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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 a 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

Prerequisites

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

pip install pyyaml

Installing

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)

Usage

  • 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. To do so, we recommend using the environment variables JOB_NAME and JOB_UUID which are injected to the container by Run:ai.
hpo_root = '/path/to/nfs'
hpo_experiment = '%s_%s' % (os.getenv('JOB_NAME'), os.getenv('JOB_UUID'))

runai.hpo.init(hpo_root, hpo_experiment)
  • 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(
    grid=dict(
        batch_size=[32, 64, 128],
        lr=[1, 0.1, 0.01, 0.001]),
    strategy=strategy)
  • 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 runai.hpo.report. You should pass the epoch number and a dictionary with metrics to be reported. For example:

runai.hpo.report(epoch=5, metrics={ 'accuracy': 0.87 })

See Also

Last update: March 23, 2022