Install and run Text Analytics containers

The Text Analytics containers provide advanced natural language processing over raw text, and includes three main functions: sentiment analysis, key phrase extraction, and language detection. Entity linking is not currently supported in a container.

If you don't have an Azure subscription, create a free account before you begin.

Prerequisites

In order to run any of the Text Analytics containers, you must have the host computer and container environments.

Preparation

You must meet the following prerequisites before using Text Analytics containers:

Required Purpose
Docker Engine You need the Docker Engine installed on a host computer. Docker provides packages that configure the Docker environment on macOS, Windows, and Linux. For a primer on Docker and container basics, see the Docker overview.

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.

Familiarity with Docker You should have a basic understanding of Docker concepts, like registries, repositories, containers, and container images, as well as knowledge of basic docker commands.
Cognitive Services resource In order to use the container, you must have:

A Cognitive Services Azure resource to get the associated billing key and billing endpoint URI. Both values are available on the Azure portal's Cognitive Services Overview and Keys pages and are required to start the container. You need to add the text/analytics/v2.0 routing to the endpoint URI as shown in the following BILLING_ENDPOINT_URI example.

{BILLING_KEY}: resource key

{BILLING_ENDPOINT_URI}: endpoint URI example is: https://westus.api.cognitive.microsoft.com/text/analytics/v2.1

The host computer

The host is a 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:

Container requirements and recommendations

The following table describes the minimum and recommended CPU cores, at least 2.6 gigahertz (GHz) or faster, and memory, in gigabytes (GB), to allocate for each Text Analytics container.

Container Minimum Recommended TPS
(Minimum, Maximum)
Key Phrase Extraction 1 core, 2 GB memory 1 core, 4 GB memory 15, 30
Language Detection 1 core, 2 GB memory 1 core, 4 GB memory 15, 30
Sentiment Analysis 1 core, 2 GB memory 1 core, 4 GB memory 15, 30
  • Each core must be at least 2.6 gigahertz (GHz) or faster.
  • TPS - transactions per second

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

Container images for Text Analytics are available from Microsoft Container Registry.

Container Repository
Key Phrase Extraction mcr.microsoft.com/azure-cognitive-services/keyphrase
Language Detection mcr.microsoft.com/azure-cognitive-services/language
Sentiment Analysis mcr.microsoft.com/azure-cognitive-services/sentiment

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

For a full description of available tags for the Text Analytics containers, see the following containers on the Docker Hub:

Use the docker pull command to download a container image.

Docker pull for the Key phrase extraction container

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

Docker pull for the language detection container

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

Docker pull for the sentiment container

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

Tip

You can use the docker images command to list your downloaded container images. For example, the following command lists the ID, repository, and tag of each downloaded container image, formatted as a table:

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

IMAGE ID            REPOSITORY              TAG
ebbee78a6baa       <container-name>         latest

How to use the container

Once the container is on the host computer, use the following process to work with the container.

  1. Run the container, with the required billing settings. More examples of the docker run command are available.
  2. Query the container's prediction endpoint.

Run the container with docker run

Use the docker run command to run any of the three containers. The command uses the following parameters:

Placeholder Value
{BILLING_KEY} This key is used to start the container, and is available on the Azure portal's Cognitive Services Keys page.
{BILLING_ENDPOINT_URI} The billing endpoint URI value is available on the Azure Cognitive Services Overview page.

Example:
Billing=https://westus.api.cognitive.microsoft.com/text/analytics/v2.0

You need to add the text/analytics/v2.0 routing to the endpoint URI as shown in the preceding BILLING_ENDPOINT_URI example.

Replace these parameters with your own values in the following example docker run command.

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

This command:

  • Runs a key phrase 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.

More examples of the docker run command are available.

Important

The Eula, Billing, and ApiKey options must be specified to run the container; otherwise, the container won't start. For more information, see Billing.

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 Cognitive Services container running on the HOST together. You also can have multiple containers of the same Cognitive Services container running.

Query the container's prediction endpoint

The container provides REST-based query prediction endpoint APIs.

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

Validate that a container is running

There are several ways to validate that the container is running.

Request Purpose
http://localhost:5000/ The container provides a home page.
http://localhost:5000/status Requested with GET, to validate that the container is running without causing an endpoint query. This request can be used for Kubernetes liveness and readiness probes.
http://localhost:5000/swagger The container provides a full set of documentation for the endpoints and a Try it now feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that's required.

Container's home page

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 Text Analytics containers send billing information to Azure, using a Cognitive Services resource on your Azure account.

Queries to the container are billed at the pricing tier of the Azure resource used for the <ApiKey>.

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

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 running.

Billing arguments

All three of the following options must be specified with valid values in order for the docker run command to start the container.

Option Description
ApiKey The API key of the Cognitive Services resource used to track billing information.
The value of this option must be set to an API key for the provisioned resource specified in Billing.
Billing The endpoint of the Cognitive Services resource used to track billing information.
The value of this option must be set to the endpoint URI of a provisioned Azure resource.
Eula Indicates that you accepted the license for the container.
The value of this option must be set to accept.

For more information about these options, see Configure containers.

Summary

In this article, you learned concepts and workflow for downloading, installing, and running Text Analytics containers. In summary:

  • Text Analytics provides three Linux containers for Docker, encapsulating key phrase extraction, language detection, and sentiment analysis.
  • Container images are downloaded from the Microsoft Container Registry (MCR) in Azure.
  • Container images run in Docker.
  • You can use either the REST API or SDK to call operations in Text Analytics containers by specifying the host URI of the container.
  • You must specify billing information when instantiating a container.

Important

Cognitive Services containers are not licensed to run without being connected to Azure for metering. Customers need to enable the containers to communicate billing information with the metering service at all times. Cognitive Services containers do not send customer data (e.g., the image or text that is being analyzed) to Microsoft.

Next steps