Ray Tune: How do schedulers and search algorithms interact?

问题

It seems to me that the natural way to integrate hyperband with a bayesian optimization search is to have the search algorithm determine each bracket and have the hyperband scheduler run the bracket. That is to say, the bayesian optimization search runs only once per bracket. Looking at Tune's source code for this, it's not clear to me whether the Tune library applies this strategy or not.

In particular, I want to know how the Tune library handles passing between the search algorithm and trial scheduler. For instance, how does this work if I call SkOptSearch and AsyncHyperBandScheduler (or HyperBandScheduler) together as follows:

sk_search = SkOptSearch(optimizer,
 ['group','dimensions','normalize','sampling_weights','batch_size','lr_adam','loss_weight'],
 max_concurrent=4,
 reward_attr="neg_loss",
 points_to_evaluate=current_params)

hyperband = AsyncHyperBandScheduler(
        time_attr="training_iteration",
        reward_attr="neg_loss",
        max_t=50,
        grace_period=5,
        reduction_factor=2,
        brackets=5
        )

run(Trainable_Dense,
    name='hp_search_0',
    stop={"training_iteration": 9999,
         "neg_loss": -0.2},
    num_samples=75,
    resources_per_trial={'cpu':4,'gpu':1},
    local_dir='./tune_save',
    checkpoint_freq=5,
    search_alg=sk_search,
    scheduler=hyperband,
    verbose=2,
    resume=False,
    reuse_actors=True)

Based on the source code linked above and the source code here, it seems to me that sk_search would return groups of up to 4 trials at a time, but hyperband should be querying the sk_search algorithm for N_sizeofbracket trials at a time.

回答1:

There is now a Bayesian Optimization HyperBand implementation in Tune - https://ray.readthedocs.io/en/latest/tune-searchalg.html#bohb.

For standard search algorithms and schedulers, the search algorithm currently only sees the result of a trial if it is completed.

来源：https://stackoverflow.com/questions/56083501/ray-tune-how-do-schedulers-and-search-algorithms-interact

标签

deep-learning

hyperparameters

ray