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[under construction]

This repo is a toolkit to manage ML experiments.

It allows to organize hyperparameters and experiments configurations, load and save results, and perform hyperparameter search using some optimization algorithm.

Installation

To install the latest stable version, run the following:

pip install experiments-manager

To install the latest version, run the following:

pip install git+https://github.com/jvasso/experiments_manager

Basic usage

This is the basic structure of a project with experiments-manager:

├── main.py
├── config
│   ├── hyperparams.yaml
│   ├── exp_config.yaml
│   └── extra_config.yaml
├── ids
└── results

To run an experiment, you simply need to complete these 2 steps:

  • set your config files
  • define your experiment

Step 1: set your config files

There are 3 files in config/ which set the parameters of the experiment(s):

  • hyperparams.yaml (can also be a directory): contains all the hyperparameters of the ML model. Try multiple hyperparams values by entering a list (example below).
  • exp_config.yaml (can also be a directory): contains the parameters of the experiment (dataset, RL environment etc.). Similarly to hyperparams.yaml you can run multiple experiments by entering lists of values. The idea is that two experimental results obtained with different exp_config values are not comparable (i.e. they are different experiments).
  • extra_config.yaml: contains other parameters such as the metrics to measure, the number of seeds for each exp, the metrics for the gridsearch, the logging policy etc.
# config/hyperparams.yaml
num_layers: [4,6]
activation: ["relu", "tanh"]

This will run all configurations: {"num_layers": 4, "activation": "relu"}, {"num_layers": 4, "activation": "tanh"}, {"num_layers": 6, "activation": "relu"} etc.

# config/exp_config.yaml
dataset: "dataset1"
train_test_split: 0.8
# config/extra_config.yaml
log_frequency: 5
metrics: {"train_loss", "valid_loss"} # metrics measured at each experiment
gridsearch_metric: "valid_loss"       # metric used for the gridsearch (it will drive the search in the hyperparameter space)
criteria_complete_result: {"seed":10} # criteria that must be fulfilled to consider one experiment as complete (--> as many "trials" as values: here, 10 seeds will be tried)

Step 2: define your experiment

To run your own experiment, you just need to subclass the Experiment base class, which contains one mandatory method: run_experiment.

from experiments_manager.experiment import Experiment
from experiments_manager.config import ConfigManager
from experiments_manager.experiment import ExperimentManager

class CustomExperiment(Experiment):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def run_experiment(self, trial_params):
        seed = trial_params["seed"]

        num_layers = self.config.hyperparams.num_layers
        activation = self.config.hyperparams.activation
        loss = self.config.exp_config.loss

        model = MyModel(num_layers=num_layers, activation=activation)
        results = model.train(loss=loss)
        return result

configManager = ConfigManager()
experimentManager = ExperimentManager(configManager, experiment_cls=CustomExperiment)
experimentManager.run_experiments()
results = experimentManager.compare_results(metrics=["valid_loss"])