hyper parameter tuning including neg_size

hyper_main.py

         scheduler=scheduler,
         progress_reporter=reporter,
         log_to_file=True,
-        # resume=True,
+        resume=True,
         local_dir="/media/external_10TB/10TB/maheri/metaTL_ray/ray_local_dir",
         name="bpr_rnn",
     )

main.py

     args.add_argument("-m", "--margin", default=1, type=float)
     args.add_argument("-p", "--dropout_p", default=0.5, type=float)
 
-    args.add_argument("-gpu", "--device", default=1, type=int)
+    args.add_argument("-gpu", "--device", default=0, type=int)
+    args.add_argument("--number_of_neg",default=5,type=int)
+
 
 
     args = args.parse_args()
     for k, v in vars(args).items():
         params[k] = v
 
-    params['device'] = torch.device('cuda:'+str(args.device))
+
+
+    # params['device'] = torch.device('cuda:'+str(args.device))
+    params['device'] = args.device
     # params['device'] = torch.device('cpu')
 
     return params, args
 
 if __name__ == '__main__':
+    print(torch.cuda.is_available())
     params, args = get_params()
 
     if params['seed'] is not None:
         np.random.seed(SEED)
         random.seed(SEED)
 
+    print("===============", torch.cuda.device_count(), "=======")
     user_train, usernum_train, itemnum, user_input_test, user_test, user_input_valid, user_valid = data_load(args.dataset, args.K)    
 
-    sampler = WarpSampler(user_train, usernum_train, itemnum, batch_size=args.batch_size, maxlen=args.K, n_workers=3)
+    sampler = WarpSampler(user_train, usernum_train, itemnum, batch_size=args.batch_size, maxlen=args.K, n_workers=3,params=params)
 
     sampler_test = DataLoader(user_input_test, user_test, itemnum, params)
 
     sampler_valid = DataLoader(user_input_valid, user_valid, itemnum, params)
-    
 
-    trainer = Trainer([sampler, sampler_valid, sampler_test], itemnum, params)
+    print("===============", torch.cuda.device_count(), "=======")
 
+    trainer = Trainer([sampler, sampler_valid, sampler_test], itemnum, params)
+    print("===============", torch.cuda.device_count(), "=======")
     trainer.train()
 
     sampler.close()