Install and run containers

This content applies to: v3.0 (GA) v3.1 (GA)

Azure AI Document Intelligence is an Azure AI service that lets you build automated data processing software using machine-learning technology. Document Intelligence enables you to identify and extract text, key/value pairs, selection marks, table data, and more from your documents. The results are delivered as structured data that ../includes the relationships in the original file. Containers process only the data provided to them and solely utilize the resources they are permitted to access. Containers cannot process data from other regions.

In this article you can learn how to download, install, and run Document Intelligence containers. Containers enable you to run the Document Intelligence service in your own environment. Containers are great for specific security and data governance requirements.

• Read, Layout, ID Document, Receipt, and Invoice models are supported by Document Intelligence v3.1 containers.

• Read, Layout, General Document, Business Card, and Custom models are supported by Document Intelligence v3.0 containers.

Version support

Support for containers is currently available with Document Intelligence version v3.0: 2022-08-31 (GA) for all models and v3.1 2023-07-31 (GA) for Read, Layout, ID Document, Receipt, and Invoice models:

• REST API v3.0: 2022-08-31 (GA)
• REST API v3.1: 2023-07-31 (GA)
• Client libraries targeting REST API v3.0: 2022-08-31 (GA)
• Client libraries targeting REST API v3.1: 2023-07-31 (GA)

Prerequisites

To get started, you need an active Azure account. If you don't have one, you can create a free account.

You also need the following to use Document Intelligence containers:

Required	Purpose
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 terminology and commands.
Docker Engine installed	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.
Document Intelligence resource	A single-service Azure AI Document Intelligence or multi-service resource in the Azure portal. To use the containers, you must have the associated key and endpoint URI. Both values are available on the Azure portal Document Intelligence Keys and Endpoint page: {FORM_RECOGNIZER_KEY}: one of the two available resource keys. {FORM_RECOGNIZER_ENDPOINT_URI}: the endpoint for the resource used to track billing information.

Optional	Purpose
Azure CLI (command-line interface)	The Azure CLI enables you to use a set of online commands to create and manage Azure resources. It's available to install in Windows, macOS, and Linux environments and can be run in a Docker container and Azure Cloud Shell.

Host computer requirements

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:

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

Note

Note that Studio container cannot be deployed and run in Azure Kubernetes Service. Studio container is only supported to be run on local machine.

Container requirements and recommendations

Required supporting containers

The following table lists one or more supporting containers for each Document Intelligence container you download. For more information, see the Billing section.

Feature container	Supporting containers
Read	Not required
Layout	Not required
Business Card	Read
General Document	Layout
Invoice	Layout
Receipt	Read or Layout
ID Document	Read
Custom Template	Layout

Recommended CPU cores and memory

Note

The minimum and recommended values are based on Docker limits and not the host machine resources.

Document Intelligence containers

Container	Minimum	Recommended
Read	8 cores, 10-GB memory	8 cores, 24-GB memory
Layout	8 cores, 16-GB memory	8 cores, 24-GB memory
Business Card	8 cores, 16-GB memory	8 cores, 24-GB memory
General Document	8 cores, 12-GB memory	8 cores, 24-GB memory
ID Document	8 cores, 8-GB memory	8 cores, 24-GB memory
Invoice	8 cores, 16-GB memory	8 cores, 24-GB memory
Receipt	8 cores, 11-GB memory	8 cores, 24-GB memory
Custom Template	8 cores, 16-GB memory	8 cores, 24-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 compose or docker run command.

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>

Run the container with the docker-compose up command

• Replace the {ENDPOINT_URI} and {API_KEY} values with your resource Endpoint URI and the key from the Azure resource page.

  

• Ensure that the EULA value is set to accept.

• The EULA, Billing, and ApiKey values must be specified; otherwise the container can't start.

Important

The keys are used to access your Document Intelligence resource. Do not share your keys. Store them securely, for example, using Azure Key Vault. We also recommend regenerating these keys regularly. Only one key is necessary to make an API call. When regenerating the first key, you can use the second key for continued access to the service.

