1. Aug 23, 2020 Today we install the famous DNS advertisment blocker pihole in a LXC container on a Proxmox server, and set this as our network wide primary DNS server on the Unifi controller. The cool thing is that even as pihole was born as a Raspberry Pi project, it can easily run on most other Debian-based operating systems.
2. Feb 25, 2018 To run Gremlin on a host to attack Docker containers, view the guide on How to Install and Use Gremlin with Docker on Ubuntu 16.04. This tutorial will provide a walkthrough of the following: How to install Docker; How to create an Nginx Docker container to attack using Gremlin; How to install Gremlin in a Docker container.
3. The Red Hat Ecosystem Catalog is where you can find solutions built by our partners that are certified to work with your Red Hat technologies.

1. Install Container-selinux
2. Install Containerd Ubuntu 20.04
3. Install Containerd.io Centos 8
4. Install Container
5. Install Container Store Elfa Drawers

Mar 09, 2021 On a fresh system, it may take a few minutes for the container images to download. $ minikube kubectl get pods NAME READY STATUS RESTARTS AGE awx-5ffbfd489c-bvtvf 3/3 Running 0 2m54s awx-operator-7c78bfbfd-xb6th 1/1 Running 0 6m42s awx-postgres-0 1/1 Running 0 2m58s. For a flat one-time fee of $150, the community can install Discourse in the cloud for you. Click here to purchase a self-supported community install. Create New Cloud Server. Create your new cloud server, for example on DigitalOcean: The default of the current supported LTS release of Ubuntu Server works fine. At minimum, a 64-bit Linux OS with.

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Caution

• The Form Recognizer v1.0 container is being retired. This article is for reference only.
• You can access the latest version of the API with the REST API and SDK.

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 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.

Function	Features
Form Recognizer	Processes PDF, PNG, and JPG files Trains custom models with a minimum of five forms of the same layout Extracts key-value pairs and table information Uses the Azure Cognitive Services Computer Vision API Recognize Text feature to detect and extract printed text from images inside forms Doesn't require annotation or labeling

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:

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, such as registries, repositories, containers, and container images, and knowledge of basic docker commands.
The Azure CLI	Install the Azure CLI on your host.
Computer Vision API resource	To process scanned documents and images, you need a Computer Vision resource. You can access the Recognize Text feature as either an Azure resource (the REST API or SDK) or a cognitive-services-recognize-textcontainer. The usual billing fees apply. Pass in both the API key and endpoints for your Computer Vision resource (Azure cloud or Cognitive Services container). Use this API key and the endpoint as {COMPUTER_VISION_API_KEY} and {COMPUTER_VISION_ENDPOINT_URI}. If you use the cognitive-services-recognize-text container, make sure that: Your Computer Vision key for the Form Recognizer container is the key specified in the Computer Vision docker run command for the cognitive-services-recognize-text container. Your billing endpoint is the container's endpoint (for example, http://localhost:5000). If you use both the Computer Vision container and Form Recognizer container together on the same host, they can't both be started with the default port of 5000.
Form Recognizer resource	To use these containers, you must have: An Azure Form Recognizer resource to get the associated API key and endpoint URI. Both values are available on the Azure portal Form Recognizer Overview and Keys pages, and both values are required to start the container. {FORM_RECOGNIZER_API_KEY}: One of the two available resource keys on the Keys page {FORM_RECOGNIZER_ENDPOINT_URI}: The endpoint as provided on the Overview page

Note

The Computer Vision resource name should be a single word, without a hyphen - or any other special characters. This restriction is in place to ensure Form Recognizer and Recognize Text container compatibility.

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.

Important

These subscription keys are used to access your Cognitive Service API. 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.

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:

Container	Minimum	Recommended
Form Recognizer	2 core, 4-GB memory	4 core, 8-GB memory
Recognize Text	1 core, 8-GB memory	2 cores, 8-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.

Note

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

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

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:

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

Note

The directories use for --mount in these examples are Windows directory paths. If you're using Linux or macOS, change the parameter for your environment.

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.

Run the second container on port 5001.

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.

Important

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

Query the container's prediction endpoint

Container	Endpoint
form-recognizer	http://localhost:5000

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:

to this call, which uses the container endpoint:

For Python

Change from using this Azure-cloud initialization call:

to this call, which uses the container endpoint:

Form Recognizer

The container provides REST endpoint APIs, which you can find on the [Form Recognizer API]https://westus.dev.cognitive.microsoft.com/docs/services/form-recognizer-api-v2-1-preview-3/operations/AnalyzeWithCustomForm) reference 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.

Request URL	Purpose
http://localhost:5000/	The container provides a home page.
http://localhost:5000/ready	Requested with GET, this 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	Also requested with GET, this 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 HTTP headers and body format that's required.

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.

Tip

For more troubleshooting information and guidance, see Cognitive Services containers frequently asked questions (FAQ).

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 metering / billing endpoint. 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.

Install Container-selinux

Connect to Azure

Install Containerd Ubuntu 20.04

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 Cognitive Services container FAQ for an example of the information sent to Microsoft for billing.

Billing arguments

The docker run command will start the container when all three of the following options are provided 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.

Summary

Install Containerd.io Centos 8

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.

Install 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 (for example, the image or text that is being analyzed) to Microsoft.

Next steps

Install Container Store Elfa Drawers

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