cc @pamelafox

Any updates on this?

Sorry for delay, was just making sure I had the right answer. I believe you need to use this SDK for listing deployments:
https://pypi.org/project/azure-mgmt-cognitiveservices/

You'd initialize with this:
https://learn.microsoft.com/en-us/python/api/azure-mgmt-cognitiveservices/azure.mgmt.cognitiveservices.cognitiveservicesmanagementclient?view=azure-python

And then looks like the method to list deployments:
https://learn.microsoft.com/en-us/python/api/azure-mgmt-cognitiveservices/azure.mgmt.cognitiveservices.operations.deploymentsoperations?view=azure-python#azure-mgmt-cognitiveservices-operations-deploymentsoperations-list

It might require more filtering to get OpenAI specific deployments. I haven't used this particular API myself.

Background: For Azure resources, we generally distinguish between the "control plane" and the "data plane", where the control plane does stuff like create new Azure resources and the data plane actually uses those resources. In this case, the control plane is taken care of by azure-mgmt-cognitiveservices and the data plane by the openai package.

I've passed feedback onto the team that this is not a clear developer experience.

@rattrayalex / @mspronesti Could you please let us know what you plan to do with the models (/deployments) returned once listed using this SDK? As in what is your overarching use case/job to be done to list this? Fine tuning or something else?

OpenAI version: 1.3.7 Python version: 3.10

I'm trying to access the deployments associated with a specific Azure OpenAI subscription as follows

from openai import AzureOpenAI
client = AzureOpenAI(
 api_version="2023-08-01-preview",
 azure_endpoint="https://xxx.openai.azure.com/",
 api_key="xxx",
)
models = client.models.list()
print(models)
for model in models:
 print(model.id)

However this returns a list of all the models available, not those I deployed (with their specific deployment name).

ada
babbage
curie
davinci
gpt-35-turbo-0301
gpt-35-turbo-0613
gpt-35-turbo-1106
gpt-4-1106-Preview
gpt-35-turbo-instruct-0914
gpt-35-turbo-16k-0613
gpt-4-0314
gpt-4-0613
gpt-4-32k-0314
gpt-4-32k-0613
text-davinci-003
text-similarity-ada-001
text-similarity-babbage-001
text-similarity-curie-001
text-similarity-davinci-001
text-search-ada-doc-001
text-search-ada-query-001
text-search-babbage-doc-001
text-search-babbage-query-001
text-search-curie-doc-001
text-search-curie-query-001
text-search-davinci-doc-001
text-search-davinci-query-001
code-search-ada-code-001
code-search-ada-text-001
code-search-babbage-code-001
code-search-babbage-text-001
text-embedding-ada-002
text-embedding-ada-002-2
gpt-35-turbo
gpt-35-turbo-instruct
gpt-35-turbo-16k
gpt-4
gpt-4-32k
text-embedding-ada-002

How can I achieve the desired outcome? Thanks in advance :)

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It is really baffling that someone thought it's a good user experience to prevent listing the available deployments for an AI resource?!

We have a simple UI that lets users input their Service Endpoint and the Api Key (or better yet, an IT person put those information in for them). Now all that is left for the actual user to be able to submit a query to the LLM is to select the actual model they want to use (aka a deployed model in Azure world). If my developers could, with the two information above, get the list of deployments, they could present the user with a nice dropdown, and then the user can select one of their available models. Easy! Good UX.

But no, not with Azure! Here, either the user needs to know what deployment IDs they have available and paste them in manually, or find much more credentials and information for the overarching Azure account itself so my developers are able to query some resource endpoint to get the deployments.

TL;DR:

• The models listing API (https://learn.microsoft.com/en-us/rest/api/azureopenai/models/list?view=rest-azureopenai-2024年10月21日&tabs=HTTP#model) is pointless without an equivalent way of getting the available deployments.
• Terrible decision to remove the formerly existing azureopenai/deployments/list endpoint making UX of both users and devs much worse