def testAlternateMetrics(self): """Checking that alternate metrics will pass.""" def result2(t, rew): return dict(time_total_s=t, neg_mean_loss=rew) sched = HyperBandScheduler(time_attr='time_total_s', reward_attr='neg_mean_loss') stats = self.default_statistics() for i in range(stats["max_trials"]): t = Trial("__fake") sched.on_trial_add(None, t) runner = _MockTrialRunner(sched) big_bracket = sched._hyperbands[0][-1] for trl in big_bracket.current_trials(): runner._launch_trial(trl) current_length = len(big_bracket.current_trials()) # Provides results from 0 to 8 in order, keeping the last one running for i, trl in enumerate(big_bracket.current_trials()): action = sched.on_trial_result(runner, trl, result2(1, i)) runner.process_action(trl, action) new_length = len(big_bracket.current_trials()) self.assertEqual(action, TrialScheduler.CONTINUE) self.assertEqual(new_length, self.downscale(current_length, sched))
def testAlternateMetrics(self): """Checking that alternate metrics will pass.""" def result2(t, rew): return TrainingResult(time_total_s=t, neg_mean_loss=rew) sched = HyperBandScheduler(time_attr='time_total_s', reward_attr='neg_mean_loss') stats = self.default_statistics() for i in range(stats["max_trials"]): t = Trial("__fake") sched.on_trial_add(None, t) runner = _MockTrialRunner() big_bracket = sched._hyperbands[0][-1] for trl in big_bracket.current_trials(): runner._launch_trial(trl) current_length = len(big_bracket.current_trials()) # Provides results from 0 to 8 in order, keeping the last one running for i, trl in enumerate(big_bracket.current_trials()): status = sched.on_trial_result(runner, trl, result2(1, i)) if status == TrialScheduler.CONTINUE: continue elif status == TrialScheduler.PAUSE: runner._pause_trial(trl) elif status == TrialScheduler.STOP: self.assertNotEqual(trl.status, Trial.TERMINATED) self.stopTrial(trl, runner) new_length = len(big_bracket.current_trials()) self.assertEqual(status, TrialScheduler.CONTINUE) self.assertEqual(new_length, self.downscale(current_length, sched))
def testAlternateMetrics(self): """Checking that alternate metrics will pass.""" def result2(t, rew): return TrainingResult(time_total_s=t, neg_mean_loss=rew) sched = HyperBandScheduler( time_attr='time_total_s', reward_attr='neg_mean_loss') stats = self.default_statistics() for i in range(stats["max_trials"]): t = Trial("__fake") sched.on_trial_add(None, t) runner = _MockTrialRunner(sched) big_bracket = sched._hyperbands[0][-1] for trl in big_bracket.current_trials(): runner._launch_trial(trl) current_length = len(big_bracket.current_trials()) # Provides results from 0 to 8 in order, keeping the last one running for i, trl in enumerate(big_bracket.current_trials()): action = sched.on_trial_result(runner, trl, result2(1, i)) runner.process_action(trl, action) new_length = len(big_bracket.current_trials()) self.assertEqual(action, TrialScheduler.CONTINUE) self.assertEqual(new_length, self.downscale(current_length, sched))
def testConfigSameEtaSmall(self): sched = HyperBandScheduler(max_t=1) i = 0 while len(sched._hyperbands) < 2: t = Trial("__fake") sched.on_trial_add(None, t) i += 1 self.assertEqual(len(sched._hyperbands[0]), 5) self.assertTrue(all(v is None for v in sched._hyperbands[0][1:]))
def default_statistics(self): """Default statistics for HyperBand""" sched = HyperBandScheduler() res = { str(s): {"n": sched._get_n0(s), "r": sched._get_r0(s)} for s in range(sched._s_max_1) } res["max_trials"] = sum(v["n"] for v in res.values()) res["brack_count"] = sched._s_max_1 res["s_max"] = sched._s_max_1 - 1 return res
def schedulerSetup(self, num_trials): """Setup a scheduler and Runner with max Iter = 9 Bracketing is placed as follows: (3, 9); (5, 3) -> (2, 9); (9, 1) -> (3, 3) -> (1, 9); """ sched = HyperBandScheduler(9, eta=3) for i in range(num_trials): t = Trial("t%d" % i, "__fake") sched.on_trial_add(None, t) runner = _MockTrialRunner() return sched, runner
def schedulerSetup(self, num_trials): """Setup a scheduler and Runner with max Iter = 9 Bracketing is placed as follows: (5, 81); (8, 27) -> (3, 81); (15, 9) -> (5, 27) -> (2, 81); (34, 3) -> (12, 9) -> (4, 27) -> (2, 81); (81, 1) -> (27, 3) -> (9, 9) -> (3, 27) -> (1, 81);""" sched = HyperBandScheduler() for i in range(num_trials): t = Trial("__fake") sched.on_trial_add(None, t) runner = _MockTrialRunner() return sched, runner
