mllogs

Local experiment tracking and model registry for machine learning.

Why mllogs?

mllogs is built for developers who want structured experiment tracking without running a separate tracking server or external service. It provides a simple local workflow for tracking experiments, runs, parameters, metrics, artifacts, and model versions using SQLite and the filesystem.

Use it when your training code has outgrown ad hoc dictionaries, folders, and filenames, but you do not need the infrastructure of a larger ML platform.

Installation

pip install mllogs

Quickstart

Track an experiment

Train several models with different hyperparameter configurations and track their results.

from mllogs import MLLogs
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# Prepare data
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Create experiment
mll = MLLogs()
experiment = mll.create_experiment("ridge-tuning")

best_mse = float("inf")
best_model_ref = None

# Track different configurations
for alpha in [0.1, 1.0, 10.0]:
    mll.start_run(experiment.name)

    model = Ridge(alpha=alpha)
    model.fit(X_train, y_train)

    predictions = model.predict(X_test)
    mse = mean_squared_error(y_test, predictions)

    mll.log_param("alpha", alpha)
    mll.log_metric("mse", mse)

    mll.save_artifact("predictions", predictions, "npy")
    model_ref = mll.save_artifact("model", model, "joblib")

    mll.complete_run()

    if mse < best_mse:
        best_mse = mse
        best_model_ref = model_ref

Each configuration creates a separate run within the same experiment, preserving its parameters, metrics, and trained model.

Query experiments and runs

Retrieve experiments and inspect previously recorded runs.

mll.query.list_experiments()
runs = mll.query.list_runs(experiment.name)

run = runs[0]
mll.query.get_run(run.id)
mll.query.get_params(run.id)
mll.query.get_metrics(run.id)
mll.query.get_tags(run.id)

Load saved artifacts

Load a previously trained model from its associated run.

mll.query.list_artifacts(run.id)

predictions = mll.query.load_artifact(run.id, "predictions")
model = mll.query.load_artifact(run.id, "model")

Supported artifact formats

mllogs supports saving and loading artifacts in the following formats:

Specify the format when saving an artifact:

mll.save_artifact("predictions", predictions, "csv")
mll.save_artifact("model", model, "joblib")

Artifacts are associated with individual runs and can be loaded via mll.query.load_artifact().

Note: Only load Pickle or Joblib from trusted sources, since deserialization can execute arbitrary code.

Register models

Register the best-performing model artifact as a versioned model artifact.

mll.registry.create_version("diabetes-ridge", best_model_ref)

latest = mll.registry.get_latest_version("diabetes-ridge")

create_version automatically registers the model if it does not already exist. Subsequent versions can reference new model artifacts without overwriting previous versions.

Retrieve a specific model version:

version = mll.registry.get_version(
    model_name="diabetes-ridge",
    version=1,
)

Features

• Create experiments to organize related machine learning runs
• Start, complete, and fail runs
• Log parameters, metrics, and tags
• Persist run metadata to a local SQLite database
• Save and load artifacts using local file storage
• Query experiments, runs, and logged data
• Register models and create versioned references to persisted model artifacts

Development

Install from source with development dependencies:

pip install -e ".[dev]"

Run the test suite:

pytest

License

MIT