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The minka package

Project description


Minka is ment to be a middleware between your optimization problem and a modular set of supporting tools to:

  • automatically perform hyperparameter optimization
  • store the results, parameter sets and custom data
  • compare different training runs
  • visualize the results and training processes

The aim of the project is to provide a simple as possible interface without the need to change your existing code.

One line to change it all

Could it be easier than just adding one line?

minka(yourOptimizationTask, 'configuration.json').opt(numberOfRuns)

Architecture overview



!pip install --upgrade PyYAML==4.2b4 optuna sacred python-telegram-bot dnspython loguru wandb pymongo


Define your optimization problem

Minka uses a very simple combination of a configuration json file and the actual optimization task, represented as a single class.

optimization class

optimization template:

class yourOptimizationTask:
    def __init__(self):

    def prepare(self):

    def run(self, config):
        x = config['x']
        result = (x - 2) ** 2

        evalMetrics = {
            'error': result

        logArrays = {
            'someValues': [1,2,3,4]

        return result, evalMetrics, logArrays

    def cleanup(self):

parameter configuration

content of the configuration.json file:

    "comment": "parameter types: fix, categorical, discrete_uniform, int, loguniform, uniform",
    "parameters": {
        "batchSize": {"type": "fix","values": 256},
        "epochs": {"type": "fix","values": 75},
        "x": {"type": "categorical","values": [11,22,26]}


Project details

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