Wallaroo SDK Essentials Guide: Model Uploads and Registrations

Upload Model

ML Models are uploaded to Wallaroo Ops through the wallaroo.client.upload_model method.

Upload Model Parameters

wallaroo.client.upload_model has the following parameters.

Model Architecture Inheritance

Deployment configurations inherit the model’s architecture setting. This is set during model upload by specifying the arch parameter. By default, models uploaded to Wallaroo default to the x86 architecture.

The following model operations inherit the model’s architecture setting.

• Model Deployment: Model deployment and Model Deployment Deployment Configuration inherit the the model’s architecture. No specification of the architecture is required for model deployment.
• Pipeline Publishing: The Wallaroo engine set when a pipeline is containerized and published to an Open Container Initiative (OCI) Registry inherits the model’s architecture setting.

The following example shows uploading a model set with the architecture set to ARM, and how the deployment inherits that architecture without additional deployment configuration changes. For this example, an ONNX model is uploaded.

import wallaroo

housing_model_control_arm = (wl.upload_model(model_name_arm, 
                                        model_file_name, 
                                        framework=Framework.ONNX,
                                        arch=wallaroo.engine_config.Architecture.ARM)
                                        .configure(tensor_fields=["tensor"])
                        )

display(housing_model_control_arm)

Note that the deployment configuration settings, no architecture is specified. When pipeline_arm is displayed, we see the arch setting inherited the model’s arch setting.

pipeline_arm = wl.build_pipeline(arm_pipeline_name)

# set the model step with the ARM targeted model
pipeline_arm.add_model_step(housing_model_control_arm)

#minimum deployment config for this model
deploy_config = wallaroo.DeploymentConfigBuilder().replica_count(1).cpus(1).memory("1Gi").build()

pipeline_arm.deploy(deployment_config = deploy_config)

    Waiting for deployment - this will take up to 45s .......... ok

display(pipeline_arm)

pipeline_arm.status()

    {'status': 'Running',
     'details': [],
     'engines': [{'ip': '10.124.0.45',
       'name': 'engine-5d94d89b5d-gbr9h',
       'status': 'Running',
       'reason': None,
       'details': [],
       'pipeline_statuses': {'pipelines': [{'id': 'architecture-demonstration-arm',
          'status': 'Running'}]},
       'model_statuses': {'models': [{'config': {'batch_config': None,
           'filter_threshold': None,
           'id': 76,
           'input_schema': None,
           'model_version_id': 43,
           'output_schema': None,
           'runtime': 'onnx',
           'sidekick_uri': None,
           'tensor_fields': ['tensor']},
          'model_version': {'conversion': {'arch': 'arm',
            'framework': 'onnx',
            'python_version': '3.8',
            'requirements': []},
           'file_info': {'file_name': 'rf_model.onnx',
            'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6',
            'version': '163ff0a9-0f1a-4229-bbf2-a19e4385f10f'},
           'id': 43,
           'image_path': None,
           'name': 'house-price-estimator-arm',
           'status': 'ready',
           'task_id': None,
           'visibility': 'private',
           'workspace_id': 62},
          'status': 'Running'}]}}],
     'engine_lbs': [{'ip': '10.124.0.44',
       'name': 'engine-lb-d7cc8fc9c-4s9fc',
       'status': 'Running',
       'reason': None,
       'details': []}],
     'sidekicks': []}

Model Accelerator Inheritance

Models deployed to Wallaroo and edge deployments include AI hardware accelerator support. The type of accelerator is set using the wallaroo.client.model_upload(accel: wallaroo.engine_config.Accelerator | None) parameter.

Once uploaded, model deployment configurations for deployments and publishes inherit the model’s accelerator.

The following accelerators are supported.

The following model operations inherit the model’s accelerator setting.

• Model Deployment: Model deployment and Model Deployment Deployment Configuration inherit the the model’s architecture. No specification of the architecture is required for model deployment.
• Pipeline Publishing: The Wallaroo engine set when a pipeline is containerized and published to an Open Container Initiative (OCI) Registry inherits the model’s architecture setting.

Upload Model Returns

wallaroo.client.upload_model returns the model version. The model version refers to the version of the model object in Wallaroo. In Wallaroo, a model version update happens when we upload a new model file (artifact) against the same model object name.

