Merge pull request #3155 from microsoft/dev-retiarii

[Do NOT Squash] Merge retiarii dev branch to master

test/retiarii_test/darts/test.py

+import json
+import os
+import sys
+import torch
+from pathlib import Path
+
+from nni.retiarii.experiment import RetiariiExperiment, RetiariiExeConfig
+from nni.retiarii.strategies import TPEStrategy
+from nni.retiarii.trainer import PyTorchImageClassificationTrainer
+
+from darts_model import CNN
+
+if __name__ == '__main__':
+    base_model = CNN(32, 3, 16, 10, 8)
+    trainer = PyTorchImageClassificationTrainer(base_model, dataset_cls="CIFAR10",
+            dataset_kwargs={"root": "data/cifar10", "download": True},
+            dataloader_kwargs={"batch_size": 32},
+            optimizer_kwargs={"lr": 1e-3},
+            trainer_kwargs={"max_epochs": 1})
+
+    simple_startegy = TPEStrategy()
+
+    exp = RetiariiExperiment(base_model, trainer, [], simple_startegy)
+
+    exp_config = RetiariiExeConfig('local')
+    exp_config.experiment_name = 'darts_search'
+    exp_config.trial_concurrency = 2
+    exp_config.max_trial_number = 10
+    exp_config.trial_gpu_number = 1
+    exp_config.training_service.use_active_gpu = True
+    exp_config.training_service.gpu_indices = [1, 2]
+
+    exp.run(exp_config, 8081, debug=True)

test/retiarii_test/simple_strategy.py

+import json
+import logging
+import random
+import os
+
+from nni.retiarii import Model, submit_models, wait_models
+from nni.retiarii.strategy import BaseStrategy
+from nni.retiarii import Sampler
+
+
+_logger = logging.getLogger(__name__)
+
+class RandomSampler(Sampler):
+    def choice(self, candidates, mutator, model, index):
+        return random.choice(candidates)
+
+class SimpleStrategy(BaseStrategy):
+    def __init__(self):
+        self.name = ''
+
+    def run(self, base_model, applied_mutators, trainer):
+        try:
+            _logger.info('stargety start...')
+            while True:
+                model = base_model
+                _logger.info('apply mutators...')
+                _logger.info('mutators: {}'.format(applied_mutators))
+                random_sampler = RandomSampler()
+                for mutator in applied_mutators:
+                    _logger.info('mutate model...')
+                    mutator.bind_sampler(random_sampler)
+                    model = mutator.apply(model)
+                # get and apply training approach
+                _logger.info('apply training approach...')
+                model.apply_trainer(trainer['modulename'], trainer['args'])
+                # run models
+                submit_models(model)
+                wait_models(model)
+                _logger.info('Strategy says:', model.metric)
+        except Exception as e:
+            _logger.error(logging.exception('message'))