def schedulerSetup(self, num_trials): """Setup a scheduler and Runner with max Iter = 9 Bracketing is placed as follows: (5, 81); (8, 27) -> (3, 54); (15, 9) -> (5, 27) -> (2, 45); (34, 3) -> (12, 9) -> (4, 27) -> (2, 42); (81, 1) -> (27, 3) -> (9, 9) -> (3, 27) -> (1, 41);""" sched = HyperBandScheduler() for i in range(num_trials): t = Trial("__fake") sched.on_trial_add(None, t) runner = _MockTrialRunner(sched) return sched, runner
def testConfigSameEta(self): sched = HyperBandScheduler() i = 0 while not sched._cur_band_filled(): t = Trial("__fake") sched.on_trial_add(None, t) i += 1 self.assertEqual(len(sched._hyperbands[0]), 5) self.assertEqual(sched._hyperbands[0][0]._n, 5) self.assertEqual(sched._hyperbands[0][0]._r, 81) self.assertEqual(sched._hyperbands[0][-1]._n, 81) self.assertEqual(sched._hyperbands[0][-1]._r, 1) sched = HyperBandScheduler(max_t=810) i = 0 while not sched._cur_band_filled(): t = Trial("__fake") sched.on_trial_add(None, t) i += 1 self.assertEqual(len(sched._hyperbands[0]), 5) self.assertEqual(sched._hyperbands[0][0]._n, 5) self.assertEqual(sched._hyperbands[0][0]._r, 810) self.assertEqual(sched._hyperbands[0][-1]._n, 81) self.assertEqual(sched._hyperbands[0][-1]._r, 10)
parser = argparse.ArgumentParser() parser.add_argument('--smoke-test', action='store_true', help='Finish quickly for testing') args, _ = parser.parse_known_args() register_trainable("my_class", TrainMNIST) mnist_spec = { 'run': 'my_class', 'stop': { 'mean_accuracy': 0.99, 'time_total_s': 600, }, 'config': { 'learning_rate': lambda spec: 10**np.random.uniform(-5, -3), 'activation': grid_search(['relu', 'elu', 'tanh']), }, "repeat": 10, } if args.smoke_test: mnist_spec['stop']['training_iteration'] = 2 mnist_spec['repeat'] = 2 ray.init() hyperband = HyperBandScheduler(time_attr="training_iteration", reward_attr="mean_accuracy", max_t=10) run_experiments({'mnist_hyperband_test': mnist_spec}, scheduler=hyperband)
#shutil.rmtree("/home/edward/ray_results/my_Experiment/") ray.init(num_gpus=3, num_cpus=10) data_train = pin_in_object_store( TensorFactDataset(csv_file_serie="complete_tensor_train1.csv", cov_path="complete_covariates")) data_val = pin_in_object_store( TensorFactDataset(csv_file_serie="complete_tensor_val1.csv", cov_path="complete_covariates")) tune.register_trainable("my_class", train_class) hyperband = HyperBandScheduler(time_attr="timesteps_total", reward_attr="neg_mean_loss", max_t=100) exp = { 'run': "my_class", 'trial_resources': { "gpu": 1 }, 'repeat': 50, 'stop': { "training_iteration": 80 }, 'config': { "L2": lambda spec: 10**(-4 * random.random() - 6), "L2_w": lambda spec: 10**(-4 * random.random() - 6), }
return path def _restore(self, checkpoint_path): with open(checkpoint_path) as f: self.timestep = json.loads(f.read())["timestep"] register_trainable("my_class", MyTrainableClass) if __name__ == "__main__": ray.init() # Hyperband early stopping, configured with `episode_reward_mean` as the # objective and `timesteps_total` as the time unit. hyperband = HyperBandScheduler(time_attr="timesteps_total", reward_attr="episode_reward_mean", max_t=100) run_experiments( { "hyperband_test": { "run": "my_class", "repeat": 100, "resources": { "cpu": 1, "gpu": 0 }, "config": { "width": lambda spec: 10 + int(90 * random.random()), "height": lambda spec: int(100 * random.random()), },
parser = argparse.ArgumentParser() parser.add_argument('--smoke-test', action='store_true', help='Finish quickly for testing') args, _ = parser.parse_known_args() register_trainable("my_class", TrainMNIST) mnist_spec = { 'run': 'my_class', 'stop': { 'mean_accuracy': 0.99, 'time_total_s': 600, }, 'config': { 'learning_rate': lambda spec: 10**np.random.uniform(-5, -3), 'activation': grid_search(['relu', 'elu', 'tanh']), }, "repeat": 10, } if args.smoke_test: mnist_spec['stop']['training_iteration'] = 2 mnist_spec['repeat'] = 2 ray.init() hyperband = HyperBandScheduler(time_attr="timesteps_total", reward_attr="mean_accuracy", max_t=100) run_experiments({'mnist_hyperband_test': mnist_spec}, scheduler=hyperband)