Custom Search Methods

API reference

Custom Searcher Reference

Determined supports defining your own hyperparameter search algorithms and provides search runner utilities for executing them.

Tip

Remember that a Determined experiment is a set of trials, each corresponding to a point in the hyperparameter space.

To implement a custom hyperparameter tuning algorithm, subclass SearchMethod, overriding its event handler methods. If you want to achieve fault tolerance and your search method carries any state in addition to the SearcherState passed into the event handlers, also override save_method_state() and load_method_state().

To run the custom hyperparameter tuning algorithm, you can use:

Both search runners execute the custom hyperparameter tuning algorithm and start a multi-trial experiment on a Determined cluster.

The following sections explain the steps to take in order to implement and use a custom hyperparameter search algorithm. A detailed example can be found in asha_search_method.tgz.

Experiment Configuration for Custom Searcher

Specify the custom searcher type in the experiment configuration:

searcher:
  name: custom
  metric: validation_loss
  smaller_is_better: true
  unit: batches

Run Hyperparameter Search Locally

A script performing hyperparameter tuning using LocalSearchRunner may look like the following run_local_searcher.py:

import logging
from pathlib import Path
from determined import searcher


if __name__ == "__main__":
    # The content of the following directory is uploaded to Determined cluster.
    # It should include all files necessary to run the experiment (as usual).
    model_context_dir = "experiment_files"

    # Path to the .yaml file with the multi-trial experiment configuration.
    model_config = "experiment_files/config.yaml"

    # While LocalSearchRunner saves its own state and ensures invoking save() and
    # load() methods when necessary, a user is responsible for implementing
    # SearchMethod.save_method_state() and SearchMethod.load_method_state() to ensure
    # correct resumption of the SearchMethod.
    searcher_dir = Path("local_search_runner/searcher_dir")

    # Instantiate your search method, passing the necessary parameters.
    search_method = MySearchMethod(...)

    search_runner = searcher.LocalSearchRunner(search_method, searcher_dir=searcher_dir)

    experiment_id = search_runner.run(model_config, model_dir=model_context_dir)
    logging.info(f"Experiment {experiment_id} has been completed.")

To start the custom search method locally, you can use the following CLI command:

python run_local_searcher.py

Run Hyperparameter Search on a Cluster

A script to run your custom search method on a Determined cluster may look like the following run_remote_searcher.py:

import determined as det
from pathlib import Path
from determined import searcher

if __name__ == "__main__":
    model_context_dir = "experiment_files"

    model_config = "experiment_files/config.yaml"

    with det.core.init() as core_context:
        info = det.get_cluster_info()
        assert info is not None

        search_method = MySearchMethod(...)

        search_runner = searcher.RemoteSearchRunner(search_method, context=core_context)
        search_runner.run(model_config, model_dir=model_context_dir)

To start the custom search method on a cluster, you need to submit it to the master as a single-trial experiment. To this end, you can use the following CLI command:

det e create searcher_config.yaml context_dir

The custom search method runs on a Determined cluster as a single trial experiment. Configuration for the search method experiment is specified in the searcher_config.yaml and may look like this:

name: remote-searcher
entrypoint: python3 run_remote_searcher.py
searcher:
  metric: validation_error
  smaller_is_better: true
  name: single
  max_length:
    batches: 1000
max_restarts: 0