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title: Content safety containers overview - Azure AI Content Safety
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titleSuffix: Azure AI services
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description: Use the Docker containers for the content safety service to perform content safety check on-premises.
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author:
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manager:
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description: Use the Docker containers for the content safety service to perform content safety operations on-premises.
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author: PatrickFarley
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manager: nitinme
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ms.service: azure-ai-content-safety
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ms.topic:
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ms.date:
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ms.author:
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ms.topic:overview
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ms.date: 09/23/2024
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ms.author:pafarley
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keywords: on-premises, Docker, container
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---
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# Content Safety containers overview
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By using containers, you can use a subset of the content safety service features in your own environment. With content safety containers, you can build a content safety application architecture optimized for both robust cloud capabilities and edge locality. Containers are great for specific security and data governance requirements.
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Containers let you use a subset of the Azure AI Content Safety features in your own environment. With content safety containers, you can build a content safety application architecture optimized for both robust cloud capabilities and edge locality. Containers help you meet specific security and data governance requirements.
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## Available content safety containers
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## Available containers
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The following table lists the content safety containers available in the Microsoft Container Registry (MCR). The table also lists the features supported by each container and the latest version of the container.
|Analyze text|Scans text for sexual content, violence, hate, and selfharm with multi-severity levels.|
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|Analyze image|Scans images for sexual content, violence, hate, and selfharm with multi-severity levels.|
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|Analyze text|Scans text for sexual content, violence, hate, and self-harm with multiple severity levels.|
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|Analyze image|Scans images for sexual content, violence, hate, and self-harm with multiple severity levels.|
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The content safety container is available in public preview. Containers in preview are still under development and don't meet Microsoft's stability and support requirements.
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## Request approval to run containers disconnected from the internet
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## Request approval to run offline containers
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To use the content safety containers in environments that are disconnected from the internet, you must submit a [request form](https://aka.ms/csdisconnectedcontainers) and wait for approval. For more information about applying and purchasing a commitment plan to use containers in disconnected environments, see [Use containers in disconnected environments](../../ai-services/containers/disconnected-containers.md) in the Azure AI services documentation.
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To use the content safety containers in environments that are disconnected from the internet, you must submit a [request form](https://aka.ms/csdisconnectedcontainers) and wait for approval. For more information about applying and purchasing a commitment plan to use containers in disconnected environments, see [Use containers in disconnected environments](../../../../ai-services/containers/disconnected-containers.md) in the Azure AI services documentation.
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The form requests information about you, your company, and the user scenario for which you use the container.
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The request form takes information about you, your company, and the user scenario for which you'll use the container. Make sure you meet the following requirements:
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* On the form, you must use an email address associated with an Azure subscription ID.
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* On the form, you must use the email address associated with your Azure subscription ID.
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* The Azure resource you use to run the container must be created with the approved Azure subscription ID.
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* Check your email for updates on the status of your application from Microsoft.
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After you submit the form, the Azure AI services team reviews it and emails you with a decision within 10 business days.
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## Billing
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The content safety containers send billing information to Azure by using a content safety resource on your Azure account.
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The content safety containers send billing information to Azure through the content safety resource in your Azure account.
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> [!NOTE]
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> Connected and disconnected container pricing and commitment tiers vary. For more information, see [content safety service pricing](https://azure.microsoft.com/en-us/pricing/details/cognitive-services/content-safety/).
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Content safety containers aren't licensed to run without being connected to Azure for metering. You must configure your container to always communicate billing information with the metering service. For more information, see [Billing arguments](./install-run-container.md#billing-arguments).
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Content safety containers aren't licensed to run without being connected to Azure for metering. You must configure your container to always communicate billing information with the metering service. For more information, see [billing arguments](content safety-container-howto.md#billing-arguments).
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> [!NOTE]
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> Connected and disconnected container pricing and commitment tiers vary. For more information, see [Content safety service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/content-safety/).
