0.2.6 (#126)

* 0.2.5 setup.py (#111)

* [WIP] Attempts to Fix Memory Error  (#112)

* Add Website Badge in README.md, apply timeout in search function in search.py

* Add timeout in maximize_acq function in search.py

* Update unit test to allow timeout to raise TimeoutError

* Add unit test for timeout resume

* Remove TimeoutError from expectation

* Check Timeout exception in search() in search.py

* 0.2.5 setup.py (#110)

* Prevent gpu memory copy to main process after train() finished

* Cast loss from tensor to float

* Add pass() in MockProcess

* [MRG] Search Space limited to avoid out of memory (#121)

* limited the search space

* limited the search space

* reduce search space

* test added

* [MRG]Pytorch mp (#124)

* Change multiprcoessing to torch.multiprocessing

* Replace multiprocessing.Pool with torch.multiprocessing.Pool in tests

* 0.2.6 (#125)

* new release

* auto deploy

.travis.yml

@@ -1,11 +1,22 @@
 language: python
 python:
-  - "3.6"
+- '3.6'
 before_install:
-  - sudo apt-get install graphviz
+- sudo apt-get install graphviz
 install:
-  - pip install -r requirements.txt --quiet
+- pip install -r requirements.txt --quiet
 script:
-  - pytest tests --cov=autokeras
+- pytest tests --cov=autokeras
 after_success:
-  - pip install python-coveralls && coveralls
+- pip install python-coveralls && coveralls
+deploy:
+  provider: pypi
+  user: "jhfjhfj1"
+  password:
+    secure: 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
+  on:
+    tags: true
+    branch: master
+    repo: jhfjhfj1/autokeras
+    python: 3.6
+

autokeras/bayesian.py

@@ -245,15 +245,15 @@ def optimize_acq(self, model_ids, descriptors, timeout):
         t_min = self.t_min
         alpha = 0.9
         opt_acq = self._get_init_opt_acq_value()
-        remaining_time = timeout - (time.time() - start_time)
+        remaining_time = timeout
         while not pq.empty() and t > t_min and remaining_time > 0:
             elem = pq.get()
             if self.metric.higher_better():
-                temp_exp = min((elem.metric_value - opt_acq) / t, 709.0)
+                temp_exp = min((elem.metric_value - opt_acq) / t, 1.0)
             else:
-                temp_exp = min((opt_acq - elem.metric_value) / t, 709.0)
+                temp_exp = min((opt_acq - elem.metric_value) / t, 1.0)
             ap = math.exp(temp_exp)
-            if ap > random.uniform(0, 1):
+            if ap >= random.uniform(0, 1):
                 for temp_graph in transform(elem.graph):
                     if contain(descriptors, temp_graph.extract_descriptor()):
                         continue

autokeras/constant.py

@@ -10,8 +10,8 @@ class Constant:
     KERNEL_LAMBDA = 0.1
     T_MIN = 0.0001
     N_NEIGHBOURS = 8
-    # T_MIN = 0.8
-    # N_NEIGHBOURS = 1
+    MAX_MODEL_WIDTH = 2048
+    MAX_MODEL_DEPTH = 15
 
     # Model Defaults
 

autokeras/net_transformer.py

@@ -5,11 +5,16 @@
 from autokeras.graph import NetworkDescriptor
 
 from autokeras.constant import Constant
-from autokeras.layers import is_layer
+from autokeras.layers import is_layer, layer_width
 
 
 def to_wider_graph(graph):
     weighted_layer_ids = graph.wide_layer_ids()
+    weighted_layer_ids = list(filter(lambda x: layer_width(graph.layer_list[x]) * 2 <= Constant.MAX_MODEL_WIDTH,
+                                     weighted_layer_ids))
+
+    if len(weighted_layer_ids) == 0:
+        return None
     # n_wider_layer = randint(1, len(weighted_layer_ids))
     # wider_layers = sample(weighted_layer_ids, n_wider_layer)
     wider_layers = sample(weighted_layer_ids, 1)
@@ -56,6 +61,9 @@ def to_skip_connection_graph(graph):
 
