hp_tuning

AI Platform Training

Overview

To use hyperparameter tuning in your training job you must perform the following steps:

• Specify the hyperparameter tuning configuration for your training job by including a HyperparameterSpec in your TrainingInput object.
• Include the config in your training application.

Parse the command-line arguments representing the hyperparameters you want to tune, and use the values to set the hyperparameters for your training trial.

• Add your hyperparameter metric to the summary for your graph.
• Add your hyperparameter configuration information to your configuration YAML file.

Prerequisites

• Setup your project by following the instructions in the setup folder. Run variables.sh in setup folder.

   source ./variables.sh

• Download the datasets using download-taxi.sh in datasets folder.

   source ./download-taxi.sh /your_local_datasets_dir/

• Create a Python 3 virtual environment and activate it.

• Change the directory to this sample and run:

  python setup.py install
  

  Optional: You can also run:

  pip install -r requirements.txt
  

• Run code in tensorflow/structured/base/trainer/ location using the config.yaml in this folder.

Note: These instructions are used for local testing. When you submit a training job, no code will be executed on your local machine.

Documentation

Please go to the official AI platform documentation for more details.