How To Set Model Path

This document describes how to configure model paths in Olive for both local and remote model resources.

Local Model Path

If the model path is a local path or a string name (such as a model hub id), it can be directly specified as a string. Olive will automatically infer if it is a string name, local file or local directory.

{
    "model_path": "my_model.pt"
}

You can also specify the resource type explicitly.

Local File

{
    "model_path": {
        "type": "file",
        "path": "my_model.pt"
    }
}

Local Folder

{
    "model_path": {
        "type": "folder",
        "path": "my_model_dir"
    }
}

String Name

{
    "model_path": {
        "type": "string_name",
        "name": "my_model"
    }
}

Remote Model Path

Olive supports remote model resources. Currently, it supports AzureML model, AzureML datastore and AzureML job output.

AzureML Model

Models registered in an Azure Machine Learning workspace.

{
    "model_path": {
        "type": "azureml_model",
        "azureml_client": {
            "subscription_id": "my_subscription_id",
            "resource_group": "my_resource_group",
            "workspace_name": "my_workspace"
        },
        "name": "my_model",
        "version": 1
    }
}

AzureML Registry Model

Models curated in an Azure Machine Learning or models in your own registry. Azure ML curated model doesn’t require an azureml_client config section, but you can still add this section for additional mlclient configuration.

{
    "model_path": {
        "type": "azureml_registry_model",
        "name": "model_name",
        "registry_name": "registry_name",
        "version": 1
    }
}

AzureML Datastore

Model files or folders stored in an Azure Machine Learning datastore.

{
    "model_path": {
        "type": "azureml_datastore",
        "azureml_client": {
            "subscription_id": "my_subscription_id",
            "resource_group": "my_resource_group",
            "workspace_name": "my_workspace"
        },
        "datastore_name": "my_datastore",
        "relative_path": "model_dir/my_model.pt" // Relative path to the resource from the datastore root
    }
}

AzureML Job Output

Model files or folders generated by an Azure Machine Learning job and saved in the job output.

{
    "model_path": {
        "type": "azureml_job_output",
        "azureml_client": {
            "subscription_id": "my_subscription_id",
            "resource_group": "my_resource_group",
            "workspace_name": "my_workspace"
        },
        "job_id": "my_job_id", // id of the job
        "output_name": "my_output_name", // name of the job output
        "relative_path": "model_dir/my_model.pt" // Relative path to the resource from the job output root
    }
}

Note: If the workflow config file has azureml_client at the top level, azureml_client in the model path config can be omitted. The workflow will automatically use the top level azureml_client if it is not specified in the model path config.

{
    "azureml_client": {
        "subscription_id": "my_subscription_id",
        "resource_group": "my_resource_group",
        "workspace_name": "my_workspace"
    },
    "input_model": {
        "type": "PytorchModel",
        "model_path": {
            "type": "azureml_model",
            "name": "my_model",
            "version": 1
        }
    }
}