LolaML - track your ML experiments

Documentation Status Pipeline Status Coverage Report PyPI Version

Track your machine learning experiments with LolaML, never lose any information or forget which parameter yielded which results. Lola creates a simple JSON representation of the run that contains all the information you logged. The JSON can easily be shared to collaborate with friends and colleagues. Lola strives to be non-magic and simple but configurable.

Lola’s documentation lives at Read the Docs, the code on Gitlab, and the latest release on PyPI. It’s tested on Python 3.6.

If you’d like to contribute to Lola you’re most welcome and we’ve written a little guide to get you started.


  • a simple logging interface
  • a simple JSON representation of the logged data
  • works with any machine learning library
  • automatically creates an artifact folder for each run
  • automatically uploads artifacts to a remote bucket (if you want)
  • simple jupyter notebook dashboard (more to come…)
import lolaml as lola

# Use the run context manager to start/end a run
with lola.Run(project="mnist", prefix_path="data/experiments") as run:
    # a unique id for the run
    # store all artifacts (model files, images, etc.) here
    print(run.path)  # -> data/experiments/mnist/<run_id>

    run.log_param("lr", 0.1)
    run.log_param("epochs", 10)

    run.log_tags("WIP", "RNN")

    # Create and train your model...

    run.log_metric("loss", loss, step=1)
    run.log_metric("train_acc", train_acc, step=1)
    run.log_metric("val_acc", val_acc, step=1), "model.pkl"))

# After a run there is a lola_run*.json file under run.path.
# It contails all the information you logged.

After the run there is a JSON file that looks something like this:

    "project": "mnist",
    "run_id": "9a531da0-dc43-4dcc-8968-77fd480ff7ee",
    "status": "done",
    "path": "data/experiments/mnist/9a531da0-dc43-4dcc-8968-77fd480ff7ee",
    "run_file": "data/experiments/mnist/9a531da0-dc43-4dcc-8968-77fd480ff7ee/lola_run.json",
    "user": "stefan",
    "start_time": "2019-02-16 12:49:32.782958",
    "end_time": "2019-02-16 12:49:32.814529",
    "metrics": [
            "name": "loss",
            "value": 1.5
            "step": 1,
            "ts": "2019-02-16 12:49:32.813750"
    "params": {
        "lr": "0.1",
        "epochs": 10,
    "tags": ["WIP", "RNN"],
    "artifacts": {
        "data/experiments/mnist/9a531da0-dc43-4dcc-8968-77fd480ff7ee/lola_run_9a531da0-dc43-4dcc-8968-77fd480ff7ee.json": {},
    "git": {
        "sha": "41cb4fb11b7e937c602c2282b9275200c88b8797",
        "status": "...",
        "diff": "...",
    "call_info": {
        "cwd": "some/where",
        "__file__": "",
        "argv": [],

Lola can automatically upload all artifacts to a remote storage bucket for you:

) as run:
    # train and log ...

# All artifacts are uploaded now

The remote location can also be configured with the .lola.toml file.

Additionally, Lola offers some helpers to analyse the your experiments:

TODO add image of dashboard



  • Python 3.6+


Install this library directly into an activated virtual environment:

$ pip install lolaml

or add it to your Poetry project:

$ poetry add lolaml


This project was generated with cookiecutter using jacebrowning/template-python. Thanks!

Indices and tables