MLflow

An open source platform for the machine learning lifecycle

MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components: Tracking, Projects, Models and Registry.

Example
import mlflow
import mlflow.spacy

# MLflow Tracking
nlp = spacy.load('my_best_model_path/output/model-best')
with mlflow.start_run(run_name='Spacy'):
    mlflow.set_tag('model_flavor', 'spacy')
    mlflow.spacy.log_model(spacy_model=nlp, artifact_path='model')
    mlflow.log_metric(('accuracy', 0.72))
    my_run_id = mlflow.active_run().info.run_id


# MLflow Models
model_uri = f'runs:/{my_run_id}/model'
nlp2 = mlflow.spacy.load_model(model_uri=model_uri)

Author info

Databricks

GitHubmlflow/mlflow

Categories standalone apis

Submit your project

If you have a project that you want the spaCy community to make use of, you can suggest it by submitting a pull request to the spaCy website repository. The Universe database is open-source and collected in a simple JSON file. For more details on the formats and available fields, see the documentation. Looking for inspiration your own spaCy plugin or extension? Check out the project idea label on the issue tracker.

Read the docs JSON source