Wallaroo SDK Essentials Guide: Model Uploads and Registrations: SKLearn

How to upload and use SKLearn ML Models with Wallaroo

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

Sci-kit Learn aka SKLearn.

Web Sitehttps://scikit-learn.org/stable/index.html
Supported Libraries
  • scikit-learn==1.2.2
FrameworkFramework.SKLEARN aka sklearn
RuntimeContainerized aka tensorflow / mlflow

SKLearn Schema Inputs

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

 sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)

For submission to an SKLearn 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:

 sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)

Converted DataFrame:

0[5.1, 3.5, 1.4, 0.2]
1[4.9, 3.0, 1.4, 0.2]

SKLearn Schema Outputs

Outputs for SKLearn that are meant to be predictions or probabilities when output by the model are labeled in the output schema for the model when uploaded to Wallaroo. For example, a model that outputs either 1 or 0 as its output would have the output schema as follows:

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

When used in Wallaroo, the inference result is contained in the out metadata as out.predictions.

02023-07-05 15:11:29.776[5.1, 3.5, 1.4, 0.2]00
12023-07-05 15:11:29.776[4.9, 3.0, 1.4, 0.2]00

Uploading SKLearn Models

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

Upload SKLearn Model Parameters

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

namestring (Required)The name of the model. Model names are unique per workspace. Models that are uploaded with the same name are assigned as a new version of the model.
pathstring (Required)The path to the model file being uploaded.
frameworkstring (Upload Method Optional, SKLearn model Required)Set as the Framework.SKLEARN.
input_schemapyarrow.lib.Schema (Upload Method Optional, SKLearn model Required)The input schema in Apache Arrow schema format.
output_schemapyarrow.lib.Schema (Upload Method Optional, SKLearn model Required)The output schema in Apache Arrow schema format.
convert_waitbool (Upload Method Optional, SKLearn model Optional) (Default: True)
  • True: Waits in the script for the model conversion completion.
  • False: Proceeds with the script without waiting for the model conversion process to display complete.

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

Upload SKLearn Model Return

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

namestringThe name of the model.
versionstringThe model version as a unique UUID.
file_namestringThe file name of the model as stored in Wallaroo.
image_pathstringThe image used to deploy the model in the Wallaroo engine.
last_update_timeDateTimeWhen the model was last updated.

Upload SKLearn Model Example

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

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

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

model = wl.upload_model(

Waiting for model conversion... It may take up to 10.0min.
Model is Pending conversion..Converting.Pending conversion..Converting.......Ready.

    'name': 'sklearn-linear-regression', 
    'version': 'e84809eb-0992-457e-95cb-cc3d20c792db', 
    'file_name': 'model-auto-conversion_sklearn_linreg_diabetes.pkl', 
    'image_path': 'proxy.replicated.com/proxy/wallaroo/ghcr.io/wallaroolabs/mlflow-deploy:v2023.3.0-main-3443', 
    'last_update_time': datetime.datetime(2023, 6, 27, 22, 17, 52, 967054, tzinfo=tzutc())
0[0.0380759064, 0.0506801187, 0.0616962065, 0.0…
1[-0.0018820165, -0.0446416365, -0.051474061200…
02023-07-05 15:43:33.065[0.0380759064, 0.0506801187, 0.0616962065, 0.0…206.1166770
12023-07-05 15:43:33.065[-0.0018820165, -0.0446416365, -0.0514740612, …68.0710330

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.

Runtime DisplayModel Runtime SpacePipeline Configuration
tensorflowNativeNative Runtime Configuration Methods
onnxNativeNative Runtime Configuration Methods
pythonNativeNative Runtime Configuration Methods
mlflowContainerizedContainerized Runtime Deployment

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()

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, 

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()

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')
                        "__timeout=188 --workers=1"}