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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Benchmark script for TensorFlow.
See the README for more information.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags as absl_flags
import tensorflow as tf
import benchmark_cnn
import cnn_util
import flags
import mlperf
from cnn_util import log_fn
flags.define_flags()
for name in flags.param_specs.keys():
absl_flags.declare_key_flag(name)
absl_flags.DEFINE_boolean(
'ml_perf_compliance_logging', False,
'Print logs required to be compliant with MLPerf. If set, must clone the '
'MLPerf training repo https://github.com/mlperf/training and add '
'https://github.com/mlperf/training/tree/master/compliance to the '
'PYTHONPATH')
def main(positional_arguments):
# Command-line arguments like '--distortions False' are equivalent to
# '--distortions=True False', where False is a positional argument. To prevent
# this from silently running with distortions, we do not allow positional
# arguments.
assert len(positional_arguments) >= 1
if len(positional_arguments) > 1:
raise ValueError('Received unknown positional arguments: %s'
% positional_arguments[1:])
params = benchmark_cnn.make_params_from_flags()
with mlperf.mlperf_logger(absl_flags.FLAGS.ml_perf_compliance_logging,
params.model):
params = benchmark_cnn.setup(params)
bench = benchmark_cnn.BenchmarkCNN(params)
tfversion = cnn_util.tensorflow_version_tuple()
log_fn('TensorFlow: %i.%i' % (tfversion[0], tfversion[1]))
bench.print_info()
bench.run()
if __name__ == '__main__':
app.run(main) # Raises error on invalid flags, unlike tf.app.run()
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