release v0.113

docs/index.md

@@ -1,5 +1,3 @@
-# PYTORCH-LIGHTNING DOCUMENTATION
-
 ###### New project Quick Start    
 To start a new project define these two files.   
 

examples/new_project_templates/trainer_cpu_template.py

@@ -41,7 +41,6 @@ def main(hparams):
     model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
     checkpoint = ModelCheckpoint(
         filepath=model_save_path,
-        save_function=None,
         save_best_only=True,
         verbose=True,
         monitor='val_acc',

examples/new_project_templates/trainer_gpu_cluster_template.py

@@ -17,7 +17,7 @@ np.random.seed(SEED)
 # ---------------------
 # DEFINE MODEL HERE
 # ---------------------
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from lightning_module_template import LightningTemplateModel
 # ---------------------
 
 AVAILABLE_MODELS = {
@@ -58,9 +58,7 @@ def main(hparams, cluster, results_dict):
     log_dir = os.path.dirname(os.path.realpath(__file__))
     exp = Experiment(
         name='test_tube_exp',
-        debug=True,
         save_dir=log_dir,
-        version=0,
         autosave=False,
         description='test demo'
     )
@@ -84,7 +82,6 @@ def main(hparams, cluster, results_dict):
     model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
     checkpoint = ModelCheckpoint(
         filepath=model_save_path,
-        save_function=None,
         save_best_only=True,
         verbose=True,
         monitor=hparams.model_save_monitor_value,
@@ -102,7 +99,7 @@ def main(hparams, cluster, results_dict):
         cluster=cluster,
         checkpoint_callback=checkpoint,
         early_stop_callback=early_stop,
-        gpus=gpu_list
+        gpus=gpu_list,
     )
 
     # train model

pytorch_lightning/models/trainer.py

@@ -128,13 +128,15 @@ class Trainer(TrainerIO):
     def __tng_tqdm_dic(self):
         tqdm_dic = {
             'tng_loss': '{0:.3f}'.format(self.avg_loss),
-            'gpu': '{}'.format(self.current_gpu_name),
             'v_nb': '{}'.format(self.experiment.version),
             'epoch': '{}'.format(self.current_epoch),
             'batch_nb':'{}'.format(self.batch_nb),
         }
         tqdm_dic.update(self.tqdm_metrics)
 
+        if self.on_gpu:
+            tqdm_dic['gpu'] = '{}'.format(self.current_gpu_name)
+
         return tqdm_dic
 
     def __layout_bookeeping(self, model):
@@ -244,7 +246,9 @@ class Trainer(TrainerIO):
     # -----------------------------
     def fit(self, model):
 
+        # give model convenience properties
         model.trainer = self
+        model.experiment = self.experiment
 
         # transfer data loaders from model
         self.__get_dataloaders(model)
@@ -369,7 +373,8 @@ class Trainer(TrainerIO):
                         metrics.update(grad_norm_dic)
 
                     # log metrics
-                    self.experiment.log(metrics)
+                    scalar_metrics = self.__metrics_to_scalars(metrics, blacklist=self.__log_vals_blacklist())
+                    self.experiment.log(scalar_metrics, global_step=self.global_step)
                     self.experiment.save()
 
                 # hook
@@ -396,6 +401,24 @@ class Trainer(TrainerIO):
                 if stop:
                     return
 
+    def __metrics_to_scalars(self, metrics, blacklist=[]):
+        new_metrics = {}
+        for k, v in metrics.items():
+            if type(v) is torch.Tensor:
+                v = v.item()
+
+            if type(v) is dict:
+                v = self.__metrics_to_scalars(v)
+
+            if k not in blacklist:
+                new_metrics[k] = float(v)
+
+        return new_metrics
+
+    def __log_vals_blacklist(self):
+        """avoid logging some vals lightning uses to maintain state"""
+        blacklist = {'batch_nb', 'v_nb', 'epoch', 'gpu'}
+        return blacklist
 
     def __run_tng_batch(self, data_batch, batch_nb):
         if data_batch is None:

pytorch_lightning/root_module/model_saving.py

@@ -107,6 +107,9 @@ class TrainerIO(object):
         # save exp to make sure we get all the metrics
         experiment.save()
 
+        # close experiment to avoid issues
+        experiment.close()
+
         ckpt_number = self.max_ckpt_in_folder(folderpath) + 1
 
         if not os.path.exists(folderpath):

pytorch_lightning/root_module/root_module.py

@@ -26,6 +26,7 @@ class LightningModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
         self.gradient_clip = hparams.gradient_clip
         self.trainer = None
         self.from_lightning = True
+        self.experiment = None
 
         # track if gpu was requested for checkpointing
         self.on_gpu = False

requirements.txt

@@ -1,27 +1,44 @@
-atomicwrites==1.2.1
-attrs==18.2.0
-certifi==2018.11.29
-cffi==1.11.5
+absl-py==0.7.1
+astor==0.8.0
+bleach==3.1.0
+certifi==2019.6.16
+cffi==1.12.3
+chardet==3.0.4
+docutils==0.14
+gast==0.2.2
+google-pasta==0.1.7
+grpcio==1.21.1
 h5py==2.9.0
-imageio==2.4.1
-mkl-fft==1.0.6
+idna==2.8
+imageio==2.5.0
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+Markdown==3.1.1
+mkl-fft==1.0.12
 mkl-random==1.0.2
-more-itertools==5.0.0
-numpy==1.15.4
+numpy==1.16.4
 olefile==0.46
-pandas==0.23.4
-Pillow==5.3.0
-pluggy==0.8.0
-py==1.7.0
+pandas==0.24.2
+Pillow==6.0.0
+pkginfo==1.5.0.1
+protobuf==3.8.0
 pycparser==2.19
-pytest==4.0.2
-python-dateutil==2.7.5
-pytz==2018.7
-scikit-learn==0.20.2
-scipy==1.2.0
+Pygments==2.4.1
+python-dateutil==2.8.0
+pytz==2019.1
+readme-renderer==24.0
+requests==2.22.0
+requests-toolbelt==0.9.1
 six==1.12.0
-sklearn==0.0
-test-tube==0.6282
-torch==1.0.0
-torchvision==0.2.1
-tqdm==4.28.1
+tensorboard==1.14.0
+tensorboardX==1.7
+tensorflow==1.14.0
+tensorflow-estimator==1.14.0
+termcolor==1.1.0
+test-tube==0.643
+tqdm==4.32.1
+twine==1.13.0
+urllib3==1.25.3
+webencodings==0.5.1
+Werkzeug==0.15.4
+wrapt==1.11.2

setup.py

@@ -7,7 +7,7 @@ from setuptools import setup, find_packages
 # http://blog.ionelmc.ro/2014/05/25/python-packaging/
 setup(
     name="pytorch-lightning",
-    version='0.112',
+    version='0.113',
     description="The Keras for ML researchers using PyTorch",
     author="William Falcon",
     author_email="waf2107@columbia.edu",
@@ -19,7 +19,8 @@ setup(
     install_requires=[
         "torch>=1.0.0",
         "tqdm",
-        "test-tube",
+        "test-tube>=0.643",
+        "tensorflow>=1.14.0"
     ],
     packages=find_packages(),
     long_description=open("README.md", encoding="utf-8").read(),