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Copy file name to clipboardExpand all lines: articles/ai-services/openai/api-version-deprecation.md
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# Azure OpenAI API preview lifecycle
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This article is to help you understand the support lifecycle for the Azure OpenAI API previews.
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This article is to help you understand the support lifecycle for the Azure OpenAI API previews. New preview APIs target a monthly release cadence. Post April 2, 2024, the latest three preview APIs will remain supported while older APIs will no longer be supported.
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> [!NOTE]
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> The `2023-06-01-preview` API will remain supported at this time, as `DALL-E 2` is only available in this API version. `DALL-E 3` is supported in the latest API releases.
Copy file name to clipboardExpand all lines: articles/ai-services/openai/concepts/provisioned-throughput.md
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#### How many concurrent calls can I have on my deployment?
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The number of concurrent calls you can have at one time is dependent on each call's shape. The service will continue to accept calls until the utilization is above 100%. To determine the approximate number of concurrent calls you can model out the maximum requests per minute for a particular call shape in the [capacity calculator](https://oai.azure.com/portal/calculator). If `max_tokens` is empty, you can assume a value of 1000
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The number of concurrent calls you can achieve depends on each call's shape (prompt size, max_token parameter, etc). The service will continue to accept calls until the utilization reach 100%. To determine the approximate number of concurrent calls you can model out the maximum requests per minute for a particular call shape in the [capacity calculator](https://oai.azure.com/portal/calculator). If the system generates less than the number of samplings tokens like max_token, it will accept more requests.
-`2023-07-01-preview` (retiring April 2, 2024) [Swagger spec](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-07-01-preview/inference.json)
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-`2023-08-01-preview` (retiring April 2, 2024) [Swagger spec](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-08-01-preview/inference.json)
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-`2023-09-01-preview` (retiring April 2, 2024) [Swagger spec](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-09-01-preview/inference.json)
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-`2023-12-01-preview` (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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-`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)
Copy file name to clipboardExpand all lines: articles/ai-studio/how-to/sdk-generative-overview.md
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The Azure AI Generative Python SDK includes a telemetry feature that collects usage and failure data about the SDK and sends it to Microsoft when you use the SDK in a Jupyter Notebook only. Telemetry won't be collected for any use of the Python SDK outside of a Jupyter Notebook.
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Telemetry data helps the SDK team understand how the SDK is used so it can be improved and the information about failures helps the team resolve problems and fix bugs. The SDK telemetry feature is enabled by default for Jupyter Notebook usage and can't be enabled for non-Jupyter scenarios. To opt out of the telemetry feature in a Jupyter scenario, set the environment variable `"AZURE_AI_GENERATIVE_ENABLE_LOGGING"` to `"False"`.
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Telemetry data helps the SDK team understand how the SDK is used so it can be improved and the information about failures helps the team resolve problems and fix bugs. The SDK telemetry feature is enabled by default for Jupyter Notebook usage and can't be enabled for non-Jupyter scenarios.
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To opt out of the telemetry feature in a Jupyter scenario:
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- When using the `azure-ai-generative` package, set both of the following environment variables to `"False"`: `"AZURE_AI_GENERATIVE_ENABLE_LOGGING"` and `"AZURE_AI_RESOURCES_ENABLE_LOGGING"`. Both environment variables need to be set to `"False"` since `azure-ai-generative` is dependent on `azure-ai-resources`.
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- When using the `azure-ai-resources` package, set the environment variable `"AZURE_AI_RESOURCES_ENABLE_LOGGING"` to `"False"`.
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