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## Container recipes and other container services
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You can use container recipes to create containers that can be reused. Containers can be built with some or all configuration settings so that they aren't needed when the container is started. For container recipes see the following Azure AI services articles:
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-[Create containers for reuse](../containers/container-reuse-recipe.md)
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-[Deploy and run container on Azure Container Instance](../containers/azure-container-instance-recipe.md)
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-[Deploy a language detection container to Azure Kubernetes Service](../containers/azure-kubernetes-recipe.md)
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-[Use Docker Compose to deploy multiple containers](../containers/docker-compose-recipe.md)
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-[Create containers for reuse](/azure/ai-services/containers/container-reuse-recipe)
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-[Deploy and run containers on Azure Container Instance](/azure/ai-services/containers/azure-container-instance-recipe)
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-[Deploy a language detection container to Azure Kubernetes Service](/azure/ai-services/containers/azure-kubernetes-recipe)
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-[Use Docker Compose to deploy multiple containers](/azure/ai-services/containers/docker-compose-recipe)
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For information about other container services, see the following Azure AI services articles:
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-[Tutorial: Create a container image for deployment to Azure Container Instances](/azure/container-instances/container-instances-tutorial-prepare-app)
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## Next steps
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*[Install and run analyze text containers](./analyze-text-container.md)
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[Install and run analyze image containers](./analyze-image-container.md)
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*[Install and run analyze image containers](./analyze-image-container.md)
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```
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This command:
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* Runs a`image-analyze` container from the container image.
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*`--gpus all` Use all available GPU resources. Content safety container requires CUDA for optimal performance. See more in [host requirements and recommendations](./container-install-run.md#host-computer-requirements-and-recommendations). Also make sure your host install [NVIDIA container toolkit](./container-install-run.md#installing-the-nvidia-container-toolkit)
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* Runs an`image-analyze` container from the container image.
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*`--gpus all` Use all available GPU resources. Content safety container requires CUDA for optimal performance. See more in [host requirements and recommendations](./container-install-run.md#host-computer-requirements-and-recommendations). Also make sure your host install [NVIDIA container toolkit](./container-install-run.md#installing-the-nvidia-container-toolkit).
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* Exposes TCP port 5000 and allocates a pseudo-TTY for the container.
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* Automatically removes the container after it exits. The container image is still available on the host computer.
Once the container is up and running, you can validate its operation by sending a request to the REST endpoint deployed within the container. To do this, follow below article. Note, you need to replace the endpoint URL with the Docker URL specific to your container deployment. Also, ensure that you're using host authentication, rather than key-based authentication.
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When you start or run the container, you might experience issues. Use an output [mount](content safety-container-configuration.md#mount-settings) and enable logging. Doing so allows the container to generate log files that are helpful when you troubleshoot issues.
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> [!TIP]
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> For more troubleshooting information and guidance, see [Azure AI containers frequently asked questions (FAQ)](../containers/container-faq.yml) in the Azure AI services documentation.
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> For more troubleshooting information and guidance, see [Azure AI containers frequently asked questions (FAQ)](../../../containers/container-faq.yml) in the Azure AI services documentation.
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### Logging settings
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Logging:Console:LogLevel:Default=Information
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```
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For more information about logging, see [Configure content safety containers](content safety-container-configuration.md#logging-settings) and [usage records](../containers/disconnected-containers.md#usage-records) in the Azure AI services documentation.
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For more information about logging, see [Configure content safety containers](./install-run-container.md#logging-settings) and [usage records](../../../containers/disconnected-containers.md#usage-records) in the Azure AI services documentation.
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Below SDK parameters can impact the infernece time of the embedded content safety model.
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-`gpuEnabled`* Set as **true** to enable GPU, otherwise CPU will be used. Generally inference time will be shorter on GPU.
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-`gpuEnabled` Set as **true** to enable GPU, otherwise CPU will be used. Generally inference time will be shorter on GPU.
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-`numThreads` This parameter only works for CPU. It defines number of threads to be used in a multi-threaded environment. We support a maximum number of 4 threads.
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See next section to for the performance benchmark data on popular PC CPUs and GPUs
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