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Copy file name to clipboardExpand all lines: articles/ai-services/openai/faq.yml
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How do I deploy a model with the REST API?
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answer:
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There are currently two different REST APIs that allow model deployment. For the latest model deployment features such as the ability to specify a model version during deployment for models like text-embedding-ada-002 Version 2, use the [Deployments - Create Or Update](/rest/api/cognitiveservices/accountmanagement/deployments/create-or-update?tabs=HTTP) REST API call.
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There are currently two different REST APIs that allow model deployment. For the latest model deployment features such as the ability to specify a model version during deployment for models like text-embedding-ada-002 Version 2, use the [Deployments - Create Or Update](/rest/api/aiservices/accountmanagement/deployments/create-or-update?tabs=HTTP) REST API call.
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Can I use quota to increase the max token limit of a model?
Copy file name to clipboardExpand all lines: articles/ai-services/openai/how-to/quota.md
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**Request body**
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This is only a subset of the available request body parameters. For the full list of the parameters, you can refer to the [REST API reference documentation](/rest/api/cognitiveservices/accountmanagement/deployments/create-or-update?tabs=HTTP).
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This is only a subset of the available request body parameters. For the full list of the parameters, you can refer to the [REST API reference documentation](/rest/api/aiservices/accountmanagement/deployments/create-or-update?tabs=HTTP).
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|Parameter|Type| Description |
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|--|--|--|
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> [!NOTE]
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> There are multiple ways to generate an authorization token. The easiest method for initial testing is to launch the Cloud Shell from the [Azure portal](https://portal.azure.com). Then run [`az account get-access-token`](/cli/azure/account?view=azure-cli-latest#az-account-get-access-token&preserve-view=true). You can use this token as your temporary authorization token for API testing.
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For more information, refer to the REST API reference documentation for [usages](/rest/api/cognitiveservices/accountmanagement/usages/list?branch=main&tabs=HTTP) and [deployment](/rest/api/cognitiveservices/accountmanagement/deployments/create-or-update).
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For more information, refer to the REST API reference documentation for [usages](/rest/api/aiservices/accountmanagement/usages/list?branch=main&tabs=HTTP) and [deployment](/rest/api/aiservices/accountmanagement/deployments/create-or-update).
Copy file name to clipboardExpand all lines: articles/ai-services/openai/how-to/use-your-data-securely.md
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To allow your Azure AI Search and Storage Account to recognize your Azure OpenAI service via Microsoft Entra ID authentication, you need to assign a managed identity for your Azure OpenAI service. The easiest way is to toggle on system assigned managed identity on Azure portal.
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:::image type="content" source="../media/use-your-data/openai-managed-identity.png" alt-text="A screenshot showing the system assigned managed identity option in the Azure portal." lightbox="../media/use-your-data/openai-managed-identity.png":::
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To set the managed identities via the management API, see [the management API reference documentation](/rest/api/cognitiveservices/accountmanagement/accounts/update#identity).
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To set the managed identities via the management API, see [the management API reference documentation](/rest/api/aiservices/accountmanagement/accounts/update#identity).
To query the current model deployment settings including the deployment upgrade configuration for a given resource use [`Deployments List`](/rest/api/cognitiveservices/accountmanagement/deployments/list?tabs=HTTP#code-try-0). If the value is null, you won't see a `versionUpgradeOption` property.
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To query the current model deployment settings including the deployment upgrade configuration for a given resource use [`Deployments List`](/rest/api/aiservices/accountmanagement/deployments/list?tabs=HTTP#code-try-0). If the value is null, you won't see a `versionUpgradeOption` property.
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```http
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GET https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{accountName}/deployments?api-version=2023-05-01
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**Request body**
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This is only a subset of the available request body parameters. For the full list of the parameters, you can refer to the [REST API reference documentation](/rest/api/cognitiveservices/accountmanagement/deployments/create-or-update).
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This is only a subset of the available request body parameters. For the full list of the parameters, you can refer to the [REST API reference documentation](/rest/api/aiservices/accountmanagement/deployments/create-or-update).
-`2023-12-01-preview` (retiring April 2, 2024) (This version or greater required for Vision scenarios) [Swagger spec](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-12-01-preview)
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Azure OpenAI is deployed as a part of the Azure AI services. All Azure AI services rely on the same set of management APIs for creation, update, and delete operations. The management APIs are also used for deploying models within an OpenAI resource.
Unlike the previous Python SDK commands in this tutorial, since the introduction of the quota feature, model deployment must be done using the [REST API](/rest/api/cognitiveservices/accountmanagement/deployments/create-or-update?tabs=HTTP), which requires separate authorization, a different API path, and a different API version.
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Unlike the previous Python SDK commands in this tutorial, since the introduction of the quota feature, model deployment must be done using the [REST API](/rest/api/aiservices/accountmanagement/deployments/create-or-update?tabs=HTTP), which requires separate authorization, a different API path, and a different API version.
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Alternatively, you can deploy your fine-tuned model using any of the other common deployment methods like [Azure OpenAI Studio](https://oai.azure.com/), or [Azure CLI](/cli/azure/cognitiveservices/account/deployment#az-cognitiveservices-account-deployment-create()).
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Deleting the deployment won't affect the model itself, so you can re-deploy the fine-tuned model that you trained for this tutorial at any time.
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You can delete the deployment in [Azure OpenAI Studio](https://oai.azure.com/), via [REST API](/rest/api/cognitiveservices/accountmanagement/deployments/delete?tabs=HTTP), [Azure CLI](/cli/azure/cognitiveservices/account/deployment#az-cognitiveservices-account-deployment-delete()), or other supported deployment methods.
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You can delete the deployment in [Azure OpenAI Studio](https://oai.azure.com/), via [REST API](/rest/api/aiservices/accountmanagement/deployments/delete?tabs=HTTP), [Azure CLI](/cli/azure/cognitiveservices/account/deployment#az-cognitiveservices-account-deployment-delete()), or other supported deployment methods.
Copy file name to clipboardExpand all lines: articles/api-center/enable-api-analysis-linting.md
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This article shows how to enable linting to analyze API definitions in your organization's [API center](overview.md) for conformance with your organizations's API style rules. Linting generates an analysis report that you can access in your API center. Use API linting and analysis to detect common errors and inconsistencies in your API definitions.
Copy file name to clipboardExpand all lines: articles/azure-monitor/containers/container-insights-data-collection-dcr.md
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### Cost presets
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When you use the Azure portal to configure cost optimization, you can select from the following preset configurations. You can select one of these or provide your own customized settings. By default, Container insights uses the *Standard* preset.
| Cost-optimized | 5 m | Excludes kube-system, gatekeeper-system, azure-arc | Not enabled |
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| Syslog | 1 m | None | Enabled by default |
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| Cost preset | Collection frequency | Namespace filters | Syslog collection | Collected data |
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| --- | --- | --- | --- | --- |
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| Standard | 1 m | None | Not enabled | All standard container insights tables |
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| Cost-optimized | 5 m | Excludes kube-system, gatekeeper-system, azure-arc | Not enabled | All standard container insights tables |
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| Syslog | 1 m | None | Enabled by default | All standard container insights tables |
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| Logs and Events | 1 m | None | Not enabled | ContainerLog/ContainerLogV2<br> KubeEvents<br>KubePodInventory |
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### Collected data
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The **Collected data** option allows you to select the tables that are populated for the cluster. This is the equivalent of the `streams` parameter when performing the configuration with CLI or ARM. If you select any option other than **All (Default)**, the Container insights experience becomes unavailable, and you must use Grafana or other methods to analyze collected data.
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