureml://registries/azureml/models/deepset-roberta-base-squad2/versions/16"
demo_deployment = ManagedOnlineDeployment(
name="demo",
endpoint_name=online_endpoint_name,
model=model_name,
instance_type="Standard_DS3_v2",
instance_count=2,
liveness_probe=ProbeSettings(
failure_threshold=30,
success_threshold=1,
timeout=2,
period=10,
initial_delay=1000,
),
readiness_probe=ProbeSettings(
failure_threshold=10,
success_threshold=1,
timeout=10,
period=10,
initial_delay=1000,
),
)
workspace_ml_client.online_deployments.begin_create_or_update(demo_deployment).wait()
endpoint.traffic = {"demo": 100}
workspace_ml_client.begin_create_or_update(endpoint).result()
Inference the deployment
You need a sample json data to test inferencing. Create sample_score.json with the following example.
{
"inputs": {
"question": [
"Where do I live?",
"Where do I live?",
"What's my name?",
"Which name is also used to describe the Amazon rainforest in English?"
],
"context": [
"My name is Wolfgang and I live in Berlin",
"My name is Sarah and I live in London",
"My name is Clara and I live in Berkeley.",
"The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
]
}
}
Let's inference with sample_score.json. Change the location based on where you saved your sample json file.
scoring_file = "./sample_score.json"
response = workspace_ml_client.online_endpoints.invoke(
endpoint_name=online_endpoint_name,
deployment_name="demo",
request_file=scoring_file,
)
response_json = json.loads(response)
print(json.dumps(response_json, indent=2))
Delete the deployment endpoint
To delete deployments in AI Studio, select the Delete button on the top panel of the deployment details page.
Quota considerations
To deploy and perform inferencing with real-time endpoints, you consume Virtual Machine (VM) core quota that is assigned to your subscription on a per-region basis. When you sign up for AI Studio, you receive a default VM quota for several VM families available in the region. You can continue to create deployments until you reach your quota limit. Once that happens, you can request for a quota increase.
Next steps
- Learn more about what you can do in AI Studio
- Get answers to frequently asked questions in the Azure AI FAQ article