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tf.test.Benchmark

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Abstract class that provides helpers for TensorFlow benchmarks.

tf.test.Benchmark()

Methods

evaluate

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evaluate(
    tensors
)

Evaluates tensors and returns numpy values.

Args
tensors A Tensor or a nested list/tuple of Tensors.
Returns
tensors numpy values.

is_abstract

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@classmethod
is_abstract()

report_benchmark

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report_benchmark(
    iters=None, cpu_time=None, wall_time=None, throughput=None, extras=None,
    name=None, metrics=None
)

Report a benchmark.

Args
iters (optional) How many iterations were run
cpu_time (optional) Median or mean cpu time in seconds.
wall_time (optional) Median or mean wall time in seconds.
throughput (optional) Throughput (in MB/s)
extras (optional) Dict mapping string keys to additional benchmark info. Values may be either floats or values that are convertible to strings.
name (optional) Override the BenchmarkEntry name with name. Otherwise it is inferred from the top-level method name.
metrics (optional) A list of dict, where each dict has the keys below name (required), string, metric name value (required), double, metric value min_value (optional), double, minimum acceptable metric value max_value (optional), double, maximum acceptable metric value

run_op_benchmark

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run_op_benchmark(
    sess, op_or_tensor, feed_dict=None, burn_iters=2, min_iters=10,
    store_trace=False, store_memory_usage=True, name=None, extras=None, mbs=0
)

Run an op or tensor in the given session. Report the results.

Args
sess Session object to use for timing.
op_or_tensor Operation or Tensor to benchmark.
feed_dict A dict of values to feed for each op iteration (see the feed_dict parameter of Session.run).
burn_iters Number of burn-in iterations to run.
min_iters Minimum number of iterations to use for timing.
store_trace Boolean, whether to run an extra untimed iteration and store the trace of iteration in returned extras. The trace will be stored as a string in Google Chrome trace format in the extras field "full_trace_chrome_format". Note that trace will not be stored in test_log_pb2.TestResults proto.
store_memory_usage Boolean, whether to run an extra untimed iteration, calculate memory usage, and store that in extras fields.
name (optional) Override the BenchmarkEntry name with name. Otherwise it is inferred from the top-level method name.
extras (optional) Dict mapping string keys to additional benchmark info. Values may be either floats or values that are convertible to strings.
mbs (optional) The number of megabytes moved by this op, used to calculate the ops throughput.
Returns
A dict containing the key-value pairs that were passed to report_benchmark. If store_trace option is used, then full_chrome_trace_format will be included in return dictionary even though it is not passed to report_benchmark with extras.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/test/Benchmark