• Note that models are uploaded to the current workspace assigned in the SDK session. By default, this is the user’s Default Workspace.

Upload Model Examples

The following examples demonstrate uploading different model types. Models uploaded to Wallaroo fall under two runtimes:

• Wallaroo Native Runtimes: The following model frameworks are always deployed in the Wallaroo Native Runtime. When these model frameworks are uploaded to Wallaroo, the model name, file path, and model framework are required.

  • ONNX
  • Tensorflow

• Wallaroo Containerized Runtimes: The following model frameworks may be deployed in either the Wallaroo Native Runtime, or the Wallaroo Containerized Runtime. When these models are uploaded to Wallaroo, the model name, file path, model framework, input and output schemas are required.

  When uploaded, Wallaroo will attempt to convert Non-Native Runtimes to a Wallaroo Native Runtime. If it can not be converted, then it will be packed into a Wallaroo Containerized Runtime.

  • Arbitrary Python(BYOP)
  • Python Models
  • PyTorch
  • SKLearn
  • Hugging Face
  • Tensorflow Keras
  • XGBoost

Native Runtime Upload

The following demonstrates uploading a ONNX model to a Wallaroo Ops instance. For Wallaroo SDK Essentials Guide: Model Uploads and Registrations: ONNX for full details on uploading ONNX models and model configurations.

ONNX models are deployed in the Wallaroo Native Runtime and require the following fields when uploaded via the wallaroo.client.Client.upload_model method:

The following example demonstrates uploading the ONNX file via the Wallaroo SDK.

model = wl.upload_model(
            name = 'sample-model',
            path = './models/sample_model.onnx',
            framework = wallaroo.framework.Framework.ONNX
        )

pipeline.add_model_step(model)

deploy_config = wallaroo.DeploymentConfigBuilder()\
                .replica_count(1)\
                .cpus(0.5)\
                .memory("1Gi")\
                .build()
pipeline.deploy(deployment_config=deploy_config)

smoke_test = pd.DataFrame.from_records([
    {
        "dense_input":[
            1.0678324729,
            0.2177810266,
            -1.7115145262,
            0.682285721,
            1.0138553067,
            -0.4335000013,
            0.7395859437,
            -0.2882839595,
            -0.447262688,
            0.5146124988,
            0.3791316964,
            0.5190619748,
            -0.4904593222,
            1.1656456469,
            -0.9776307444,
            -0.6322198963,
            -0.6891477694,
            0.1783317857,
            0.1397992467,
            -0.3554220649,
            0.4394217877,
            1.4588397512,
            -0.3886829615,
            0.4353492889,
            1.7420053483,
            -0.4434654615,
            -0.1515747891,
            -0.2668451725,
            -1.4549617756
        ]
    }
])
result = pipeline.infer(smoke_test)
display(result)

Wallaroo Containerized Upload

Models uploaded to Wallaroo that may require containerization before deploying in Wallaroo require the following parameters when uploaded via the Wallaroo SDK method wallaroo.client.Client.upload_model.

The following demonstrates uploading an PyTorch model to a Wallaroo Ops instance. In this example, the ML model is converted to the Wallaroo Native Runtime.

input_schema = pa.schema(
    [
        pa.field('input', pa.list_(pa.float32(), list_size=10))
    ]
)

output_schema = pa.schema(
[
    pa.field('output', pa.list_(pa.float32(), list_size=1))
]
)

model = wl.upload_model('pt-single-io-model', 
                        "./models/model-auto-conversion_pytorch_single_io_model.pt", 
                        framework=Framework.PYTORCH, 
                        input_schema=input_schema, 
                        output_schema=output_schema
                       )
display(model)

Waiting for model loading - this will take up to 10.0min.
Model is pending loading to a native runtime..
Ready

model.config().runtime()

'onnx'

The following example demonstrates uploading a BYOP model. After it is uploaded, it is converted to a Wallaroo Containerized Runtime.