Read

The following code sample is a self-contained docker compose example to run the Document Intelligence Layout container. With docker compose, you use a YAML file to configure your application's services. Then, with the docker-compose up command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Layout container instance.

version: "3.9"
services:
  azure-form-recognizer-read:
    container_name: azure-form-recognizer-read
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
    environment:
      - EULA=accept
      - billing={FORM_RECOGNIZER_ENDPOINT_URI}
      - apiKey={FORM_RECOGNIZER_KEY}
    ports:
      - "5000:5000"
    networks:
      - ocrvnet
networks:
  ocrvnet:
    driver: bridge

Now, you can start the service with the docker compose command:

docker-compose up

General Document

The following code sample is a self-contained docker compose example to run the Document Intelligence General Document container. With docker compose, you use a YAML file to configure your application's services. Then, with docker-compose up command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your General Document and Layout container instances.

version: "3.9"
services:
  azure-cognitive-service-document:
    container_name: azure-cognitive-service-document
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/document-3.0
    environment:
        - EULA=accept
        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
        - apiKey={FORM_RECOGNIZER_KEY}
        - AzureCognitiveServiceLayoutHost=http://azure-cognitive-service-layout:5000
    ports:
      - "5000:5050"
  azure-cognitive-service-layout:
     container_name: azure-cognitive-service-layout
     image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.0
     environment:
         - EULA=accept
         - billing={FORM_RECOGNIZER_ENDPOINT_URI}
         - apiKey={FORM_RECOGNIZER_KEY}

Now, you can start the service with the docker compose command:

docker-compose up

Given the resources on the machine, the General Document container might take some time to start up.

Layout

The following code sample is a self-contained docker compose example to run the Document Intelligence Layout container. With docker compose, you use a YAML file to configure your application's services. Then, with docker-compose up command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Layout container instance.

version: "3.9"
services:
  azure-form-recognizer-layout:
    container_name: azure-form-recognizer-layout
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.1
    environment:
      - EULA=accept
      - billing={FORM_RECOGNIZER_ENDPOINT_URI}
      - apiKey={FORM_RECOGNIZER_KEY}
    ports:
      - "5000:5000"
    networks:
      - ocrvnet
networks:
  ocrvnet:
    driver: bridge

Now, you can start the service with the docker compose command:

docker-compose up

Invoice

The following code sample is a self-contained docker compose example to run the Document Intelligence Invoice container. With docker compose, you use a YAML file to configure your application's services. Then, with docker-compose up command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Invoice and Layout container instances.

You must use 3.1 GA Layout image as an upstream for both 3.0 GA and 3.1 GA Invoice models.

version: "3.9"
services:
  azure-cognitive-service-invoice:
    container_name: azure-cognitive-service-invoice
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/invoice-3.1
    environment:
        - EULA=accept
        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
        - apiKey={FORM_RECOGNIZER_KEY}
        - AzureCognitiveServiceLayoutHost=http://azure-cognitive-service-layout:5000
    ports:
      - "5000:5050"
  azure-cognitive-service-layout:
    container_name: azure-cognitive-service-layout
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.1
    environment:
        - EULA=accept
        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
        - apiKey={FORM_RECOGNIZER_KEY}

Now, you can start the service with the docker compose command:

docker-compose up

Receipt

The following code sample is a self-contained docker compose example to run the Document Intelligence General Document container. With docker compose, you use a YAML file to configure your application's services. Then, with docker-compose up command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Receipt and Read container instances.

You can use 3.1 GA Layout image as an upstream instead of Read image.

version: "3.9"
services:
  azure-cognitive-service-receipt:
    container_name: azure-cognitive-service-receipt
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/receipt-3.1
    environment:
        - EULA=accept
        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
        - apiKey={FORM_RECOGNIZER_KEY}
        - AzureCognitiveServiceReadHost=http://azure-cognitive-service-read:5000
    ports:
          - "5000:5050"
    azure-cognitive-service-read:
      container_name: azure-cognitive-service-read
      image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
      environment:
          - EULA=accept
          - billing={FORM_RECOGNIZER_ENDPOINT_URI}
          - apiKey={FORM_RECOGNIZER_KEY}

Now, you can start the service with the docker compose command:

docker-compose up

ID Document

The following code sample is a self-contained docker compose example to run the Document Intelligence General Document container. With docker compose, you use a YAML file to configure your application's services. Then, with docker-compose up command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your ID and Read container instances.

