Install and run Form Recognizer containers (Preview)

• 11/21/2019
• 13 minutes to read
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Azure Form Recognizer applies machine learning technology to identify and extract key-value pairs and tables from forms. It associates values and table entries with the key-value pairs and then outputs structured data that includes the relationships in the original file.

To reduce complexity and easily integrate a custom Form Recognizer model into your workflow automation process or other application, you can call the model by using a simple REST API. Only five form documents (or one empty form and two filled-in forms) are needed, so you can get results quickly, accurately, and tailored to your specific content. No heavy manual intervention or extensive data science expertise is necessary. And it doesn't require data labeling or data annotation.

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

Prerequisites

Before you use Form Recognizer containers, you must meet the following prerequisites:

Gathering required parameters

There are three primary parameters for all Cognitive Services' containers that are required. The end-user license agreement (EULA) must be present with a value of accept. Additionally, both an Endpoint URL and API Key are needed.

Endpoint URI {COMPUTER_VISION_ENDPOINT_URI} and {FORM_RECOGNIZER_ENDPOINT_URI}

The Endpoint URI value is available on the Azure portal Overview page of the corresponding Cognitive Service resource. Navigate to the Overview page, hover over the Endpoint, and a Copy to clipboard icon will appear. Copy and use where needed.

Keys {COMPUTER_VISION_API_KEY} and {FORM_RECOGNIZER_API_KEY}

This key is used to start the container, and is available on the Azure portal's Keys page of the corresponding Cognitive Service resource. Navigate to the Keys page, and click on the Copy to clipboard icon.

Request access to the container registry

You must first complete and submit the Cognitive Services Form Recognizer Containers access request form to request access to the container. Doing so also signs you up for Computer Vision. You don't need to sign up for the Computer Vision request form separately.

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.

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:

• 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 minimum and recommended CPU cores and memory to allocate for each Form Recognizer container are described in the following table:

• 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 images with the docker pull command

Container images for both the Form Recognizer and Recognize Text offerings are available in the following container registry:

You will need both containers, please note that the Recognizer Text container is detailed outside of this article.

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

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

Docker pull for the Form Recognizer container

Form Recognizer

To get the Form Recognizer container, use the following command:

docker pull containerpreview.azurecr.io/microsoft/cognitive-services-form-recognizer:latest

Docker pull for the Recognize Text container

Recognize Text

To get the Recognize Text container, use the following command:

docker pull containerpreview.azurecr.io/microsoft/cognitive-services-recognize-text:latest

How to use the container

After 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 by using the docker run command

Use the docker run command to run the container. Refer to gathering required parameters for details on how to get the {COMPUTER_VISION_ENDPOINT_URI}, {COMPUTER_VISION_API_KEY}, {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_API_KEY} values.

Examples of the docker run command are available.

Form Recognizer

docker run --rm -it -p 5000:5000 --memory 8g --cpus 2 \
--mount type=bind,source=c:\input,target=/input  \
--mount type=bind,source=c:\output,target=/output \
containerpreview.azurecr.io/microsoft/cognitive-services-form-recognizer \
Eula=accept \
Billing={FORM_RECOGNIZER_ENDPOINT_URI} \
ApiKey={FORM_RECOGNIZER_API_KEY} \
FormRecognizer:ComputerVisionApiKey={COMPUTER_VISION_API_KEY} \
FormRecognizer:ComputerVisionEndpointUri={COMPUTER_VISION_ENDPOINT_URI}

This command:

• Runs a Form Recognizer container from the container image.
• Allocates 2 CPU cores and 8 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.
• Mounts an /input and an /output volume to the container.

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.

