Launch HPO Experiment with Python Backend

The Python backend does not need a separate training script.

Population-Based Training (PBT)

This launcher script is using the following pbt_example.py training script:

For this toy example, PBT is run with a population size of 2, so only two parallel workers are needed. In order to use PBT competitively, choose the SageMaker backend. Note that PBT requires your training script to support checkpointing.

Visualize Tuning Progress with Tensorboard

Requirements:

  • Needs tensorboardX to be installed: pip install tensorboardX.

Makes use of train_height.py.

Tensorboard visualization works by using a callback, for example TensorboardCallback, which is passed to the Tuner. In order to visualize other metrics, you may have to modify this callback.

Multi-objective Asynchronous Successive Halving (MOASHA)

This launcher script is using the following mo_artificial.py training script:

Transfer Tuning on NASBench-201

Requirements:

  • Syne Tune dependencies blackbox-repository need to be installed.

  • Needs nasbench201 blackbox to be downloaded and preprocessed. This can take quite a while when done for the first time

  • If AWS SageMaker is used or an S3 bucket is accessible, the blackbox files are uploaded to your S3 bucket

In this example, we use the simulator backend with the NASBench-201 blackbox. It serves as a simple demonstration how evaluations from related tasks can be used to speed up HPO.

Plot Results of Tuning Experiment

Requirements:

  • Needs matplotlib to be installed: pip install matplotlib. If you installed Syne Tune with visual or extra, this dependence is included.

Makes use of train_height.py.

Resume a Tuning Job