Before going through this section, make sure you have installed Schedy.
Schedy is the combination of three tools:
- A Python API.
- An online dashboard, that will help you visualize your experiments.
- A command line tool,
schedy, that can help you with the basic setup and monitoring of your experiments.
Using the command line tool is totally optional. It simply wraps up the most common operations you would perform with the Python API.
For example, running
schedy add MyTask manual in the command line tool is
the same as running the following Python script:
import schedy db = schedy.SchedyDB() experiment = schedy.ManualSearch('MyTask') db.add_experiment(experiment)
Database, experiments and jobs¶
When using Schedy, you store all your hyperparameters and results in the Schedy
database, represented by the SchedyDB object. You can access these
experiments and jobs from any workstation with your credentials (the ones you
schedy gen-token or on the website).
An experiment is a topic, a single task, for which you try to find the best configuration of hyperparameters. Examples of experiments are:
- Trying to fit a linear regression on your dataset
- Trying to train a Random Forest to predict tomorrow’s temperature
- Trying to classify objects in a picture using an ResNet-50
A job is a trial for an experiment. A job tries to fulfill the task using a set of hyperparameters. Once it has completed its task, it reports how well it performed, by updating its results. (Actually, a job can also report results while it is running, e.g. the training loss associated to each epoch while training a neural network.)