Submission#

aurora.foundry.client.api.submit(batch: Batch, model_name: str, num_steps: int, channel: CommunicationChannel, foundry_client: FoundryClient) → Generator[Batch, None, None][source]#

Submit a request to Azure AI Foundry and retrieve the predictions.

Parameters:

• batch (aurora.Batch) – Initial condition.

• model_name (str) – Name of the model. This name must be available in aurora.foundry.common.model.

• num_steps (int) – Number of prediction steps.

• channel (aurora.foundry.common.channel.CommunicationChannel) – Channel to use for sending and receiving data.

• foundry_client (aurora.foundry.client.foundry.FoundryClient) – Client to communicate with Azure Foundry AI.

Yields:

aurora.Batch – Predictions.

class aurora.foundry.client.foundry.FoundryClient(endpoint: str, token: str)[source]#

__init__(endpoint: str, token: str) → None[source]#

Initialise.

Parameters:

• endpoint (str) – URL to the endpoint.

• token (str) – Authorisation token.

class aurora.foundry.common.channel.BlobStorageChannel(blob_folder: str)[source]#

A communication channel via a folder in an Azure Blob Storage container.

__init__(blob_folder: str) → None[source]#

Instantiate.

Parameters:

blob_folder (str) – Folder to use. This must be a full URL that includes a SAS token with read and write permissions.

Available Models#

These models need to be referred to by the value of their attribute name.

class aurora.foundry.common.model.AuroraFineTuned[source]#

name = 'aurora-0.25-finetuned'#

Name of the model.

Type:

str

class aurora.foundry.common.model.AuroraSmall[source]#

name = 'aurora-0.25-small-pretrained'#

Name of the model.

Type:

str

Server#

aurora.foundry.server.mlflow_wrapper.AuroraModelWrapper()[source]#

A wrapper around an async workflow for making predictions with Aurora.