def iteration_report(opts, time):
return "{:5f} items/sec".format(opts.batch_size * opts.batches_per_step / time)
if __name__ == '__main__':
sys.path.insert(1, '../../../utils/benchmarks/popart')
import benchmark
module = benchmark.Benchmark(
graph_builder,
add_args,
iteration_report
)
opts = benchmark.parse_opts(module)
opts.train = opts.mode == "train"
# Log Benchmark Message
print("Popart Multi-IPU {} Synthetic benchmark.\n"
" Batch size {}.\n"
" Batches per Step {}.\n"
" Steps {}.\n"
" {} IPUs."
.format(
{"infer": "Inference", "eval": "Evaluation", "train": "Training"}[opts.mode],
opts.batch_size,
opts.batches_per_step if not opts.report else "n/a",
opts.steps if not opts.report else "n/a",
opts.shards))
if __name__ == '__main__':
# Add benchmark module to path
cwd = os.path.dirname(os.path.abspath(inspect.stack()[0][1]))
sys.path.insert(1, os.path.join(cwd, '..', '..', '..', 'utils',
'benchmarks', 'tensorflow'))
import benchmark
module = benchmark.Benchmark(
graph_builder,
inputs,
initializer,
add_args,
iteration_report
)
options = benchmark.parse_opts(module, False)
if options.shards > 1:
raise NotImplementedError(
"--shards option has not been implemented with this example")
# Log Benchmark Message
print("Multi-layer LSTM with a dense final layer, {} Benchmark.\n"
" Batch size {}.\n"
" Batches per Step {}.\n"
" Steps {}.\n"
" Hidden size {}.\n"
" Number of layers {}.\n"
" Timesteps {}.\n"
.format(
"Training" if options.train else "Inference",
parser.set_defaults(batches_per_step=1000,
steps=5,
convolution_options=None)
return parser
def iteration_report(opts, time):
return "{:5f} images/sec.".format(opts.batch_size * opts.batches_per_step /
time)
if __name__ == '__main__':
module = Benchmark(graph_builder, inputs, initializer, add_args,
iteration_report)
opts = parse_opts(module, False)
if opts.shards > 1:
raise NotImplementedError(
"--shards option has not been implemented with this example")
# Temporary setting for ResNet18 before this option is automated.
if opts.size == 18:
opts.convolution_options = '{"availableMemoryProportion": "0.4"}'
# Log Benchmark Message
print("TensorFlow ResNet{} {} Synthetic benchmark.\n"
" Batch size {}.\n"
" Batches per Step {}.\n"
" Steps {}.".format(opts.size,
"Training" if opts.train else "Inference",
