/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/autotvm/tuner/ |
H A D | xgboost_tuner.py | 82 log_interval=50, argument 89 log_interval=log_interval // 2, 92 optimizer = SimulatedAnnealingOptimizer(task, log_interval=log_interval)
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H A D | sa_model_optimizer.py | 60 log_interval=50, argument 72 self.log_interval = log_interval 77 temp, n_iter, early_stop, log_interval = ( 81 self.log_interval, 136 if log_interval and k % log_interval == 0:
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/dports/misc/py-tvm/incubator-tvm-0.6.1/python/tvm/autotvm/tuner/ |
H A D | xgboost_tuner.py | 69 optimizer='sa', diversity_filter_ratio=None, log_interval=50): argument 74 log_interval=log_interval // 2) 76 optimizer = SimulatedAnnealingOptimizer(task, log_interval=log_interval)
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H A D | sa_model_optimizer.py | 51 early_stop=50, log_interval=50): argument 62 self.log_interval = log_interval 67 temp, n_iter, early_stop, log_interval = \ 68 self.temp, self.n_iter, self.early_stop, self.log_interval 122 if log_interval and k % log_interval == 0:
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/dports/misc/tvm/incubator-tvm-0.6.1/python/tvm/autotvm/tuner/ |
H A D | xgboost_tuner.py | 69 optimizer='sa', diversity_filter_ratio=None, log_interval=50): argument 74 log_interval=log_interval // 2) 76 optimizer = SimulatedAnnealingOptimizer(task, log_interval=log_interval)
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H A D | sa_model_optimizer.py | 51 early_stop=50, log_interval=50): argument 62 self.log_interval = log_interval 67 temp, n_iter, early_stop, log_interval = \ 68 self.temp, self.n_iter, self.early_stop, self.log_interval 122 if log_interval and k % log_interval == 0:
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/dports/www/chromium-legacy/chromium-88.0.4324.182/chrome/browser/safe_browsing/ |
H A D | safe_browsing_metrics_collector.cc | 25 base::TimeDelta log_interval = in StartLogging() local 31 if (delay >= log_interval) { in StartLogging() 34 ScheduleNextLoggingAfterInterval(log_interval - delay); in StartLogging()
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/dports/misc/py-gluonnlp/gluon-nlp-0.10.0/scripts/bert/ |
H A D | pretraining_utils.py | 347 trainer, log_interval): argument 352 running_mlm_loss = running_mlm_loss / log_interval 353 running_nsp_loss = running_nsp_loss / log_interval 358 throughput.asscalar(), lr, duration, duration*1000/log_interval)) 362 mlm_metric, nsp_metric, trainer, log_interval): argument 367 running_mlm_loss = running_mlm_loss / log_interval 368 running_nsp_loss = running_nsp_loss / log_interval 373 … nsp_metric.get()[1] * 100, throughput.asscalar(), lr, duration, duration*1000/log_interval)) 425 def evaluate(data_eval, model, ctx, log_interval, dtype): argument 467 if (step_num + 1) % (log_interval) == 0: [all …]
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H A D | finetune_classifier.py | 213 log_interval = args.log_interval * accumulate if accumulate else args.log_interval variable 488 def log_train(batch_id, batch_num, metric, step_loss, log_interval, epoch_id, learning_rate): argument 496 logging.info(train_str, epoch_id + 1, batch_id + 1, batch_num, step_loss / log_interval, 500 def log_eval(batch_id, batch_num, metric, step_loss, log_interval): argument 508 logging.info(eval_str, batch_id + 1, batch_num, step_loss / log_interval, *metric_val) 605 if (batch_id + 1) % (args.log_interval) == 0: 606 log_train(batch_id, len(train_data), metric, step_loss, args.log_interval, 675 if (batch_id + 1) % (args.log_interval) == 0: 676 log_eval(batch_id, len(loader_dev), metric, step_loss, args.log_interval)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/python/mxnet/gluon/contrib/estimator/ |
H A D | event_handler.py | 246 def __init__(self, log_interval='epoch', argument 250 if not isinstance(log_interval, int) and log_interval != 'epoch': 259 self.log_interval = log_interval 290 if isinstance(self.log_interval, int): 294 if isinstance(self.log_interval, int): 300 if self.batch_index % self.log_interval == 0: 311 if isinstance(self.log_interval, int) or self.log_interval == 'epoch': 325 if isinstance(self.log_interval, int) or self.log_interval == 'epoch':
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/python/mxnet/gluon/contrib/estimator/ |
H A D | event_handler.py | 246 def __init__(self, log_interval='epoch', argument 250 if not isinstance(log_interval, int) and log_interval != 'epoch': 259 self.log_interval = log_interval 290 if isinstance(self.log_interval, int): 294 if isinstance(self.log_interval, int): 300 if self.batch_index % self.log_interval == 0: 311 if isinstance(self.log_interval, int) or self.log_interval == 'epoch': 325 if isinstance(self.log_interval, int) or self.log_interval == 'epoch':
