modelzone-sdk 3.0.3

Modelzone SDK – a slim model training and serving toolkit

ModelZone SDK

A Python package for managing, training, and installing machine learning models. The package consists of:

• A Python SDK used for writing the code for training and inference of a machine learning model.
• A CLI used for tracking and registration of models in Azure ML.

Installation

pip install modelzone-sdk

Overview

Taking a look at the various Python machine learning frameworks out there, you will come across many different workflows used for training and prediction. ModelZone SDK uses the following definition:

A model project is a Python package with a training function, which is responsible for (1) tracking metrics and graphs used for comparing different model experiments and (2) generating model artifacts (such as model parameters and hyper-parameters) that need to be shipped with the package.

ModelZone SDK assumes your machine learning workflow looks more or less like the following:

1. Experimentation by training different models (and hyper-parameters).
2. Release of the best suited model for production usage.
3. Using the released model for inference in operations.

---

Quick guide

We have made an example of how a training and inference project should look like:

• Training project: examples/training_example
• Inference project: examples/predict_example
• Test script running the full workflow: examples/test.sh

---

Detailed guide

Model project structure

Training function

Your training function must be defined within the Python package:

# my-model/model.py
def train():
    X, y = ...
    lin_reg = LinearRegression()
    lin_reg.fit(X, y)

Tracking

If you have experience with the built-in logging module of Python, tracking experiment data with ModelZone SDK will feel much the same. Tracking is done using a global tracker, which needs to be configured before running your code (when using the CLI tool, this takes care of configuring your tracker to log to the correct place; local or Azure AI Workspace).

Metrics and visualziation

The tracker can be used for logging metrics and visualizations for your experiment run:

# my-model/model.py
import modelzone as mz

tracker = mz.get_tracker()

def train():
    X, y = ...
    lin_reg = LinearRegression()
    tracker.log_tag("model_type", "LinearRegression")
    lin_reg.fit(X, y)
    y_hat = lin_reg.predict(X)
    mae = (y - y_hat).abs().mean()
    tracker.log_metric("MAE", mae)

The tracker can also be used for logging artifacts for your experiment run (e.g. model parameters). You define an artifact by inheriting from the Artifact class available in ModelZone SDK (all this does is to provide methods for saving and loading the class using the pickle module):

# my-model/model.py
import modelzone as mz

tracker = mz.get_tracker()

class Predictor(mz.Artifact):

    def __init__(self, model: Estimator):
        self.model = model

    def predict(self, ...):
        ...

def train():
    X, y = ...
    lin_reg = LinearRegression()
    lin_reg.fit(X, y)
    predictor = Predictor(lin_reg)
    tracker.log_artifact("my-artifact", predictor)

When installing this package elsewhere, you can now get back the artifact as follows:

from mypackage import Predictor

predictor = Predictor.load(name="my-artifact")

Logging

ModelZone SDK allows for logging various items to your experiment run.

Metrics

You can log a single metric as follows

tracker.log_metric("MAE", 1.23)

You can also log multiple related metrics (e.g. the same metric but with varying context):

tracker.log_metric("MAE", 1.23, dimensions={"country": "DK"})
tracker.log_metric("MAE", 4.56, dimensions={"country": "SE"})

These will be visualized a Plotly table when tracking to Azure ML:

Figures

You can log a single figure as follows:

tracker.log_figure(
    name="Errors",
    figure=px.histogram(x=[1, 2, 2, 3, 3, 3]),
)

You can also log multiple related figures (e.g. the same figure but with varying context):

tracker.log_figure(
    name="Errors",
    figure=px.histogram(x=[1, 2, 2, 3, 3, 3]),
    dimensions={"country": "DK"}
)
tracker.log_figure(
    name="Errors",
    figure=px.histogram(x=[4, 4, 5, 5, 5, 6]),
    dimensions={"country": "SE"}
)

These will be visualized a Plotly figure with dropdown selection when tracking to Azure ML: