fix prints for py3.5

README.md

@@ -1,4 +1,4 @@
-![Logo](./docs/source/_static/lightning_logo_medium.png)
+![Logo](./docs/source/_static/lightning_logo_small.png)
 
 # PyTorch Lightning
 
@@ -123,7 +123,7 @@ trainer = Trainer(experiment=exp, max_nb_epochs=1, train_percent_check=0.1)
 trainer.fit(model)
 
 # view tensorflow logs 
-print(f'View tensorboard logs by running\ntensorboard --logdir {os.getcwd()}')
+print('View tensorboard logs by running\ntensorboard --logdir %s' % os.getcwd())
 print('and going to http://localhost:6006 on your browser')
 ```
 

docs/Trainer/Distributed training.md

@@ -94,7 +94,7 @@ cluster.add_command('export NCCL_SOCKET_IFNAME=^docker0,lo')
 cluster.add_command('export NCCL_DEBUG=INFO')
 
 # setting a master port here is a good idea.
-cluster.add_command(f'export MASTER_PORT={PORT}')
+cluster.add_command('export MASTER_PORT=%r' % PORT)
 
 # good to load the latest NCCL version
 cluster.load_modules(['NCCL/2.4.7-1-cuda.10.0'])

examples/new_project_templates/single_cpu_template.py

@@ -102,5 +102,5 @@ if __name__ == '__main__':
     # RUN TRAINING
     # ---------------------
     # run on HPC cluster
-    print(f'RUNNING ON CPU')
+    print('RUNNING ON CPU')
     main(hyperparams)

examples/new_project_templates/single_gpu_node_16bit_template.py

@@ -105,5 +105,5 @@ if __name__ == '__main__':
     # RUN TRAINING
     # ---------------------
     # run on HPC cluster
-    print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
+    print('RUNNING INTERACTIVE MODE ON GPUS. gpu ids: %i' % hyperparams.gpus)
     main(hyperparams)

examples/new_project_templates/single_gpu_node_ddp_template.py

@@ -105,5 +105,5 @@ if __name__ == '__main__':
     # RUN TRAINING
     # ---------------------
     # run on HPC cluster
-    print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
+    print('RUNNING INTERACTIVE MODE ON GPUS. gpu ids: %i' % hyperparams.gpus)
     main(hyperparams)

examples/new_project_templates/single_gpu_node_dp_template.py

@@ -104,5 +104,5 @@ if __name__ == '__main__':
     # RUN TRAINING
     # ---------------------
     # run on HPC cluster
-    print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
+    print('RUNNING INTERACTIVE MODE ON GPUS. gpu ids: %i' % hyperparams.gpus)
     main(hyperparams)

pytorch_lightning/models/trainer.py

@@ -460,9 +460,11 @@ class Trainer(TrainerIO):
         # check for this bug (amp + dp + !01 doesn't work)
         # https://github.com/NVIDIA/apex/issues/227
         if self.use_dp and self.use_amp:
-            m = f'amp level {self.amp_level} with DataParallel is not supported. ' \
-                f'See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227. ' \
-                f'We recommend you switch to ddp if you want to use amp'
+            m = """
+            Amp level %r with DataParallel is not supported.
+            See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.
+            We recommend you switch to ddp if you want to use amp
+            """ % self.amp_level
             raise MisconfigurationException(m)
 
         model = LightningDataParallel(model, device_ids=self.data_parallel_device_ids)
@@ -543,7 +545,7 @@ class Trainer(TrainerIO):
             port = os.environ['MASTER_PORT']
         except Exception:
             port = 12910
-            os.environ['MASTER_PORT'] = f'{port}'
+            os.environ['MASTER_PORT'] = str(port)
 
         # figure out the root node addr
         try:

pytorch_lightning/root_module/memory.py

@@ -196,6 +196,6 @@ def get_gpu_memory_map():
     gpu_memory = [int(x) for x in result.strip().split('\n')]
     gpu_memory_map = {}
     for k, v in zip(range(len(gpu_memory)), gpu_memory):
-        k = f'gpu_{k}'
+        k = 'gpu_%i' % k
         gpu_memory_map[k] = v
     return gpu_memory_map

tests/debug.py

@@ -132,7 +132,7 @@ def run_prediction(dataloader, trained_model):
 
     print(val_acc)
 
-    assert val_acc > 0.70, f'this model is expected to get > 0.7 in test set (it got {val_acc})'
+    assert val_acc > 0.70, 'this model is expected to get > 0.7 in test set (it got %f)' % val_acc
 
 
 def main():

tests/test_models.py

@@ -670,13 +670,13 @@ def run_prediction(dataloader, trained_model):
 
     print(val_acc)
 
-    assert val_acc > 0.50, f'this model is expected to get > 0.50 in test set (it got {val_acc})'
+    assert val_acc > 0.50, 'this model is expected to get > 0.50 in test set (it got %f)' % val_acc
 
 
 def assert_ok_acc(trainer):
     # this model should get 0.80+ acc
     acc = trainer.tng_tqdm_dic['val_acc']
-    assert acc > 0.50, f'model failed to get expected 0.50 validation accuracy. Got: {acc}'
+    assert acc > 0.50, 'model failed to get expected 0.50 validation accuracy. Got: %f' % acc
 
 
 if __name__ == '__main__':