Install and run Docker containers for the Anomaly Detector API

Containers enable you to use the Anomaly Detector API your own environment. Containers are great for specific security and data governance requirements. In this article you'll learn how to download, install, and run an Anomaly Detector container.

Anomaly Detector offers a single Docker container for using the API on-premises. Use the container to:

• Use the Anomaly Detector's algorithms on your data
• Monitor streaming data, and detect anomalies as they occur in real-time.
• Detect anomalies throughout your data set as a batch.
• Detect trend change points in your data set as a batch.
• Adjust the anomaly detection algorithm's sensitivity to better fit your data.

For detailed information about the API, please see:

• Learn more about Anomaly Detector API service

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

Prerequisites

You must meet the following prerequisites before using Anomaly Detector containers:

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.

The host computer

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.

Container requirements and recommendations

The following table describes the minimum and recommended CPU cores and memory to allocate for Anomaly Detector container.

Each core must be at least 2.6 gigahertz (GHz) or faster.

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 Anomaly Detector container image can be found on the mcr.microsoft.com container registry syndicate. It resides within the azure-cognitive-services/decision repository and is named anomaly-detector. The fully qualified container image name is mcr.microsoft.com/azure-cognitive-services/decision/anomaly-detector.

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

Use the docker pull command to download a container image.

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

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

Docker pull for the Anomaly Detector container

docker pull mcr.microsoft.com/azure-cognitive-services/anomaly-detector: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 the container. Refer to gather required parameters for details on how to get the {ENDPOINT_URI} and {API_KEY} values.

Examples of the docker run command are available.

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

This command:

• Runs an Anomaly Detector 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.

Running 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 port. For example, run the first container on port 5000 and the second container on port 5001.

Replace the <container-registry> and <container-name> with the values of the containers you use. These do not have to be the same container. You can have the Anomaly Detector container and the LUIS container running on the HOST together or you can have multiple Anomaly Detector containers running.

Run the first container on host port 5000.

docker run --rm -it -p 5000:5000 --memory 4g --cpus 1 \
<container-registry>/microsoft/<container-name> \
Eula=accept \
Billing={ENDPOINT_URI} \
ApiKey={API_KEY}

Run the second container on host port 5001.

docker run --rm -it -p 5001:5000 --memory 4g --cpus 1 \
<container-registry>/microsoft/<container-name> \
Eula=accept \
Billing={ENDPOINT_URI} \
ApiKey={API_KEY}

Each subsequent container should be on a different port.

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.

If you're having trouble running an Azure AI services container, you can try using the Microsoft diagnostics container. Use this container to diagnose common errors in your deployment environment that might prevent Azure AI containers from functioning as expected.

To get the container, use the following docker pull command:

docker pull mcr.microsoft.com/azure-cognitive-services/diagnostic

Then run the container. Replace {ENDPOINT_URI} with your endpoint, and replace {API_KEY} with your key to your resource:

docker run --rm mcr.microsoft.com/azure-cognitive-services/diagnostic \
eula=accept \
Billing={ENDPOINT_URI} \
ApiKey={API_KEY}

The container will test for network connectivity to the billing endpoint.

Billing

The Anomaly Detector containers send billing information to Azure, using an Anomaly Detector 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:

For more information about these options, see Configure containers.

Summary

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

• Anomaly Detector provides one Linux container for Docker, encapsulating anomaly detection with batch vs streaming, expected range inference, and sensitivity tuning.
• Container images are downloaded from a private Azure Container Registry dedicated for containers.
• Container images run in Docker.
• You can use either the REST API or SDK to call operations in Anomaly Detector containers by specifying the host URI of the container.
• You must specify billing information when instantiating a container.

Next steps

• Review Configure containers for configuration settings
• Deploy an Anomaly Detector container to Azure Container Instances
• Learn more about Anomaly Detector API service