Model Naming Requirements

Model names map onto Kubernetes objects, and must be DNS compliant. The strings for model names must be ASCII alpha-numeric characters or dash (-) only. . and _ are not allowed.

Wallaroo supports PyTorch models by containerizing the model and running as an image.

Uploading PyTorch Models

PyTorch models are uploaded to Wallaroo through the Wallaroo Client upload_model method.

Upload PyTorch Model Parameters

The following parameters are required for PyTorch models. Note that while some fields are considered as optional for the upload_model method, they are required for proper uploading of a PyTorch model to Wallaroo.

Once the upload process starts, the model is containerized by the Wallaroo instance. This process may take up to 10 minutes.

Upload PyTorch Model Return

The following is returned with a successful model upload and conversion.

Upload PyTorch Model Example

The following example is of uploading a PyTorch ML Model to a Wallaroo instance.

input_schema = pa.schema(
    [
        pa.field('input', pa.list_(pa.float64(), list_size=10))
    ]
)

output_schema = pa.schema(
[
    pa.field('output', pa.list_(pa.float64(), 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
                       )
model

Waiting for model conversion... It may take up to 10.0min.
Model is Pending conversion..Converting...........Ready.
{
    'name': 'pt-single-io-model', 
    'version': '8f91dee1-79e0-449b-9a59-0e93ba4a1ba9', 
    'file_name': 'model-auto-conversion_pytorch_single_io_model.pt', 
    'image_path': 'proxy.replicated.com/proxy/wallaroo/ghcr.io/wallaroolabs/mlflow-deploy:v2023.3.0-main-3397', 
    'last_update_time': datetime.datetime(2023, 6, 23, 2, 8, 56, 669565, tzinfo=tzutc())
}

Pipeline Deployment Configurations

Pipeline deployment configurations are dependent on whether the model is converted to the Native Runtime space, or Containerized Model Runtime space. This is determined when the model is uploaded based on the size, complexity, and other factors.

Once uploaded, the Model method config().runtime() will display which space the model is in.

For example, uploading an runtime model to a Wallaroo workspace would return the following config().runtime():

ccfraud_model = wl.upload_model(model_name, model_file_name, Framework.ONNX).configure()
ccfraud_model.config().runtime()
'onnx'

For example, the following containerized model after conversion is allocated to the containerized runtime as follows:

model = wl.upload_model(model_name, model_file_name, 
                        framework=framework, 
                        input_schema=input_schema, 
                        output_schema=output_schema
                       )
model.config().runtime()
'mlflow'

Native Runtime Pipeline Deployment Configuration Example

The following configuration allocates 0.25 CPU and 1 Gi RAM to the native runtime models for a pipeline.

deployment_config = DeploymentConfigBuilder()
                    .cpus(0.25)
                    .memory('1Gi')
                    .build()

Containerized Runtime Deployment Example

The following configuration allocates 0.25 CPU and 1 Gi RAM to a specific containerized model in the containerized runtime, along with other environmental variables for the containerized model. Note that for containerized models, resources must be allocated per specific model.

deployment_config = DeploymentConfigBuilder()
                    .sidekick_cpus(sm_model, 0.25)
                    .sidekick_memory(sm_model, '1Gi')
                    .sidekick_env(sm_model, 
                        {"GUNICORN_CMD_ARGS":
                        "__timeout=188 --workers=1"}
                    )
                    .build()