testing slurm ddp

examples/new_project_templates/trainer_gpu_cluster_template.py

@@ -42,21 +42,13 @@ def main(hparams, cluster, results_dict):
     :param hparams:
     :return:
     """
-    on_gpu = hparams.gpus is not None and torch.cuda.is_available()
-
-    device = 'cuda' if on_gpu else 'cpu'
-    hparams.__setattr__('device', device)
-    hparams.__setattr__('on_gpu', on_gpu)
-    hparams.__setattr__('nb_gpus', torch.cuda.device_count())
-    hparams.__setattr__('inference_mode', hparams.model_load_weights_path is not None)
-
     # delay each training start to not overwrite logs
-    process_position, current_gpu = TRAINING_MODEL.get_process_position(hparams.gpus)
+    process_position, current_gpu = LightningTemplateModel.get_process_position(hparams.gpus)
     sleep(process_position + 1)
 
     # init experiment
     log_dir = os.path.dirname(os.path.realpath(__file__))
-    log_dir = os.path.join(log_dir, 'test_tube_demo_logs')
+    log_dir = os.path.join(log_dir, 'pt_lightning_demo_logs')
     exp = Experiment(
         name='test_tube_exp',
         save_dir=log_dir,
@@ -69,7 +61,7 @@ def main(hparams, cluster, results_dict):
 
     # build model
     print('loading model...')
-    model = TRAINING_MODEL(hparams)
+    model = LightningTemplateModel(hparams)
     print('model built')
 
     # callbacks
@@ -91,7 +83,7 @@ def main(hparams, cluster, results_dict):
 
     # configure trainer
     trainer = Trainer(
-        experiment=exp,
+        experiment=hyperparams.tt_name,
         cluster=cluster,
         checkpoint_callback=checkpoint,
         early_stop_callback=early_stop,
@@ -105,9 +97,8 @@ def main(hparams, cluster, results_dict):
 
 def get_default_parser(strategy, root_dir):
 
-    possible_model_names = list(AVAILABLE_MODELS.keys())
     parser = HyperOptArgumentParser(strategy=strategy, add_help=False)
-    add_default_args(parser, root_dir, possible_model_names=possible_model_names, rand_seed=SEED)
+    add_default_args(parser, root_dir, rand_seed=SEED)
     return parser