Auto-Conversion And Upload Tutorial

Machine Learning (ML) models can be converted into a Wallaroo and uploaded into Wallaroo workspace using the Wallaroo Client convert_model(path, source_type, conversion_arguments) method. This conversion process transforms the model into an open format that can be run across different frameworks at compiled C-language speeds.

The three input parameters are:

• path (STRING): The path to the ML model file.
• source_type (ModelConversionSource): The type of ML model to be converted. As of this time Wallaroo auto-conversion supports the following source types and their associated ModelConversionSource:
  • sklearn: ModelConversionSource.SKLEARN
  • xgboost: ModelConversionSource.XGBOOST
• conversion_arguments: The arguments for the conversion:
  • name: The name of the model being converted.
  • comment: Any comments for the model.
  • number_of_columns: The number of columns the model was trained for.
  • input_type: The ModelConversationInputType, typically Float or Double depending on the model.

The following tutorial demonstrates how to convert a sklearn Linear Model and a XGBoost Regression Model, and upload them into a Wallaroo Workspace. The following is provided for the tutorial:

• sklearn-linear-model.pickle: A sklearn linear model. An example of training the model is provided in the Jupyter Notebook sklearn-linear-model-example.ipynb. It has 25 columns.
• xgb_reg.pickle: A XGBoost regression model. An example of training the model is provided in the Jupyter Notebook xgboost-regression-model-example.ipynb. It has 25 columns.

Steps

Prerequisites

Before starting, the following must be available:

• The model to upload into a workspace.
• The number of columns the model was trained for.

Wallaroo supports the following model versions:

• XGBoost: Version 1.6.2
• SKLearn: 1.1.2

Import Libraries

Import the libraries that will be used for the auto-conversion process.

import pickle
import json

import wallaroo

from wallaroo.ModelConversion import ConvertSKLearnArguments, ConvertXGBoostArgs, ModelConversionSource, ModelConversionInputType
from wallaroo.object import EntityNotFoundError

# Verify the version of XGBoost used to generate the models

import sklearn
import sklearn.datasets

import xgboost as xgb

print(xgb.__version__)
print(sklearn.__version__)

1.6.2
1.1.2

The following code is used to either connect to an existing workspace or to create a new one. For more details on working with workspaces, see the Wallaroo Workspace Management Guide.

Connect to Wallaroo

Connect to your Wallaroo instance.

# Client connection from local Wallaroo instance

wl = wallaroo.Client()

# SSO login through keycloak

# wallarooPrefix = "YOUR PREFIX"
# wallarooSuffix = "YOUR SUFFIX"

# wl = wallaroo.Client(api_endpoint=f"https://{wallarooPrefix}.api.{wallarooSuffix}", 
#                     auth_endpoint=f"https://{wallarooPrefix}.keycloak.{wallarooSuffix}", 
#                     auth_type="sso")

Set the Workspace

We’ll connect or create the workspace testautoconversion and use it for our model testing.

workspace_name = 'testautoconversion'

def get_workspace(name):
    workspace = None
    for ws in wl.list_workspaces():
        if ws.name() == name:
            workspace= ws
    if(workspace == None):
        workspace = wl.create_workspace(name)
    return workspace

workspace = get_workspace(workspace_name)

wl.set_current_workspace(workspace)

wl.get_current_workspace()

{'name': 'testautoconversion', 'id': 20, 'archived': False, 'created_by': '435da905-31e2-4e74-b423-45c38edb5889', 'created_at': '2023-02-27T20:05:40.6269+00:00', 'models': [], 'pipelines': []}

Set the Model Conversion Arguments

We’ll create two different configurations, one for each of our models:

• sklearn_model_conversion_args: Used for our sklearn model.
• xgboost_model_converstion_args: Used for our XGBoost model.

# The number of columns
NF=25

sklearn_model_conversion_args = ConvertSKLearnArguments(
    name="sklearntest",
    comment="test linear regression",
    number_of_columns=NF,
    input_type=ModelConversionInputType.Double
)
sklearn_model_conversion_type = ModelConversionSource.SKLEARN

xgboost_model_conversion_args = ConvertXGBoostArgs(
    name="xgbtestreg",
    comment="xgboost regression model test",
    number_of_columns=NF,
    input_type=ModelConversionInputType.Float32
)
xgboost_model_conversion_type = ModelConversionSource.XGBOOST

Convert the Models

The convert_model method converts the model using the arguments, and uploads it into the current workspace - in this case, testconversion. Once complete, we can run get_current_workspace to verify that the models were uploaded.

# converts and uploads the sklearn model.
wl.convert_model('sklearn-linear-model.pickle', sklearn_model_conversion_type, sklearn_model_conversion_args)

# converts and uploads the XGBoost model.
wl.convert_model('xgb_reg.pickle', xgboost_model_conversion_type, xgboost_model_conversion_args)

{'name': 'xgbtestreg', 'version': '42779f3a-e874-4f8d-b985-822f2128a954', 'file_name': 'bffa90a2-5a27-4e77-a2f4-cd0f68f99d24-converted.onnx', 'image_path': None, 'last_update_time': datetime.datetime(2023, 2, 27, 20, 11, 36, 906580, tzinfo=tzutc())}

wl.get_current_workspace()

{'name': 'testautoconversion', 'id': 20, 'archived': False, 'created_by': '435da905-31e2-4e74-b423-45c38edb5889', 'created_at': '2023-02-27T20:05:40.6269+00:00', 'models': [{'name': 'sklearntest', 'versions': 1, 'owner_id': '""', 'last_update_time': datetime.datetime(2023, 2, 27, 20, 11, 35, 745445, tzinfo=tzutc()), 'created_at': datetime.datetime(2023, 2, 27, 20, 11, 35, 745445, tzinfo=tzutc())}, {'name': 'xgbtestreg', 'versions': 1, 'owner_id': '""', 'last_update_time': datetime.datetime(2023, 2, 27, 20, 11, 36, 906580, tzinfo=tzutc()), 'created_at': datetime.datetime(2023, 2, 27, 20, 11, 36, 906580, tzinfo=tzutc())}], 'pipelines': []}