 def to_deeper_graph(graph):
     weighted_layer_ids = graph.deep_layer_ids()
+    if len(weighted_layer_ids) >= Constant.MAX_MODEL_DEPTH:
+        return None
+
     deeper_layer_ids = sample(weighted_layer_ids, 1)
     # n_deeper_layer = randint(1, len(weighted_layer_ids))
     # deeper_layer_ids = sample(weighted_layer_ids, n_deeper_layer)
@@ -87,6 +95,7 @@ def transform(graph):
             graphs.append(to_wider_graph(deepcopy(graph)))
         elif a == 2:
             graphs.append(to_skip_connection_graph(deepcopy(graph)))
+    graphs = list(filter(lambda x: x, graphs))
     return list(filter(lambda x: legal_graph(x), graphs))
 
 

autokeras/search.py

@@ -9,7 +9,7 @@
 from autokeras.net_transformer import default_transform
 from autokeras.utils import ModelTrainer, pickle_to_file, pickle_from_file
 
-import multiprocessing
+import torch.multiprocessing as mp
 
 
 class Searcher:
@@ -161,8 +161,8 @@ def search(self, train_data, test_data, timeout=60 * 60 * 24):
         graph, father_id, model_id = self.training_queue.pop(0)
         if self.verbose:
             print('Training model ', model_id)
-        multiprocessing.set_start_method('spawn', force=True)
-        pool = multiprocessing.Pool(1)
+        mp.set_start_method('spawn', force=True)
+        pool = mp.Pool(1)
         train_results = pool.map_async(train, [(graph, train_data, test_data, self.trainer_args,
                                                 os.path.join(self.path, str(model_id) + '.png'),
                                                 self.metric, self.loss, self.verbose)])
@@ -189,13 +189,12 @@ def search(self, train_data, test_data, timeout=60 * 60 * 24):
                 metric_value, loss, graph = train_results.get(timeout=remaining_time)[0]
             else:
                 raise TimeoutError
-        except (multiprocessing.TimeoutError, TimeoutError) as e:
+        except (mp.TimeoutError, TimeoutError) as e:
             raise TimeoutError from e
         finally:
             # terminate and join the subprocess to prevent any resource leak
-            pool.terminate()
+            pool.close()
             pool.join()
-
         self.add_model(metric_value, loss, graph, model_id)
         self.search_tree.add_child(father_id, model_id)
         self.bo.fit(self.x_queue, self.y_queue)
@@ -252,14 +251,14 @@ def train(args):
     model = graph.produce_model()
     # if path is not None:
     #     plot_model(model, to_file=path, show_shapes=True)
-    loss, metric_value = ModelTrainer(model,
+    loss, mertic_value = ModelTrainer(model,
                                       train_data,
                                       test_data,
                                       metric,
                                       loss,
                                       verbose).train_model(**trainer_args)
     model.set_weight_to_graph()
-    return metric_value, loss, model.graph
+    return mertic_value, loss, model.graph
 
 
 def same_graph(des1, des2):

autokeras/utils.py

@@ -153,7 +153,8 @@ def _test(self):
             for batch_idx, (inputs, targets) in enumerate(deepcopy(loader)):
                 inputs, targets = inputs.to(self.device), targets.to(self.device)
                 outputs = self.model(inputs)
-                test_loss += self.loss_function(outputs, targets)
+                # cast tensor to float
+                test_loss += float(self.loss_function(outputs, targets))
 
                 all_predicted.append(outputs.cpu().numpy())
                 all_targets.append(targets.cpu().numpy())

