bug fixing for hyper parameter tuning

hyper_main.py

     args.add_argument("-bs", "--batch_size", default=1024, type=int)
     # args.add_argument("-lr", "--learning_rate", default=0.001, type=float)
 
-    args.add_argument("-epo", "--epoch", default=1000, type=int)
+    args.add_argument("-epo", "--epoch", default=100000, type=int)
     # args.add_argument("-prt_epo", "--print_epoch", default=100, type=int)
     # args.add_argument("-eval_epo", "--eval_epoch", default=1000, type=int)
 
         # "l1": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
         # "l2": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
         # "lr": tune.loguniform(1e-4, 1e-1),
-        # "batch_size": tune.choice([2, 4, 8, 16])
+        "embed_dim"     :   tune.choice([50,75,100,125,150,200,300]),
-        "embed_dim"     :   tune.choice([50,75,100,125,150,200]),
         # "batch_size"    :   tune.choice([128,256,512,1024,2048]),
-        "learning_rate" :   tune.choice([0.1,0.01,0.005,0.001,0.0001]),
+        "learning_rate" :   tune.choice([0.1,0.01,0.004,0.005,0.007,0.001,0.0001]),
-        "beta"          :   tune.choice([0.1,1,5,10]),
+        "beta"          :   tune.choice([0.05,0.1,1,4,4.5,5,5.5,6,10]),
-        "margin"        :   tune.choice([1]),
+        "margin"        :   tune.choice([1,0.9,0.8,1.1,1.2]),
 
         # "sampler":sampler,
         # "sampler_test":sampler_test,
         progress_reporter=reporter,
         log_to_file=True,
         # resume=True,
-        local_dir="./ray_local_dir",
+        local_dir="/media/external_10TB/10TB/maheri/metaTL_ray/ray_local_dir",
         name="metatl_rnn1",
     )
 

hyper_tunning.py

     trainer = Trainer([sampler, sampler_valid, sampler_test], conf["itemnum"], ps)
 
     # trainer.train()
+    if checkpoint_dir:
+        print("===================== using checkpoint =====================")
+        model_state, optimizer_state = torch.load(
+            os.path.join(checkpoint_dir, "checkpoint"))
+        trainer.MetaTL.load_state_dict(model_state)
+        trainer.optimizer.load_state_dict(optimizer_state)
 
-    for epoch in range(ps['epoch']):
+    for epoch in range(int(ps['epoch']/1000)):
 
-        for e in range(100):
+        for e in range(1000):
             # sample one batch from data_loader
             train_task, curr_rel = trainer.train_data_loader.next_batch()
             loss, _, _ = trainer.do_one_step(train_task, iseval=False, curr_rel=curr_rel)
         # print('Epoch  {} Testing...'.format(e))
         # test_data = self.eval(istest=True, epoch=e)
 
-        if checkpoint_dir:
-            model_state, optimizer_state = torch.load(
-                os.path.join(checkpoint_dir, "checkpoint"))
-            trainer.MetaTL.load_state_dict(model_state)
-            trainer.optimizer.load_state_dict(optimizer_state)
 
         with tune.checkpoint_dir(epoch) as checkpoint_dir:
             path = os.path.join(checkpoint_dir, "checkpoint")
         tune.report(
             MRR=valid_data["MRR"], NDCG10=valid_data['NDCG@10'], NDCG5=valid_data["NDCG@5"], NDCG1=valid_data["NDCG@1"],
             Hits10=valid_data["Hits@10"], Hits5=valid_data["Hits@5"], Hits1=valid_data["Hits@1"],
-            training_iteration=epoch*100
+            training_iteration=epoch*1000
         )
 

trainer.py

         self.epoch = parameter['epoch']
         # self.print_epoch = parameter['print_epoch']
         # self.eval_epoch = parameter['eval_epoch']
-        self.eval_epoch = 50
+        self.eval_epoch = 1000
         self.device = torch.device('cuda:0')
 
         self.MetaTL = MetaTL(itemnum, parameter)