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
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
and generate a random
run_name for you.