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 XGBoost models by containerizing the model and running as an image.

XGBoost Schema Inputs

XGBoost schema follows a different format than other models. To prevent inputs from being out of order, the inputs should be submitted in a single row in the order the model is trained to accept, with all of the data types being the same. If a model is originally trained to accept inputs of different data types, it will need to be retrained to only accept one data type for each column - typically pa.float64() is a good choice.

For example, the following DataFrame has 4 columns, each column a float.

For submission to an XGBoost model, the data input schema will be a single array with 4 float values.

input_schema = pa.schema([
    pa.field('inputs', pa.list_(pa.float64(), list_size=4))
])

When submitting as an inference, the DataFrame is converted to rows with the column data expressed as a single array. The data must be in the same order as the model expects, which is why the data is submitted as a single array rather than JSON labeled columns: this insures that the data is submitted in the exact order as the model is trained to accept.

Original DataFrame:

Converted DataFrame:

XGBoost Schema Outputs

Outputs for XGBoost are labeled based on the trained model outputs. For this example, the output is simply a single output listed as output. In the Wallaroo inference result, it is grouped with the metadata out as out.output.

output_schema = pa.schema([
    pa.field('output', pa.int32())
])

pipeline.infer(dataframe)

Uploading XGBoost Models

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

Upload XGBoost Model Parameters

The following parameters are required for XGBoost models. Note that while some fields are considered as optional for the upload_model method, they are required for proper uploading of a XGBoost 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 XGBoost Model Return

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

Upload XGBoost Model Example

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

input_schema = pa.schema([
    pa.field('inputs', pa.list_(pa.float64(), list_size=4))
])

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

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

model = wl.upload_model(f"{prefix}", 
                        'models/model-auto-conversion_xgboost_xgb_ranker_model.pkl', 
                        framework=Framework.XGBOOST, 
                        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..Pending conversion.Converting.........Ready.

{
    'name': 'xgb-ranker', 
    'version': 'c53c6a84-9f56-41c6-bb2f-049ef6b067e8', 
    'file_name': 'model-auto-conversion_xgboost_xgb_ranker_model.pkl', 
    'image_path': 'proxy.replicated.com/proxy/wallaroo/ghcr.io/wallaroolabs/mlflow-deploy:v2023.3.0-main-3367', 
    'last_update_time': datetime.datetime(2023, 6, 16, 18, 51, 15, 27969, tzinfo=tzutc())
}

data = pd.read_json('data/test-xgboost-classification-data.json')
display(data)

dataframe = pd.DataFrame({"inputs": data[:2].values.tolist()})
display(dataframe)

results = pipeline.infer(dataframe)
display(results)

Model Status

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()