You can use 3.1 GA Layout image as an upstream instead of Read image.

version: "3.9"
services:
  azure-cognitive-service-id-document:
      container_name: azure-cognitive-service-id-document
      image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/id-document-3.1
      environment:
          - EULA=accept
          - billing={FORM_RECOGNIZER_ENDPOINT_URI}
          - apiKey={FORM_RECOGNIZER_KEY}
          - AzureCognitiveServiceReadHost=http://azure-cognitive-service-read:5000
      ports:
          - "5000:5050"
  azure-cognitive-service-read:
      container_name: azure-cognitive-service-read
      image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
      environment:
          - EULA=accept
          - billing={FORM_RECOGNIZER_ENDPOINT_URI}
          - apiKey={FORM_RECOGNIZER_KEY}

Now, you can start the service with the docker compose command:

docker-compose up

Business Card

version: "3.9"
services:
  azure-cognitive-service-invoice:
    container_name: azure-cognitive-service-businesscard
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/businesscard-3.0
    environment:
        - EULA=accept
        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
        - apiKey={FORM_RECOGNIZER_KEY}
        - AzureCognitiveServiceLayoutHost=http://azure-cognitive-service-layout:5000
    ports:
      - "5000:5050"
  azure-cognitive-service-layout:
    container_name: azure-cognitive-service-layout
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.0
    environment:
        - EULA=accept
        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
        - apiKey={FORM_RECOGNIZER_KEY}

Custom

In addition to the prerequisites, you need to do the following to process a custom document:

Create a folder and store the following files

• .env
• nginx.conf
• docker-compose.yml

Create a folder and store your input data

• Name this folder files.
• We reference the file path for this folder as {FILE_MOUNT_PATH}.
• Copy the file path in a convenient location, you need to add it to your .env file. As an example if the folder is called files, located in the same folder as the docker-compose file, the .env file entry is FILE_MOUNT_PATH="./files"

Create a folder to store the logs written by the Document Intelligence service on your local machine

• Name this folder output.
• We reference the file path for this folder as {OUTPUT_MOUNT_PATH}.
• Copy the file path in a convenient location, you need to add it to your .env file. As an example if the folder is called output, located in the same folder as the docker-compose file, the .env file entry is OUTPUT_MOUNT_PATH="./output"

Create a folder for storing internal processing shared between the containers

• Name this folder shared.
• We reference the file path for this folder as {SHARED_MOUNT_PATH}.
• Copy the file path in a convenient location, you need to add it to your .env file. As an example if the folder is called shared, located in the same folder as the docker-compose file, the .env file entry is SHARED_MOUNT_PATH="./shared"

Create a folder for the Studio to store project related information

• Name this folder db.
• We reference the file path for this folder as {DB_MOUNT_PATH}.
• Copy the file path in a convenient location, you need to add it to your .env file. As an example if the folder is called db, located in the same folder as the docker-compose file, the .env file entry is DB_MOUNT_PATH="./db"

Create an environment file

1. Name this file .env.

2. Declare the following environment variables:

SHARED_MOUNT_PATH="./shared"
OUTPUT_MOUNT_PATH="./output"
FILE_MOUNT_PATH="./files"
DB_MOUNT_PATH="./db"
FORM_RECOGNIZER_ENDPOINT_URI="YourFormRecognizerEndpoint"
FORM_RECOGNIZER_KEY="YourFormRecognizerKey"
NGINX_CONF_FILE="./nginx.conf"

Create an nginx file

1. Name this file nginx.conf.

2. Enter the following configuration:

worker_processes 1;

events { worker_connections 1024; }

http {

    sendfile on;
    client_max_body_size 90M;
    upstream docker-custom {
        server azure-cognitive-service-custom-template:5000;
    }

    upstream docker-layout {
        server  azure-cognitive-service-layout:5000;
    }

    server {
        listen 5000;

        location = / {
            proxy_set_header Host $host:$server_port;
            proxy_set_header Referer $scheme://$host:$server_port;
            proxy_pass http://docker-custom/;
        }

        location /status {
            proxy_pass http://docker-custom/status;
        }

        location /test {
            return 200 $scheme://$host:$server_port;
        }

        location /ready {
            proxy_pass http://docker-custom/ready;
        }

        location /swagger {
            proxy_pass http://docker-custom/swagger;
        }

        location /formrecognizer/documentModels/prebuilt-layout {
            proxy_set_header Host $host:$server_port;
            proxy_set_header Referer $scheme://$host:$server_port;

            add_header 'Access-Control-Allow-Origin' '*' always;
            add_header 'Access-Control-Allow-Headers' 'cache-control,content-type,ocp-apim-subscription-key,x-ms-useragent' always;
            add_header 'Access-Control-Allow-Methods' 'GET, POST, OPTIONS' always;
            add_header 'Access-Control-Expose-Headers' '*' always;

            if ($request_method = 'OPTIONS') {
                return 200;
            }

            proxy_pass http://docker-layout/formrecognizer/documentModels/prebuilt-layout;
        }

        location /formrecognizer/documentModels {
            proxy_set_header Host $host:$server_port;
            proxy_set_header Referer $scheme://$host:$server_port;