input_schema = pa.schema([
    pa.field('images', pa.list_(
        pa.list_(
            pa.list_(
                pa.int64(),
                list_size=3
            ),
            list_size=32
        ),
        list_size=32
    )),
])

output_schema = pa.schema([
    pa.field('predictions', pa.int64()),
])

model = wl.upload_model('vgg16-clustering', 
                       './models/model-auto-conversion-BYOP-vgg16-clustering.zip', 
                        framework=Framework.CUSTOM, 
                        input_schema=input_schema, 
                        output_schema=output_schema, 
                        convert_wait=True)

Waiting for model loading - this will take up to 10.0min.
Model is pending loading to a container runtime..
Model is attempting loading to a container runtime..........................successful

Ready

model.config().runtime()

'flight'

Register a Containerized MLFlow Model

For models that do not fall under the supported model frameworks, organizations can use containerized MLFlow ML Models.

This guide details how to add ML Models from a model registry service into Wallaroo.

Wallaroo supports both public and private containerized model registries. See the Wallaroo Private Containerized Model Container Registry Guide for details on how to configure a Wallaroo instance with a private model registry.

Wallaroo users can register their trained MLFlow ML Models from a containerized model container registry into their Wallaroo instance and perform inferences with it through a Wallaroo pipeline.

As of this time, Wallaroo only supports MLFlow 1.30.0 containerized models. For information on how to containerize an MLFlow model, see the MLFlow Documentation.

Containerized MLFlow models are not uploaded, but registered from a container registry service. This is performed through the wallaroo.client.register_model_image(options), and wallaroo.model_version.configure(options) method.

Register a Containerized MLFlow Model Parameters

The following parameters must be set for wallaroo.client.register_model_image(options) and wallaroo.model_version.configure(options) for a Containerized MLFlow model to be registered in Wallaroo.

Register Model Image Parameters

Model Version Configuration Parameters

Model version configurations are updated with the wallaroo.model_version.config and include the following parameters.

For model version configuration for MLFlow models, the following must be defined:

• runtime: Set as mlflow.
• input_schema: The input schema from the Apache Arrow pyarrow.lib.Schema format.
• output_schema: The output schema from the Apache Arrow pyarrow.lib.Schema format.

Register a Containerized MLFlow Model Returns

wallaroo.client.register_model_image(options) returns the model version. The model version refers to the version of the model object in Wallaroo. In Wallaroo, a model version update happens when we upload a new model file (artifact) against the same model object name.

• Note that models are uploaded to the current workspace assigned in the SDK session. By default, this is the user’s Default Workspace.

Register a Containerized MLFlow Model Example

The following example demonstrates registering a Statsmodel model stored in a MLFLow container with a Wallaroo instance.

sm_input_schema = pa.schema([
  pa.field('temp', pa.float32()),
  pa.field('holiday', pa.uint8()),
  pa.field('workingday', pa.uint8()),
  pa.field('windspeed', pa.float32())
])

sm_output_schema = pa.schema([
    pa.field('predicted_mean', pa.float32())
])

sm_model = wl.register_model_image(
    name="mlflow-statmodels",
    image="ghcr.io/wallaroolabs/wallaroo_tutorials/mlflow-statsmodels-example:2023.1"
    ).configure("mlflow", 
            input_schema=sm_input_schema, 
            output_schema=sm_output_schema
    )

sm_model

MLFlow Data Formats

When using containerized MLFlow models with Wallaroo, the inputs and outputs must be named. For example, the following output:

[-12.045839810372835]

Would need to be wrapped with the data values named:

[{"prediction": -12.045839810372835}]

A short sample code for wrapping data may be:

output_df = pd.DataFrame(prediction, columns=["prediction"])
return output_df

Get Model Config

The model versions configuration defines how the model is used in the Wallaroo Inference Engine. Settings include:

• The runtime
• Input and output schemas

The model version configuration is retrieved with the method wallaroo.model_version.ModelVersion.config().

Get Model Config Parameters

N/A

Get Model Config Returns

The method wallaroo.model_version.ModelVersion.config() returns wallaroo.model_config.ModelConfig. The following fields are part of the model config object.

Get Model Config Example

The following examples retrieves the model runtime from a model version.

import wallaroo

# get the most recent model version
model_config = sample_model.versions()[-1].config()

print(model_config.runtime())

onnx