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Researcher Library: Hyperparameter Optimization Support

The Run:AI Researcher Library is a python library you can add to your deep learning python code. The hyperparameter optimization(HPO) support module of the library is helper library for hyperparameter optimization (HPO) experiments

Hyperparameter optimization (HPO) is the process of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. Example hyperparameters: Learning rate, Batch size, Different optimizers, number of layers.

To search for good hyperparameters, Researchers typically start a series of small runs with different hyperparameter values, let them run for a while and then examine results to decide what works best.

With the reporter module, you can externalize information such as progress, accuracy, and loss over time/epoch and more. In addition, you can externalize custom metrics of your choosing.

Getting Started


Run:AI HPO library is dependent on PyYAML. Install it using the command:

pip install pyyaml


Install the runai Python library using pip using the following command:

pip install runai

Make sure to use the correct pip installer (you might need to use pip3 for Python3)


  • Import the runai.hpo package.
import runai.hpo
  • Initialize the Run:AI HPO library with a path to a directory shared between all cluster nodes (typically using an NFS server). We recommend specifying a unique name for the experiment, the name will be used to create a sub-directory on the shared folder.
runai.hpo.init('/path/to/nfs', 'model-abcd-hpo')
  • Decide on an HPO strategy:
    • Random search - randomly pick a set of hyperparameter values
    • Grid search - pick the next set of hyperparameter values, iterating through all sets across multiple experiments
strategy = runai.hpo.Strategy.GridSearch
  • Call the Run:AI HPO library to specify a set of hyperparameters and pick a specific configuration for this experiment.
config = runai.hpo.pick(
        batch_size=[32, 64, 128],
        lr=[1, 0.1, 0.01, 0.001]),
  • Use the returned configuration in your code. For example:
optimizer = keras.optimizers.SGD(lr=config['lr'])

Metrics could be reported and saved in the experiment directory under the fule runai.yaml using You should pass the epoch number and a dictionary with metrics to be reported. For example:, metrics={ 'accuracy': 0.87 })

See Also

See hyperparameter Optimization Quickstart

Last update: November 15, 2020