            add_header 'Access-Control-Allow-Origin' '*' always;
            add_header 'Access-Control-Allow-Headers' 'cache-control,content-type,ocp-apim-subscription-key,x-ms-useragent' always;
            add_header 'Access-Control-Allow-Methods' 'GET, POST, OPTIONS, DELETE' always;
            add_header 'Access-Control-Expose-Headers' '*' always;

            if ($request_method = 'OPTIONS') {
                return 200;
            }

            proxy_pass http://docker-custom/formrecognizer/documentModels;
        }

        location /formrecognizer/operations {
            add_header 'Access-Control-Allow-Origin' '*' always;
            add_header 'Access-Control-Allow-Headers' 'cache-control,content-type,ocp-apim-subscription-key,x-ms-useragent' always;
            add_header 'Access-Control-Allow-Methods' 'GET, POST, OPTIONS, PUT, DELETE, PATCH' always;
            add_header 'Access-Control-Expose-Headers' '*' always;

            if ($request_method = OPTIONS ) {
                return 200;
            }

            proxy_pass http://docker-custom/formrecognizer/operations;
        }
    }
}

Create a docker compose file

1. Name this file docker-compose.yml

2. The following code sample is a self-contained docker compose example to run Document Intelligence Layout, Studio, and Custom template containers together. With docker compose, you use a YAML file to configure your application's services. Then, with docker-compose up command, you create and start all the services from your configuration.

version: '3.3'
services:
  nginx:
    image: nginx:alpine
    container_name: reverseproxy
    volumes:
      - ${NGINX_CONF_FILE}:/etc/nginx/nginx.conf
    ports:
      - "5000:5000"
  layout:
    container_name: azure-cognitive-service-layout
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.0:latest
    environment:
      eula: accept
      apikey: ${FORM_RECOGNIZER_KEY}
      billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
      Logging:Console:LogLevel:Default: Information
      SharedRootFolder: /shared
      Mounts:Shared: /shared
      Mounts:Output: /logs
    volumes:
      - type: bind
        source: ${SHARED_MOUNT_PATH}
        target: /shared
      - type: bind
        source: ${OUTPUT_MOUNT_PATH}
        target: /logs
    expose:
      - "5000"

  custom-template:
    container_name: azure-cognitive-service-custom-template
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/custom-template-3.0:latest
    restart: always
    depends_on:
      - layout
    environment:
      AzureCognitiveServiceLayoutHost: http://azure-cognitive-service-layout:5000
      eula: accept
      apikey: ${FORM_RECOGNIZER_KEY}
      billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
      Logging:Console:LogLevel:Default: Information
      SharedRootFolder: /shared
      Mounts:Shared: /shared
      Mounts:Output: /logs
    volumes:
      - type: bind
        source: ${SHARED_MOUNT_PATH}
        target: /shared
      - type: bind
        source: ${OUTPUT_MOUNT_PATH}
        target: /logs
    expose:
      - "5000"

  studio:
    container_name: form-recognizer-studio
    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/studio:3.0
    environment:
      ONPREM_LOCALFILE_BASEPATH: /onprem_folder
      STORAGE_DATABASE_CONNECTION_STRING: /onprem_db/Application.db
    volumes:
      - type: bind
        source: ${FILE_MOUNT_PATH} # path to your local folder
        target: /onprem_folder
      - type: bind
        source: ${DB_MOUNT_PATH} # path to your local folder
        target: /onprem_db
    ports:
      - "5001:5001"
    user: "1000:1000" # echo $(id -u):$(id -g)

The custom template container and Layout container can use Azure Storage queues or in memory queues. The Storage:ObjectStore:AzureBlob:ConnectionString and queue:azure:connectionstring environment variables only need to be set if you're using Azure Storage queues. When running locally, delete these variables.

Ensure the service is running

To ensure that the service is up and running. Run these commands in an Ubuntu shell.

$cd <folder containing the docker-compose file>

$source .env

$docker-compose up

Custom template containers require a few different configurations and support other optional configurations.

