evalpy 0.0.11

A lightweight framework for experiment logging and automatic visualization

evalpy

A lightweight framework for experiment logging and automatic visualization

Evalpy aims at researchers that want a fast and efficient framework to log their experiment configurations alongside the results. This includes an interface to both log the parameters and metrics of any single run as well as the progression during a run in a time series manner.

The second part of evalpy includes the GUI which is provided within the package. To start the GUI simply activate the environment in which you installed evalpy in a console and execute the following:

evalpy run

Quickstart

The intended usage involves the following steps:

• Declaring the project root, a file path
• Declaring the project name, the name of the project directory
• Starting a run with an experiment name
• In the run one can log one time the parameters and metrics and do a step logging for the run progression

A minimal usage outline is as follows

import evalpy


evalpy.set_project('my_first_project_path', 'my_project_folder_name')
with evalpy.start_run('experiment_name'):
    for log_step_stuff in model_training():
        evalpy.log_run_step(log_step_stuff, step_forward=True)  
    evalpy.log_run_entries(model_parameters_and_metrics)  # both methods expect a dict as input