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mitosis

Reproduce Machine Learning experiments easily

0.6.1
pipPyPI
Maintainers
1

Documentation Status License: MIT PyPI version Downloads Code style: black

Overview

Mitosis is an experiment runner. It handles administrative tasks to decrease the mental overhead of collaboration:

  • Creating a CLI for your experiment
  • Recording commit information
  • Tracking parameterization, as well as parameter names (e.g. "low-noise")
  • Storing logs
  • Generating HTML visuals
  • Pickling result data

The virtuous consequence of these checks and organization is a faster workflow, a more rigorous scientific method, and reduced mental overhead of collaboration.

Trivial Example

Hypothesis: the maximum value of a sine wave is equal to its amplitude.

sine_experiment/__init__.py

import numpy as np
import matplotlib.pyplot as plt

name = "sine-exp"
lookup_dict = {"frequency": {"fast": 10, "slow": 1}}

def run(amplitude, frequency):
    """Deterimne if the maximum value of the sine function equals ``amplitude``"""
    x = np.arange(0, 10, .05)
    y = amplitude * np.sin(frequency * x)
    err = np.abs(max(y) - amplitude)
    plt.title("What's the maximum value of a sine wave?")
    plt.plot(x, y, label="trial data")
    plt.plot(x, amplitude * np.ones_like(x), label="expected")
    plt.legend()
    return {"main": err, "data": y}

pyproject.toml

[tool.mitosis.steps]
my_exp = ["sine_experiment:run", "sine_experiment:lookup_dict"]

Commit these changes to a repository. After installing sine_experiment as a python package, in CLI, run:

mitosis my_exp --param my_exp.frequency=slow --eval-param my_exp.amplitude=4

Mitosis will run sin_experiment.run(), saving all output as an html file in a subdirectory. It will also track the parameters and results. If you later change the variant named "slow" to set frequency=2, mitosis will raise a RuntimeError, preventing you from running a trial. If you want to run sine_experiment with a different parameter value, you need to name that variant something new. Eval parameters, like "amplitude" in the example, behave differently. Rather than being specified by lookup_dict, they are evaluated directly.

Use

Philosophically, an experiment is any time we run code with an aim to convince someone of something. As code, mitosis takes the approach that an experiment is a callable (or a sequence of callables).

Using mitosis involves registering experiments in pyproject.toml, naming interesting parameters, running experiments on the command line, and browsing results.

Registration

mitosis uses the tool.mitosis.steps table of pyproject.toml to learn what python callables are experiment steps and where to lookup named parameter values. It uses a syntax evocative of entry points:

[tool.mitosis.steps]
my_exp = ["sine_experiment:run", "sine_experiment:lookup_dict"]

Experiment steps must be callables with a dictionary return type. The returned dictionary is required to have a key "main". All but the final step in an experiment must also have a key "data" that gets passed to the first argument of the subsequent step. If the key "metrics" is present, it will display prominently in the HTML output

Developer note: Building an experiment step static type at mitosis._typing.ExpRun

CLI

The basic invocation lists the steps along with the values of any parameters for each step.

mitosis [OPTION...] step [steps...] [[-p step.lookup_param=key...]
    [-e step.eval_param=val...]]...

Some nuance:

  • --debug can be used to waive a lot of the reproducibility checks mitosis does. This arg allows you to run experiments in a dirty git repository (or no repository) and will neither save results in the experimental database, nor increment the trials counter, nor verify/lock in the definitions of any variants. It will, however, create the output notebook. It also changes the experiment log level from INFO to DEBUG.
  • lookup parameters can be nearly any python object that is pickleable. Tracking parameter values can be turned off for parameters either for something that isn't pickleable (e.g. a lambda function) or isn't important to track (e.g. which GPU to run on). This can be done with eval or lookup parameters by adding a + to the parameter, e.g. -e +jax_playground.gpu_id=1.
  • Eval parameters which are strings will need quotation marks that escape the shell (e.g. -e smoothing.kernel=\"rbf\")
  • -e and -p are short form for --eval-param and --param (lookup param).

Results

Trials are saved in trials/ (or whatever is passed after -F). Each trial has a pseudorandom bytes key, postpended to a metadata folder and an html output filename.

There are two obviously useful things to do after an experiment:

  • view the html file. python -m http.server is helpful to browse results
  • load the data with mitosis.load_trial_data()

Beyond this, the metadata mitosis keeps to disk is useful for troubleshooting or reproducing experiments, but no facility yet exists to browse or compare experiments.

API

Mitosis is primarily intended as a command line program, so mitosis --help has the syntax documentation. There is only one intentionally public part of the api: mitosis.load_trial_data().

Keywords

Machine Learning

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