Basic Concepts

Finetuner organizes your training based on two concepts: Experiment and Run.

An Experiment defines the machine learning task you’re fine-tuning for. A Run is a piece of code that performs the Experiment with specific configurations. An Experiment contains a list of Runs, each with different configurations. For example:

• Experiment: Fine-tune a transformer on the QuoraQA dataset.

  • Run1: Use bert-based model.

  • Run2: Use sentence-transformer model.

• Experiment: Fine-tune ResNet on WILD dataset.

  • Run1: Use ResNet18 with learning rate 0.01 and SGD optimizer.

  • Run2: Use ResNet50 with learning rate 0.01 and SGD optimizer.

  • Run3: Use ResNet50 with learning rate 0.0001 and Adam optimizer.

When you start the fine-tuning job, you can declare the experiment_name and run_name like this:

import finetuner

finetuner.fit(
  ...,
  experiment_name='quora-qa-finetune',
  run_name='quora-qa-finetune-bert',
)

Please note that these two arguments are optional. If not supplied, Finetuner will use the current working directory as a default experiment_name, and generate a random run_name for you.