We are at the end of this basic tutorial. There are many further topics we did not touch here. Some are established, but not basic, while others are still experimental. Here is an incomplete overview:
- Running many experiments in parallel: We have stressed the importance of running repetitions of experiments, as results carry quite some stochastic variation. Also, there are higher-level decisions best done by trial-and-error, which can be seen as "outer loop random search". Syne Tune offers facilities to launch many tuning experiments in parallel, as SageMaker training jobs. See README.md and launch_height_sagemaker_remotely.py for one variant, and benchmarking/benchmark_loop/README.md for another. The latter allows to run several tuning experiments sequentially on the same remote instance.
- Population-based Training: This is a popular scheduler for tuning reinforcement learning, where optimization hyperparameters like learning rate can be changed at certain points during the training. An example is at examples/launch_pbt.py. Note that checkpointing is mandatory for PBT.
- Constrained HPO: In many applications, more than a single metric play a
role. With constrained HPO, you can maximize recall subject to a constraint
on precision; minimize prediction latency subject to a constraint on accuracy;
or maximize accuracy subject to a constraint on a fairness metric. Constrained
HPO is a special case of Bayesian Optimization, where
searcher='bayesopt_constrained', and the name of the constraint metric (the constraint is feasible iff this metric is non-positive) must be given asconstraint_attrinsearch_options. More details on constrained HPO and methodology adopted in Syne Tune can be found here. - Multi-objective HPO: Another way to approach tuning problems with multiple metrics is trying to sample the Pareto frontier, i.e. identifying configurations whose performance along one metric cannot be improved without degrading performance along another. Syne Tune provides a range of methodology in this direction. An example is at examples/launch_height_moasha.py. More details on multi-objective HPO and methodology adopted in Syne Tune can be found here.