setup.py

@@ -5,12 +5,12 @@
     packages=['autokeras'],  # this must be the same as the name above
     install_requires=['torch==0.4.1', 'torchvision==0.2.1', 'numpy==1.14.5', 'keras==2.2.2', 'scikit-learn==0.19.1',
                       'tensorflow==1.8.0'],
-    version='0.2.5',
+    version='0.2.6',
     description='AutoML for deep learning',
     author='Haifeng Jin',
     author_email='[email protected]',
     url='http://autokeras.com',
-    download_url='https://github.com/jhfjhfj1/autokeras/archive/0.2.5.tar.gz',
+    download_url='https://github.com/jhfjhfj1/autokeras/archive/0.2.6.tar.gz',
     keywords=['automl'],  # arbitrary keywords
     classifiers=[]
 )

tests/common.py

@@ -243,8 +243,10 @@ def get(self, timeout=None):
     def terminate(self):
         pass
 
+    def close(self):
+        pass
+
 
 def simple_transform(graph):
     graph.to_wider_model(5, 64)
     return [deepcopy(graph)]
-

tests/test_image_supervised.py

@@ -33,7 +33,7 @@ def test_x_float_exception():
     assert str(info.value) == 'x_train should only contain numerical data.'
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_fit_predict(_):
     Constant.MAX_ITER_NUM = 1
@@ -52,11 +52,11 @@ def test_fit_predict(_):
     clean_dir(path)
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 def test_timeout():
     # Constant.MAX_MODEL_NUM = 4
     Constant.SEARCH_MAX_ITER = 1000
-    Constant.T_MIN = 0.8
+    Constant.T_MIN = 0.0001
     Constant.DATA_AUGMENTATION = False
     path = 'tests/resources/temp'
     clean_dir(path)
@@ -68,7 +68,7 @@ def test_timeout():
     clean_dir(path)
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_timeout_resume(_):
     Constant.MAX_ITER_NUM = 1
@@ -99,7 +99,7 @@ def test_timeout_resume(_):
     clean_dir(path)
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.bayesian.transform', side_effect=simple_transform)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_final_fit(_, _1):
@@ -123,7 +123,7 @@ def test_final_fit(_, _1):
     clean_dir(path)
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_save_continue(_):
     Constant.MAX_ITER_NUM = 1
@@ -156,7 +156,7 @@ def test_save_continue(_):
     clean_dir(path)
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.bayesian.transform', side_effect=simple_transform)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_fit_csv_file(_, _1):
@@ -182,7 +182,7 @@ def test_init_image_classifier_with_none_path(_):
     assert clf.path == 'dummy_path/'
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_fit_predict_regression(_):
     Constant.MAX_ITER_NUM = 1

tests/test_net_transformer.py

@@ -53,3 +53,11 @@ def test_default_transform():
     model(torch.Tensor(get_conv_data()))
     assert len(graphs) == 1
     assert len(graphs[0].layer_list) == 43
+
+
+def test_search_space_limit():
+    graph = CnnGenerator(10, (32, 32, 3)).generate(model_len=3, model_width=2048)
+    assert to_wider_graph(graph) is None
+
+    graph = CnnGenerator(10, (32, 32, 3)).generate(model_len=14)
+    assert to_deeper_graph(graph) is None

tests/test_search.py

@@ -14,7 +14,7 @@ def mock_train(**_):
     return 1, 0
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.bayesian.transform', side_effect=simple_transform)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_bayesian_searcher(_, _1):
@@ -38,7 +38,7 @@ def test_search_tree():
     assert len(tree.adj_list) == 3
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.bayesian.transform', side_effect=simple_transform)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_export_json(_, _1):
@@ -66,7 +66,7 @@ def test_graph_duplicate():
     assert not same_graph(get_concat_skip_model().extract_descriptor(), get_add_skip_model().extract_descriptor())
 
 
-@patch('multiprocessing.Pool', new=MockProcess)
+@patch('torch.multiprocessing.Pool', new=MockProcess)
 @patch('autokeras.bayesian.transform', side_effect=simple_transform)
 @patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
 def test_max_acq(_, _1):