Setting	Required	Description
EULA	Yes	License acceptance Example: Eula=accept
Billing	Yes	Billing endpoint URI of the FR resource
ApiKey	Yes	The endpoint key of the FR resource
Queue:Azure:ConnectionString	No	Azure Queue connection string
Storage:ObjectStore:AzureBlob:ConnectionString	No	Azure Blob connection string
HealthCheck:MemoryUpperboundInMB	No	Memory threshold for reporting unhealthy to liveness. Default: Same as recommended memory
StorageTimeToLiveInMinutes	No	TTL duration to remove all intermediate and final files. Default: Two days, TTL can set between five minutes to seven days
Task:MaxRunningTimeSpanInMinutes	No	Maximum running time for treating request as timeout. Default: 60 minutes
HTTP_PROXY_BYPASS_URLS	No	Specify URLs for bypassing proxy Example: HTTP_PROXY_BYPASS_URLS = abc.com, xyz.com
AzureCognitiveServiceReadHost (Receipt, IdDocument Containers Only)	Yes	Specify Read container uri Example:AzureCognitiveServiceReadHost=http://onprem-frread:5000
AzureCognitiveServiceLayoutHost (Document, Invoice Containers Only)	Yes	Specify Layout container uri Example:AzureCognitiveServiceLayoutHost=http://onprem-frlayout:5000

Use the Document Intelligence Studio to train a model

• Gather a set of at least five forms of the same type. You use this data to train the model and test a form. You can use a sample data set (download and extract sample_data.zip).

• Once you can confirm that the containers are running, open a browser and navigate to the endpoint where you have the containers deployed. If this deployment is your local machine, the endpoint is [http://localhost:5001](http://localhost:5001).

• Select the custom extraction model tile.

• Select the Create project option.

• Provide a project name and optionally a description

• On the "configure your resource" step, provide the endpoint to your custom template model. If you deployed the containers on your local machine, use this URL [http://localhost:5000](http://localhost:5000).

• Provide a subfolder for where your training data is located within the files folder.

• Finally, create the project

You should now have a project created, ready for labeling. Upload your training data and get started labeling. If you're new to labeling, see build and train a custom model.

Using the API to train

If you plan to call the APIs directly to train a model, the custom template model train API requires a base64 encoded zip file that is the contents of your labeling project. You can omit the PDF or image files and submit only the JSON files.

Once you have your dataset labeled and *.ocr.json, *.labels.json and fields.json files added to a zip, use the PowerShell commands to generate the base64 encoded string.

$bytes = [System.IO.File]::ReadAllBytes("<your_zip_file>.zip")
$b64String = [System.Convert]::ToBase64String($bytes, [System.Base64FormattingOptions]::None)

Use the build model API to post the request.

POST http://localhost:5000/formrecognizer/documentModels:build?api-version=2023-07-31

{
    "modelId": "mymodel",
    "description": "test model",
    "buildMode": "template",

    "base64Source": "<Your base64 encoded string>",
    "tags": {
       "additionalProp1": "string",
       "additionalProp2": "string",
       "additionalProp3": "string"
     }
}

Validate that the service is running

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

• The container provides a homepage at \ as a visual validation that the container is running.

• You can open your favorite web browser and navigate to the external IP address and exposed port of the container in question. Use the listed request URLs to validate the container is running. The listed example request URLs are http://localhost:5000, but your specific container can vary. Keep in mind that you're navigating to your container's External IP address and exposed port.

  Request URL	Purpose
  http://localhost:5000/	The container provides a home page.
  http://localhost:5000/ready	Requested with GET, this request provides a verification that the container is ready to accept a query against the model. This request can be used for Kubernetes liveness and readiness probes.
  http://localhost:5000/status	Requested with GET, this request verifies if the api-key used to start the container is valid 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 out 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 required HTTP headers and body format.

Stop the containers

To stop the containers, use the following command:

docker-compose down

Billing

The Document Intelligence containers send billing information to Azure by using a Document Intelligence resource on your Azure account.

Queries to the container are billed at the pricing tier of the Azure resource used for the API Key. Billing is calculated for each container instance used to process your documents and images.

If you receive the following error: Container isn't in a valid state. Subscription validation failed with status 'OutOfQuota' API key is out of quota. It's an indicator that your containers aren't communication wit the billing endpoint.

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. See the Azure AI container FAQ for an example of the information sent to Microsoft for billing.

Billing arguments

The docker-compose up command starts the container when all three of the following options are provided with valid values:

Option	Description
ApiKey	The key of the Azure AI services resource used to track billing information. The value of this option must be set to a key for the provisioned resource specified in Billing.
Billing	The endpoint of the Azure AI 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

That's it! In this article, you learned concepts and workflows for downloading, installing, and running Document Intelligence containers. In summary:

• Document Intelligence provides seven Linux containers for Docker.
• Container images are downloaded from mcr.
• Container images run in Docker.
• The billing information must be specified when you instantiate a container.

Important

Azure AI 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. Azure AI containers do not send customer data (for example, the image or text that is being analyzed) to Microsoft.

Next steps

• Document Intelligence container configuration settings

• Azure container instance recipe