autogluon.core.scheduler¶
Example
Define a toy training function with searchable spaces:
>>> import numpy as np
>>> import autogluon.core as ag
>>> @ag.args(
... lr=ag.space.Real(1e-3, 1e-2, log=True),
... wd=ag.space.Real(1e-3, 1e-2),
... epochs=10)
>>> def train_fn(args, reporter):
... print('lr: {}, wd: {}'.format(args.lr, args.wd))
... for e in range(args.epochs):
... dummy_accuracy = 1 - np.power(1.8, -np.random.uniform(e, 2*e))
... reporter(epoch=e+1, accuracy=dummy_accuracy, lr=args.lr, wd=args.wd)
Note that epoch returned by reporter must be the number of epochs done, and start with 1. Create a scheduler and use it to run training jobs:
>>> scheduler = ag.scheduler.HyperbandScheduler(
... train_fn,
... resource={'num_cpus': 2, 'num_gpus': 0},
... num_trials=100,
... reward_attr='accuracy',
... time_attr='epoch',
... grace_period=1,
... reduction_factor=3,
... type='stopping')
>>> scheduler.run()
>>> scheduler.join_jobs()
Note that HyperbandScheduler obtains the maximum number of epochs from train_fn.args.epochs (specified by epochs=10 in the example above in the ag.args decorator). The value can also be passed as max_t to HyperbandScheduler.
Visualize the results:
>>> scheduler.get_training_curves(plot=True)
