Deploy

The .deploy() method of the ModelDeployer class is used to create a model deployment. It has the following parameters:

• max_wait_time: The timeout limit, in seconds, for the deployment process to wait until it is active. Defaults to 1200 seconds.

• poll_interval: The interval between checks of the deployment status in seconds. Defaults to 30 seconds.

• wait_for_completion: Blocked process until the deployment has been completed. Defaults to True.

There are two ways to use the .deploy() method. You can create a ModelDeploymentProperties object and pass that in, or you can define the model deployment properties using the .deploy() method.

Using ModelDeploymentProperties

After a ModelDeploymentProperties object is created, then you use model_deployment_properties to deploy a model as in this example:

from ads.model.deployment import ModelDeployer, ModelDeploymentProperties

model_deployment_properties = ModelDeploymentProperties(
   "<oci://your_bucket@your_namespace/path/to/dir>"
).with_prop(
    'display_name', "Model Deployment Demo using ADS"
).with_prop(
    "project_id", "<PROJECT_OCID>"
).with_prop(
    "compartment_id", "<COMPARTMENT_OCID>"
).with_logging_configuration(
    "<ACCESS_LOG_GROUP_OCID>", "<ACCESS_LOG_OCID>", "<PREDICT_LOG_GROUP_OCID>", "<PREDICT_LOG_OCID>"
).with_instance_configuration(
    config={"INSTANCE_SHAPE":"VM.Standard2.1", "INSTANCE_COUNT":"1",'bandwidth_mbps':10}
)
deployer = ModelDeployer()
deployment = deployer.deploy(model_deployment_properties)

Without Using ModelDeploymentProperties

Depending on your use case, it might be more convenient to skip the creation of a ModelDeploymentProperties object and create the model deployment directly using the .deploy() method. You can do this by passing the using keyword arguments instead of ModelDeploymentProperties. You specify the model deployment properties as parameters in the .deploy() method.

You define the model deployment properties using the following parameters:

• access_log_group_id: Log group OCID for the access logs. Required when access_log_id is specified.

• access_log_id: Custom logger OCID for the access logs. Required when access_log_group_id is specified.

• bandwidth_mbps: The bandwidth limit on the load balancer in Mbps. Optional.

• compartment_id: Compartment OCID that the model deployment belongs to.

• defined_tags: A dictionary of defined tags to be attached to the model deployment. Optional.

• description: A description of the model deployment. Optional.

• display_name: A name that identifies the model deployment in the Console.

• freeform_tags: A dictionary of freeform tags to be attached to the model deployment. Optional.

• instance_count: The number of instances to deploy.

• instance_shape: The instance compute shape to use. For example, “VM.Standard2.1”

• model_id: Model OCID that is used in the model deployment.

• predict_log_group_id: Log group OCID for the predict logs. Required when predict_log_id is specified.

• predict_log_id: Custom logger OCID for the predict logs. Required when predict_log_group_id is specified.

• project_id: Project OCID that the model deployment will belong to.

from ads.model.deployment import ModelDeployer

deployer = ModelDeployer()
deployment = deployer.deploy(
    model_id="<MODEL_OCID>",
    display_name="Model Deployment Demo using ADS",
    instance_shape="VM.Standard2.1",
    instance_count=1,
    project_id="<PROJECT_OCID>",
    compartment_id="<COMPARTMENT_OCID>",
    # The following are optional
    access_log_group_id="<ACCESS_LOG_GROUP_OCID>",
    access_log_id="<ACCESS_LOG_OCID>",
    predict_log_group_id="<PREDICT_LOG_GROUP_OCID>",
    predict_log_id="<PREDICT_LOG_OCID>"
)