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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/autotvm/tuner/
H A Dxgboost_tuner.py82 log_interval=50, argument
89 log_interval=log_interval // 2,
92 optimizer = SimulatedAnnealingOptimizer(task, log_interval=log_interval)
H A Dsa_model_optimizer.py60 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:
/dports/misc/py-tvm/incubator-tvm-0.6.1/python/tvm/autotvm/tuner/
H A Dxgboost_tuner.py69 optimizer='sa', diversity_filter_ratio=None, log_interval=50): argument
74 log_interval=log_interval // 2)
76 optimizer = SimulatedAnnealingOptimizer(task, log_interval=log_interval)
H A Dsa_model_optimizer.py51 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:
/dports/misc/tvm/incubator-tvm-0.6.1/python/tvm/autotvm/tuner/
H A Dxgboost_tuner.py69 optimizer='sa', diversity_filter_ratio=None, log_interval=50): argument
74 log_interval=log_interval // 2)
76 optimizer = SimulatedAnnealingOptimizer(task, log_interval=log_interval)
H A Dsa_model_optimizer.py51 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:
/dports/www/chromium-legacy/chromium-88.0.4324.182/chrome/browser/safe_browsing/
H A Dsafe_browsing_metrics_collector.cc25 base::TimeDelta log_interval = in StartLogging() local
31 if (delay >= log_interval) { in StartLogging()
34 ScheduleNextLoggingAfterInterval(log_interval - delay); in StartLogging()
/dports/misc/py-gluonnlp/gluon-nlp-0.10.0/scripts/bert/
H A Dpretraining_utils.py347 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 …]
H A Dfinetune_classifier.py213 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)
/dports/misc/mxnet/incubator-mxnet-1.9.0/python/mxnet/gluon/contrib/estimator/
H A Devent_handler.py246 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':
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/python/mxnet/gluon/contrib/estimator/
H A Devent_handler.py246 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':
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/tests/python/unittest/
H A Dtest_gluon_event_handler.py247 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)
/dports/misc/mxnet/incubator-mxnet-1.9.0/tests/python/unittest/
H A Dtest_gluon_event_handler.py247 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)
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/sparse/wide_deep/
H A Dtrain.py55 log_interval = args.log_interval variable
88 speedometer = mx.callback.Speedometer(batch_size, log_interval)
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/sparse/wide_deep/
H A Dtrain.py55 log_interval = args.log_interval variable
88 speedometer = mx.callback.Speedometer(batch_size, log_interval)
/dports/misc/py-gluonnlp/gluon-nlp-0.10.0/scripts/language_model/
H A Dlarge_word_language_model.py105 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)
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/sparse/matrix_factorization/
H A Dtrain.py76 log_interval = args.log_interval variable
105 speedometer = mx.callback.Speedometer(batch_size, log_interval)
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/sparse/matrix_factorization/
H A Dtrain.py76 log_interval = args.log_interval variable
105 speedometer = mx.callback.Speedometer(batch_size, log_interval)
/dports/net/jgroups/jgroups-2.12.0/tests/perf/org/jgroups/tests/perf/
H A DTest.java75 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 …]
/dports/devel/py-buildbot/buildbot-3.4.1/buildbot/process/
H A Dmetrics.py386 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)
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/sparse/factorization_machine/
H A Dtrain.py75 log_interval = args.log_interval variable
114 speedometer = mx.callback.Speedometer(batch_size, log_interval)
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/sparse/factorization_machine/
H A Dtrain.py75 log_interval = args.log_interval variable
114 speedometer = mx.callback.Speedometer(batch_size, log_interval)
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/rnn/word_lm/
H A Dtrain.py103 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
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/rnn/word_lm/
H A Dtrain.py103 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
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/rnn/large_word_lm/
H A Drun_utils.py66 def evaluate(mod, data_iter, epoch, log_interval): argument
82 if (nbatch + 1) % log_interval == 0:

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