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Copy file name to clipboardExpand all lines: articles/azure-functions/flex-consumption-plan.md
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@@ -153,7 +153,7 @@ Keep these other considerations in mind when using Flex Consumption plan:
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+**Proxies**: Functions Proxies aren't supported. Consider integrating your function apps with Azure API Management.
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+**Scale**: The lowest maximum scale is currently `40`. The highest currently supported value is `1000`.
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+**Managed dependencies**: [Managed dependencies in PowerShell](functions-reference-powershell.md#managed-dependencies-feature) aren't supported by Flex Consumption. You must instead [upload modules with app content](functions-reference-powershell.md#including-modules-in-app-content).
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+**Certificates**: Loading certificates with the WEBSITE_LOAD_CERTIFICATES app setting, managed certificates, app service certificates, and other platform certificate-based features are currently not supported.
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+**Certificates**: Loading certificates with the WEBSITE_LOAD_CERTIFICATES app setting, managed certificates, app service certificates, and other platform certificate-based features like endToEndEncryptionEnabled are currently not supported.
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+**Key Vault and App Configuration References**: You can't currently use [Azure Key Vault](../app-service/app-service-key-vault-references.md) or [Azure App Configuration](../app-service/app-service-configuration-references.md) references in your Flex Consumption plan app settings when these services are network access restricted. This limitation applies even when the function app has Virtual Network integration enabled. If you must use restricted Key Vault or App Configuration instances, you must use client SDKs to manually retrieve values from references in these services. Functions binding extensions also can't access these references, which means you must also use Azure client SDKs for accessing remote service data from your function code.
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+**Timezones**: `WEBSITE_TIME_ZONE` and `TZ` app settings aren't currently supported when running on Flex Consumption plan.
Copy file name to clipboardExpand all lines: articles/container-apps/gpu-serverless-overview.md
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## Request serverless GPU quota
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Access to this feature is only available after you have serverless GPU quota. You can submit your GPU quota request via a [customer support case](/azure/azure-portal/supportability/how-to-create-azure-support-request). When opening a support case for a GPU quota request, select issue type "Technical."
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> [!NOTE]
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> Customers with enterprise agreements and pay-as-you-go customers have A100 and T4 quota enabled by default.
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Access to this feature is only available after you have serverless GPU quota. You can submit your GPU quota request via a [customer support case](/azure/azure-portal/supportability/how-to-create-azure-support-request). When opening a support case for a GPU quota request, please select the following:
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1. In the **Problem description** tab, select the following values:
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| Field | Value |
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|--|--|
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| Issue type | Service and subscription limits (quota) |
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| Subscription | Your subscription |
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| Quota type | Container Apps |
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1. In the **Additional details** tab, select **Request details**.
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1. For **Quota type**, select either **Managed Environment Consumption NCA100 Gpus** or **Managed Environment Consumption T4 Gpus**.
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1. Complete the remaining required fields to submit your request.
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## Supported regions
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Serverless GPUs are available in the following regions:
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1. Download the github template for the model image from the [Azure Container Apps repo](https://github.com/microsoft/azure-container-apps/tree/main/templates/azml-app).
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1. Modify the score.py file to match your model type. The scoring script (named *score.py*) defines how you interact with the model. The following example shows [how to use a custom score.py file](/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2&tabs=cli).
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1. Modify the score.py file to match your model type. The scoring script (named *score.py*) defines how you interact with the model. The following example shows [how to use a custom score.py file](/azure/machine-learning/how-to-deploy-online-endpoints).
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1. Build the image and deploy it to a container registry.
Copy file name to clipboardExpand all lines: articles/iot-edge/about-iot-edge.md
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---
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title: What is Azure IoT Edge
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description: Learn how Azure IoT Edge enables you to deploy, run, and monitor containerized Linux workloads at the edge for better business insights and offline decision-making.
Copy file name to clipboardExpand all lines: articles/iot-edge/debug-module-vs-code.md
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title: Debug Azure IoT Edge modules using Visual Studio Code
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description: Debug Azure IoT Edge modules in Visual Studio Code with step-by-step guidance for multiple programming languages and device architectures.
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