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/tests/python/unittest/ |
H A D | test_gluon_event_handler.py | 247 log_interval = 1 253 logging = LoggingHandler(metrics=[acc], log_interval=log_interval) 272 assert(info_len == int(data_size/batch_size/log_interval) + 1) 277 log_interval = 5 278 logging = LoggingHandler(metrics=[acc], log_interval=log_interval) 295 assert(info_len == int(data_size/batch_size/log_interval) + 1)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/tests/python/unittest/ |
H A D | test_gluon_event_handler.py | 247 log_interval = 1 253 logging = LoggingHandler(metrics=[acc], log_interval=log_interval) 272 assert(info_len == int(data_size/batch_size/log_interval) + 1) 277 log_interval = 5 278 logging = LoggingHandler(metrics=[acc], log_interval=log_interval) 295 assert(info_len == int(data_size/batch_size/log_interval) + 1)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/sparse/wide_deep/ |
H A D | train.py | 55 log_interval = args.log_interval variable 88 speedometer = mx.callback.Speedometer(batch_size, log_interval)
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/sparse/wide_deep/ |
H A D | train.py | 55 log_interval = args.log_interval variable 88 speedometer = mx.callback.Speedometer(batch_size, log_interval)
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/dports/misc/py-gluonnlp/gluon-nlp-0.10.0/scripts/language_model/ |
H A D | large_word_language_model.py | 105 args.log_interval = 1 258 if nbatch % args.log_interval == 0: 259 cur_L = total_L / args.log_interval / len(context) 264 train_batch_size*args.log_interval/(time.time()-start_log_interval_time))) 317 if nbatch % args.log_interval == 0: 320 throughput = batch_size*args.log_interval/(time.time()-start_time)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/sparse/matrix_factorization/ |
H A D | train.py | 76 log_interval = args.log_interval variable 105 speedometer = mx.callback.Speedometer(batch_size, log_interval)
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/sparse/matrix_factorization/ |
H A D | train.py | 76 log_interval = args.log_interval variable 105 speedometer = mx.callback.Speedometer(batch_size, log_interval)
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/dports/net/jgroups/jgroups-2.12.0/tests/perf/org/jgroups/tests/perf/ |
H A D | Test.java | 75 long log_interval=1000; field in Test 174 log_interval=Long.parseLong(tmp2); in start() 176 log_interval=num_msgs / 10; in start() 179 if(num_threads > 0 && log_interval % num_threads != 0) in start() 180 …throw new IllegalArgumentException("log_interval (" + log_interval + ") must be divisible by num_t… in start() 369 if(num_msgs_received % log_interval == 0) { in handleData() 373 double msgs_sec=log_interval / (diff / 1000.0); in handleData() 380 if(counter % log_interval == 0) { in handleData() 479 long thread_interval=log_interval / num_threads; in sendMessages() 493 if(total_msgs % log_interval == 0) { in sendMessages() [all …]
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/dports/devel/py-buildbot/buildbot-3.4.1/buildbot/process/ |
H A D | metrics.py | 386 self.log_interval = None 409 log_interval = metrics_config.get('log_interval', 60) 410 if log_interval != self.log_interval: 414 if log_interval: 417 self.log_task.start(log_interval)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/sparse/factorization_machine/ |
H A D | train.py | 75 log_interval = args.log_interval variable 114 speedometer = mx.callback.Speedometer(batch_size, log_interval)
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/sparse/factorization_machine/ |
H A D | train.py | 75 log_interval = args.log_interval variable 114 speedometer = mx.callback.Speedometer(batch_size, log_interval)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/rnn/word_lm/ |
H A D | train.py | 103 speedometer = mx.callback.Speedometer(batch_size, args.log_interval) 121 if nbatch % args.log_interval == 0 and nbatch > 0: 122 cur_loss = total_loss / bptt / batch_size / args.log_interval
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/rnn/word_lm/ |
H A D | train.py | 103 speedometer = mx.callback.Speedometer(batch_size, args.log_interval) 121 if nbatch % args.log_interval == 0 and nbatch > 0: 122 cur_loss = total_loss / bptt / batch_size / args.log_interval
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/rnn/large_word_lm/ |
H A D | run_utils.py | 66 def evaluate(mod, data_iter, epoch, log_interval): argument 82 if (nbatch + 1) % log_interval == 0:
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