Chapter 06

Scenario 1: A single data scientist participating in an ML competition

NotebookPython 3.9.12 ('exp-tracking-env')12 cells

Scenario 1: A single data scientist participating in an ML competition

MLflow setup:

Tracking server: no
Backend store: local filesystem
Artifacts store: local filesystem

The experiments can be explored locally by launching the MLflow UI.

In [ ]python · cell 2

python

import mlflow

In [ ]python · cell 3

python

print(f"tracking URI: '{mlflow.get_tracking_uri()}'")

In [ ]python · cell 4

python

mlflow.search_experiments()

Creating an experiment and logging a new run

In [ ]python · cell 6

python

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score

mlflow.set_experiment("my-experiment-1")

with mlflow.start_run():

    X, y = load_iris(return_X_y=True)

    params = {"C": 0.1, "random_state": 42}
    mlflow.log_params(params)

    lr = LogisticRegression(**params).fit(X, y)
    y_pred = lr.predict(X)
    mlflow.log_metric("accuracy", accuracy_score(y, y_pred))

    mlflow.sklearn.log_model(lr, artifact_path="models")
    print(f"default artifacts URI: '{mlflow.get_artifact_uri()}'")

In [ ]python · cell 7

python

mlflow.search_experiments()

In [ ]python · cell 8

python

Interacting with the model registry

In [ ]python · cell 10

python

from mlflow.tracking import MlflowClient


client = MlflowClient()

In [ ]python · cell 11

python

from mlflow.exceptions import MlflowException

try:
    client.search_registered_models()
except MlflowException:
    print("It's not possible to access the model registry :(")

In [ ]python · cell 12

python