From: François Fleuret <francois@fleuret.org>
Date: Thu, 12 Sep 2024 17:15:46 +0000 (+0200)
Subject: Update.
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=9e508a333a34bc4b65e9e78d9dd3106b08157acb;p=culture.git

Update.
---

diff --git a/main.py b/main.py
index 80e99fd..63cd377 100755
--- a/main.py
+++ b/main.py
@@ -1386,13 +1386,6 @@ def multithread_execution(fun, arguments):
         ]
 
 
-# ----- test
-
-# ranked_models = sorted(models, key=lambda m: float(m.test_accuracy))
-# weakest_models = ranked_models[: len(gpus)]
-
-# n_epoch = 14
-
 ######################################################################
 
 for n_epoch in range(current_epoch, args.nb_epochs):
@@ -1446,36 +1439,6 @@ for n_epoch in range(current_epoch, args.nb_epochs):
             [(models, nb_c_quizzes_to_generate, gpu) for gpu in gpus],
         )
 
-        ## records, threads = [], []
-        ##
-        ## start_time = time.perf_counter()
-        ##
-        ## if len(gpus) > 1:
-        ## for gpu in gpus:
-        ## t = threading.Thread(
-        ## target=thread_generate_ae_c_quizzes,
-        ## daemon=True,
-        ## args=(models, nb_c_quizzes_to_generate, records, gpu),
-        ## )
-        ##
-        ## # To get a different sequence between threads
-        ## log_string(f"dummy {torch.rand(1)}")
-        ## threads.append(t)
-        ## t.start()
-        ##
-        ## for t in threads:
-        ## t.join()
-        ##
-        ## else:
-        ## records.append(
-        ## generate_ae_c_quizzes(models, nb_c_quizzes_to_generate, gpus[0])
-        ## )
-        ##
-        ## time_c_quizzes = int(time.perf_counter() - start_time)
-        ##
-        ## c_quizzes = torch.cat([q.to(main_device) for q, _ in records], dim=0)
-        ## agreements = torch.cat([a.to(main_device) for _, a in records], dim=0)
-
         # --------------------------------------------------------------------
 
         filename = f"culture_c_quiz_{n_epoch:04d}.png"
@@ -1499,9 +1462,6 @@ for n_epoch in range(current_epoch, args.nb_epochs):
     else:
         log_string(f"nb_c_quizzes {c_quizzes.size(0)}")
 
-    # one_ae_epoch(model, quiz_machine, n_epoch, None)
-    # exit(0)
-
     # --------------------------------------------------------------------
 
     ranked_models = sorted(models, key=lambda m: float(m.test_accuracy))
@@ -1521,41 +1481,6 @@ for n_epoch in range(current_epoch, args.nb_epochs):
         ],
     )
 
-    ## threads = []
-    ##
-    ## start_time = time.perf_counter()
-    ##
-    ## if len(gpus) > 1:
-    ## for gpu, model in zip(gpus, weakest_models):
-    ## log_string(f"training model {model.id} (accuracy {model.test_accuracy})")
-    ## if c_quizzes is None:
-    ## c_quizzes_for_this_model = None
-    ## else:
-    ## c_quizzes_for_this_model = c_quizzes[agreements[:, model.id]]
-    ##
-    ## t = threading.Thread(
-    ## target=one_ae_epoch,
-    ## daemon=True,
-    ## args=(model, quiz_machine, n_epoch, c_quizzes_for_this_model, gpu),
-    ## )
-    ##
-    ## threads.append(t)
-    ##
-    ## t.start()
-    ##
-    ## for t in threads:
-    ## t.join()
-    ##
-    ## else:
-    ## model = weakest_models[0]
-    ## log_string(f"training model {model.id} (accuracy {model.test_accuracy})")
-    ## if c_quizzes is None:
-    ## c_quizzes_for_this_model = None
-    ## else:
-    ## c_quizzes_for_this_model = c_quizzes[agreements[:, model.id]]
-    ##
-    ## one_ae_epoch(model, quiz_machine, n_epoch, c_quizzes_for_this_model, gpus[0])
-
     time_train += int(time.perf_counter() - start_time)
 
     # --------------------------------------------------------------------