Optuna

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Optuna is an automatic hyperparameter optimization (HPO) software framework, particularly designed for machine learning.

Please refer to the Optuna Documentation for a definition of terms, tutorial, API, etc.

Using Optuna on Compute Canada

Here is a sketch of an SBATCH script for an HPO using Optuna:

File : hpo_with_optuna.sh

#!/bin/bash 
#SBATCH -A def-account
#SBATCH --array 1-N%M   # This will launch N jobs, but only allow M to run in parallel
#SBATCH --time TIME     # Each of the N jobs will have the time limit defined in here.
... other SBATCH arguments ...

# Each trial in the study will be run in a separate job.
# The Optuna study_name has to be set to be able to continue an existing study.
OPTUNA_STUDY_NAME=my_optuna_study1

# Specify a path in your home, or on project.
OPTUNA_DB=$HOME/${OPTUNA_STUDY_NAME}.db

# Launch your script, giving it as arguments the database file and the study name
python train.py --optuna-db $OPTUNA_DB --optuna-study-name $OPTUNA_STUDY_NAME

In train.py, create and launch the Optuna study like the following. For the rest of the code, see the Optuna Documentation.

# args.optuna_db and args.optuna_study_name are command line arguments

study = optuna.create_study(
    storage='sqlite:///' + args.optuna_db,
    study_name=args.optuna_study_name,
    load_if_exists=True
)
...
study.optimize(objective, n_trials=1)  # Only execute a single trial at a time, to avoid wasting compute