Install and run Key Phrase Extraction containers

Containers enable you to host the Key Phrase Extraction API on your own infrastructure. If you have security or data governance requirements that can't be fulfilled by calling Key Phrase Extraction remotely, then containers might be a good option.

Containers enable you to run the Key Phrase Extraction APIs in your own environment and are great for your specific security and data governance requirements. The Key Phrase Extraction containers provide advanced natural language processing over raw text, and include three main functions: sentiment analysis, Key Phrase Extraction, and language detection.

Prerequisites

• If you don't have an Azure subscription, create a Trial.
• Docker installed on a host computer. Docker must be configured to allow the containers to connect with and send billing data to Azure.
  • On Windows, Docker must also be configured to support Linux containers.
  • You should have a basic understanding of Docker concepts.
• A Language resource with the free (F0) or standard (S) pricing tier.

Gather required parameters

Three primary parameters for all Azure AI containers are required. The Microsoft Software License Terms must be present with a value of accept. An Endpoint URI and API key are also needed.

Endpoint URI

The {ENDPOINT_URI} value is available on the Azure portal Overview page of the corresponding Azure AI services resource. Go to the Overview page, hover over the endpoint, and a Copy to clipboard icon appears. Copy and use the endpoint where needed.

Keys

The {API_KEY} value is used to start the container and is available on the Azure portal's Keys page of the corresponding Azure AI services resource. Go to the Keys page, and select the Copy to clipboard icon.

Host computer requirements and recommendations

The host is an x64-based computer that runs the Docker container. It can be a computer on your premises or a Docker hosting service in Azure, such as:

• Azure Kubernetes Service.
• Azure Container Instances.
• A Kubernetes cluster deployed to Azure Stack. For more information, see Deploy Kubernetes to Azure Stack.

The following table describes the minimum and recommended specifications for the available Key Phrase Extraction containers. Each CPU core must be at least 2.6 gigahertz (GHz) or faster. The allowable Transactions Per Second (TPS) are also listed.

CPU core and memory correspond to the --cpus and --memory settings, which are used as part of the docker run command.

Get the container image with docker pull

The key phrase extraction container image can be found on the mcr.microsoft.com container registry syndicate. It resides within the azure-cognitive-services/textanalytics/ repository and is named keyphrase. The fully qualified container image name is, mcr.microsoft.com/azure-cognitive-services/textanalytics/keyphrase.

To use the latest version of the container, you can use the latest tag. You can also find a full list of tags on the MCR.

Use the docker pull command to download a container image from Microsoft Container Registry.

docker pull mcr.microsoft.com/azure-cognitive-services/textanalytics/keyphrase:latest

docker images --format "table {{.ID}}\t{{.Repository}}\t{{.Tag}}"

IMAGE ID         REPOSITORY                TAG
<image-id>       <repository-path/name>    <tag-name>

Run the container with docker run

Once the container is on the host computer, use the docker run command to run the containers. The container will continue to run until you stop it.

To run the Key Phrase Extraction container, execute the following docker run command. Replace the placeholders below with your own values:

docker run --rm -it -p 5000:5000 --memory 4g --cpus 1 \
mcr.microsoft.com/azure-cognitive-services/textanalytics/keyphrase \
Eula=accept \
Billing={ENDPOINT_URI} \
ApiKey={API_KEY}

This command:

• Runs a Key Phrase Extraction container from the container image
• Allocates one CPU core and 4 gigabytes (GB) of memory
• Exposes TCP port 5000 and allocates a pseudo-TTY for the container
• Automatically removes the container after it exits. The container image is still available on the host computer.

Run multiple containers on the same host

If you intend to run multiple containers with exposed ports, make sure to run each container with a different exposed port. For example, run the first container on port 5000 and the second container on port 5001.

You can have this container and a different Azure AI services container running on the HOST together. You also can have multiple containers of the same Azure AI services container running.

Query the container's prediction endpoint

The container provides REST-based query prediction endpoint APIs.

Use the host, http://localhost:5000, for container APIs.

Validate that a container is running

There are several ways to validate that the container is running. Locate the External IP address and exposed port of the container in question, and open your favorite web browser. Use the various request URLs that follow to validate the container is running. The example request URLs listed here are http://localhost:5000, but your specific container might vary. Make sure to rely on your container's External IP address and exposed port.

Stop the container

To shut down the container, in the command-line environment where the container is running, select Ctrl+C.

Troubleshooting

If you run the container with an output mount and logging enabled, the container generates log files that are helpful to troubleshoot issues that happen while starting or running the container.

Billing

The Key Phrase Extraction containers send billing information to Azure, using a Key Phrase Extraction resource on your Azure account.

Queries to the container are billed at the pricing tier of the Azure resource that's used for the ApiKey parameter.

Azure AI services containers aren't licensed to run without being connected to the metering or billing endpoint. You must enable the containers to communicate billing information with the billing endpoint at all times. Azure AI services containers don't send customer data, such as the image or text that's being analyzed, to Azure.

Connect to Azure

The container needs the billing argument values to run. These values allow the container to connect to the billing endpoint. The container reports usage about every 10 to 15 minutes. If the container doesn't connect to Azure within the allowed time window, the container continues to run but doesn't serve queries until the billing endpoint is restored. The connection is attempted 10 times at the same time interval of 10 to 15 minutes. If it can't connect to the billing endpoint within the 10 tries, the container stops serving requests.

Billing arguments

The docker run command will start the container when all three of the following options are provided with valid values:

\

Summary

In this article, you learned concepts and workflow for downloading, installing, and running Key Phrase Extraction containers. In summary:

• Key Phrase Extraction provides Linux containers for Docker.
• Container images are downloaded from the Microsoft Container Registry (MCR).
• Container images run in Docker.
• You can use either the REST API or SDK to call operations in Key Phrase Extraction containers by specifying the host URI of the container.
• You must specify billing information when instantiating a container.

Next steps

• See Configure containers for configuration settings.