Run separate containers as separate docker run commands

For the Form Recognizer and Text Recognizer combination that's hosted locally on the same host, use the following two example Docker CLI commands:

Run the first container on port 5000.

docker run --rm -it -p 5000:5000 --memory 4g --cpus 1 \
--mount type=bind,source=c:\input,target=/input  \
--mount type=bind,source=c:\output,target=/output \
containerpreview.azurecr.io/microsoft/cognitive-services-form-recognizer \
Eula=accept \
Billing={FORM_RECOGNIZER_ENDPOINT_URI} \
ApiKey={FORM_RECOGNIZER_API_KEY}
FormRecognizer:ComputerVisionApiKey={COMPUTER_VISION_API_KEY} \
FormRecognizer:ComputerVisionEndpointUri={COMPUTER_VISION_ENDPOINT_URI}

Run the second container on port 5001.

docker run --rm -it -p 5001:5000 --memory 4g --cpus 1 \
containerpreview.azurecr.io/microsoft/cognitive-services-recognize-text \
Eula=accept \
Billing={COMPUTER_VISION_ENDPOINT_URI} \
ApiKey={COMPUTER_VISION_API_KEY}

Each subsequent container should be on a different port.

Run separate containers with Docker Compose

For the Form Recognizer and Text Recognizer combination that's hosted locally on the same host, see the following example Docker Compose YAML file. The Text Recognizer {COMPUTER_VISION_API_KEY} must be the same for both the formrecognizer and ocr containers. The {COMPUTER_VISION_ENDPOINT_URI} is used only in the ocr container, because the formrecognizer container uses the ocr name and port.

version: '3.3'
services:   
  ocr:
    image: "containerpreview.azurecr.io/microsoft/cognitive-services-recognize-text"
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 8g
        reservations:
          cpus: '1'
          memory: 4g
    environment:
      eula: accept
      billing: "{COMPUTER_VISION_ENDPOINT_URI}"
      apikey: "{COMPUTER_VISION_API_KEY}"

  formrecognizer:
    image: "containerpreview.azurecr.io/microsoft/cognitive-services-form-recognizer"
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 8g
        reservations:
          cpus: '1'
          memory: 4g
    environment:
      eula: accept
      billing: "{FORM_RECOGNIZER_ENDPOINT_URI}"
      apikey: "{FORM_RECOGNIZER_API_KEY}"
      FormRecognizer__ComputerVisionApiKey: {COMPUTER_VISION_API_KEY}
      FormRecognizer__ComputerVisionEndpointUri: "http://ocr:5000"
      FormRecognizer__SyncProcessTaskCancelLimitInSecs: 75
    links:
      - ocr
    volumes:
      - type: bind
        source: c:\output
        target: /output
      - type: bind
        source: c:\input
        target: /input
    ports:
      - "5000:5000"

Query the container's prediction endpoint

Form Recognizer

The container provides websocket-based query endpoint APIs, which you access through Form Recognizer services SDK documentation.

By default, the Form Recognizer SDK uses the online services. To use the container, you need to change the initialization method. See the examples below.

For C#

Change from using this Azure-cloud initialization call:

var config =
    FormRecognizerConfig.FromSubscription(
        "YourSubscriptionKey",
        "YourServiceRegion");

to this call, which uses the container endpoint:

var config =
    FormRecognizerConfig.FromEndpoint(
        "ws://localhost:5000/formrecognizer/v1.0-preview/custom",
        "YourSubscriptionKey");

For Python

Change from using this Azure-cloud initialization call:

formrecognizer_config =
    formrecognizersdk.FormRecognizerConfig(
        subscription=formrecognizer_key, region=service_region)

to this call, which uses the container endpoint:

formrecognizer_config = 
    formrecognizersdk.FormRecognizerConfig(
        subscription=formrecognizer_key,
        endpoint="ws://localhost:5000/formrecognizer/v1.0-preview/custom"

Form Recognizer

The container provides REST endpoint APIs, which you can find on the Form Recognizer API page.

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 below to validate the container is running. The example request URLs listed below are http://localhost:5000, but your specific container may vary. Keep in mind that you're 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 Form Recognizer containers send billing information to Azure by using a Form Recognizer 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:

For more information about these options, see Configure containers.

Blog posts

• Running Cognitive Services Containers
• Azure Cognitive Services

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 Form Recognizer containers. In summary:

• Form Recognizer provides one Linux container for Docker.
• Container images are downloaded from the private container registry in Azure.
• Container images run in Docker.
• You can use either the REST API or the REST SDK to call operations in Form Recognizer container by specifying the host URI of the container.
• You must specify the billing information when you instantiate a container.

Next steps

• Review Configure containers for configuration settings.
• Use more Cognitive Services Containers.