memento-ml 1.0.0

A Python library for running computationally expensive experiments

Memento

Memento is a Python library for running computationally expensive experiments.

If you need to run a large number of time-consuming experiments Memento can help:

• Structure your configuration
• Parallelize experiments across CPUs
• Save and restore results
• Checkpoint in-progress experiments
• Send notifications when experiments fail or finish

Getting Started

Install

pip install memento-ml

The Configuration Matrix

The core of Memento is a configuration matrix that describes the list of experiments you want Memento to run. This must contain a key parameters which is itself a dict, this describes each paramter you want to vary for your experiments and their values.

As an example let's say you wanted to test a few simple linear classifiers on a number of image recognition datasets. You might write something like this:

Don't worry if you're not working on machine learning, this is just an example.

matrix = {
  "parameters": {
    "model": [
      sklearn.svm.SVC,
      sklearn.linear_model.Perceptron,
      sklearn.linear_model.LogisticRegression
    ],
    "dataset": ["imagenet", "mnist", "cifar10", "quickdraw"]
  }
}

Memento would then generate 12 configurations by taking the cartesian product of the parameters.

Frequently you might also want to set some global configuration values, such as a regularization parameter or potentially even change your preprocessing pipeline. In this case Memento also accepts a "settings" key. These settings apply to all experiments and can be accessed from the configuration list as well as individual configurations.

matrix = {
  "parameters": ...,
  "settings": {
    "regularization": 1e-1,
    "preprocessing": make_preprocessing_pipeline()
  }
}

You can also exclude specific parameter configurations. Returning to our machine learning example, if you know SVCs perform poorly on cifar10 you might decide to skip that experiment entirely. This is done with the "exclude" key:

matrix = {
  "parameters": ...,
  "exclude": [
    {"model": sklearn.svm.SVC, "dataset": "cifar10"}
  ]
}

Running an experiment

Along with a configuration matrix you need some code to run your experiments. This can be any Callable such as a function, lambda, class, or class method.

from memento import Memento, Config, Context

def experiment(context: Context, config: Config):
  classifier = config.model()
  dataset = fetch_dataset(config.dataset)

  classifier.fit(*dataset)

  return classifier

Memento(experiment).run(matrix)

You can also perform a dry run to check you've gotten the matrix correct.

Memento(experiment).run(matrix, dry_run=True)

Running configurations:
  {'model': sklearn.svm.SVC, 'dataset': 'imagenet'}
  {'model': sklearn.svm.SVC, 'dataset': 'mnist'}
  {'model': sklearn.svm.SVC, 'dataset': 'cifar10'}
  {'model': sklearn.svm.SVC, 'dataset': 'quickdraw'}
  {'model': sklearn.linear_model.Perceptron, 'dataset': 'imagenet'}
  ...
Exiting due to dry run

Code demo

• Code demo can be found here.
• Memento does not depend on scikit-learn. The scikit-learn and jupyterlab packages are required to run the demo (./demo/*).

pip install scikit-learn jupyterlab

Developing

Install as local package in Editable mode

pip install -e .

Install development dependencies

pip install memento-ml[dev]

Tests

pytest

Alternatively to only run a subset of tests that haven't been marked as time consuming/slow you can use:

pytest -m "not slow"

Linters

pylint memento

Format code

black .

Build Documentation

sphinx-apidoc -o docs memento -f
sphinx-build -W -b html docs docs/_build

Bump up version

# The `--dry` flag is for testing only. Remove `--dry` to update the version number.
# Use `minor` instead of `patch` for feature updates.
bumpver update --patch --dry

Run CI locally

Install act, then:

act

Roadmap

• Finish HPC support
• Improve result serialisation
• Production testing & fleshed-out integration test suite

Contributors

• Zac Pullar-Strecker
• Feras Albaroudi
• Liam Scott-Russell
• Joshua de Wet
• Nipun Jasti
• James Lamberton
• Joerg Wicker
• Xinglong (Luke) Chang

License

Memento is licensed under the 3-Clause BSD License license.