Install and run Anomaly Detector containers

The Anomaly Detector has the following container:

Function Features
Anomaly detector
  • Detects anomalies as they occur in real-time.
  • Detects anomalies throughout your data set as a batch.
  • Infers the expected normal range of your data.
  • Supports anomaly detection sensitivity adjustment to better fit your data.
  • For detailed information about the APIs, please see:

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

    Prerequisites

    You must meet the following prerequisites before using Anomaly Detector 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.
    Anomaly Detector resource In order to use these containers, you must have:

    An Azure Anomaly Detector resource to get the associated API key and endpoint URI. Both values are available on the Azure portal's Anomaly Detector Overview and Keys pages and are required to start the container.

    {API_KEY}: One of the two available resource keys on the Keys page

    {ENDPOINT_URI}: The endpoint as provided on the Overview page

    Request access to the container registry

    You must first complete and submit the Anomaly Detector Container Request form to request access to the container.

    The form requests information about you, your company, and the user scenario for which you'll use the container. After you've submitted the form, the Azure Cognitive Services team reviews it to ensure that you meet the criteria for access to the private container registry.

    Important

    You must use an email address that's associated with either a Microsoft Account (MSA) or Azure Active Directory (Azure AD) account in the form.

    If your request is approved, you'll receive an email with instructions that describe how to obtain your credentials and access the private container registry.

    Use the Docker CLI to authenticate the private container registry

    You can authenticate with the private container registry for Cognitive Services Containers in any of several ways, but the recommended method from the command line is to use the Docker CLI.

    Use the docker login command, as shown in the following example, to log in to containerpreview.azurecr.io, the private container registry for Cognitive Services Containers. Replace <username> with the user name and <password> with the password that's provided in the credentials you received from the Azure Cognitive Services team.

    docker login containerpreview.azurecr.io -u <username> -p <password>
    

    If you've secured your credentials in a text file, you can concatenate the contents of that text file, by using the cat command, to the docker login command, as shown in the following example. Replace <passwordFile> with the path and name of the text file that contains the password and <username> with the user name that's provided in your credentials.

    cat <passwordFile> | docker login containerpreview.azurecr.io -u <username> --password-stdin
    

    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 and memory to allocate for Anomaly Detector container.

    QPS(Queries per second) Minimum Recommended
    10 QPS 4 core, 1-GB memory 8 core 2-GB memory
    20 QPS 8 core, 2-GB memory 16 core 4-GB memory

    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

    Use the docker pull command to download a container image.

    Container Repository
    cognitive-services-anomaly-detector containerpreview.azurecr.io/microsoft/cognitive-services-anomaly-detector: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
    <image-id>       <repository-path/name>    <tag-name>
    

    Docker pull for the Anomaly Detector container

    docker pull containerpreview.azurecr.io/microsoft/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 any of the three containers. The command uses the following parameters:

    Placeholder Value
    {API_KEY} This key is used to start the container, and is available on the Azure portal's Anomaly Detector Keys page.
    {ENDPOINT_URI} The billing endpoint URI value is available on the Azure portal's Anomaly Detector Overview page.

    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 \
    containerpreview.azurecr.io/microsoft/cognitive-services-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.

    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.

    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 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 port 5001.

    docker run --rm -it -p 5000:5001 --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, 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 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>.

    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

    For the docker run command to start the container, all three of the following options must be specified with valid values:

    Option Description
    ApiKey The API key of the Cognitive Services resource that's used to track billing information.
    The value of this option must be set to an API key for the provisioned resource that's specified in Billing.
    Billing The endpoint of the Cognitive Services resource that's 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.

    Blog posts

    Developer samples

    Developer samples are available at our GitHub repository.

    View webinar

    Join the webinar to learn about:

    • How to deploy Cognitive Services to any machine using Docker
    • How to deploy Cognitive Services to AKS

    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 preview.
    • 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.

    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 time series data that is being analyzed) to Microsoft.

    Next steps