155 lines
4.9 KiB
Python
Executable file
155 lines
4.9 KiB
Python
Executable file
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import time
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import tensorflow as tf
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import dllogger
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import signal
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from utils import hvd_wrapper as hvd
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__all__ = ['TrainingLoggingHook', 'TrainingPartitionHook']
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class MeanAccumulator:
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def __init__(self):
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self.sum = 0
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self.count = 0
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def consume(self, value):
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self.sum += value
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self.count += 1
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def value(self):
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if self.count:
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return self.sum / self.count
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else:
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return 0
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class TrainingLoggingHook(tf.estimator.SessionRunHook):
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def __init__(
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self, global_batch_size, num_steps, num_samples, num_epochs, steps_per_epoch, warmup_steps=20, logging_steps=1
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):
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self.global_batch_size = global_batch_size
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self.num_steps = num_steps
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self.num_samples = num_samples
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self.num_epochs = num_epochs
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self.steps_per_epoch = steps_per_epoch
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self.warmup_steps = warmup_steps
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self.logging_steps = logging_steps
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self.current_step = 0
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self.current_epoch = 0
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self.t0 = None
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self.mean_throughput = MeanAccumulator()
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# Determines if its the last step of the epoch
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def _last_step_of_epoch(self, global_step):
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return (global_step + 1) // self.steps_per_epoch > (global_step // self.steps_per_epoch)
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def before_run(self, run_context):
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run_args = tf.train.SessionRunArgs(
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fetches=[
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tf.train.get_global_step(), 'cross_entropy_loss_ref:0', 'l2_loss_ref:0', 'total_loss_ref:0',
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'learning_rate_ref:0'
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]
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)
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self.t0 = time.time()
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return run_args
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def after_run(self, run_context, run_values):
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global_step, cross_entropy, l2_loss, total_loss, learning_rate = run_values.results
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batch_time = time.time() - self.t0
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ips = self.global_batch_size / batch_time
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metrics = {
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"imgs_per_sec": ips,
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"cross_entropy": cross_entropy,
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"l2_loss": l2_loss,
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"total_loss": total_loss,
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"learning_rate": learning_rate
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}
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if self.current_step >= self.warmup_steps:
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self.mean_throughput.consume(metrics['imgs_per_sec'])
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if (self.current_step % self.logging_steps) == 0:
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metrics = {k: float(v) for k, v in metrics.items()}
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dllogger.log(data=metrics, step=(int(global_step // self.steps_per_epoch), int(global_step)))
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self.current_step += 1
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if self._last_step_of_epoch(global_step):
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metrics = {
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"cross_entropy": cross_entropy,
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"l2_loss": l2_loss,
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"total_loss": total_loss,
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"learning_rate": learning_rate
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}
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metrics = {k: float(v) for k, v in metrics.items()}
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dllogger.log(data=metrics, step=(int(global_step // self.steps_per_epoch), ))
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self.current_epoch += 1
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class TrainingPartitionHook(tf.estimator.SessionRunHook):
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def __init__(self, sync_freq=10):
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super().__init__()
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self.signal_recieved = False
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self.sync_freq = sync_freq
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self.global_step = 0
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signal.signal(signal.SIGUSR1, self._signal_handler)
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signal.signal(signal.SIGTERM, self._signal_handler)
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def begin(self):
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if hvd.size() > 1:
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with tf.device("/cpu:0"):
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self.input_op = tf.placeholder(tf.int32, shape=())
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self.allreduce_op = hvd.hvd_global_object.allreduce(
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self.input_op, op=hvd.hvd_global_object.Sum, name="signal_handler_all_reduce")
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def before_run(self, run_context):
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fetches = [tf.train.get_global_step()]
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feed_dict = None
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if hvd.size() > 1 and (self.global_step % self.sync_freq) == 0:
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fetches += [self.allreduce_op]
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feed_dict = {self.input_op: int(self.signal_recieved)}
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return tf.train.SessionRunArgs(fetches, feed_dict=feed_dict)
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def after_run(self, run_context, run_values):
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self.global_step = run_values.results[0] + 1
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if hvd.size() > 1 and len(run_values.results) == 2:
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if run_values.results[1] > 0:
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run_context.request_stop()
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elif self.signal_recieved:
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run_context.request_stop()
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def _signal_handler(self, signum, frame):
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print("Stop signal received")
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self.signal_recieved = True
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