This guide contains instructions for defining and running a TensorFlow benchmark. These benchmarks store output in TestResults format. If these benchmarks are added to TensorFlow github repo, then we will run them daily with our continuous build and display a graph on our dashboard:

Defining a Benchmark

Defining a TensorFlow benchmark requires extending from tf.test.Benchmark class and calling self.report_benchmark method. For example, take a look at the sample benchmark code below:

import time

import tensorflow as tf

# Define a class that extends from tf.test.Benchmark.
class SampleBenchmark(tf.test.Benchmark):

  # Note: benchmark method name must start with `benchmark`.
  def benchmarkSum(self):
    with tf.Session() as sess:
      x = tf.constant(10)
      y = tf.constant(5)
      result = tf.add(x, y)

      iters = 100
      start_time = time.time()
      for _ in range(iters):
      total_wall_time = time.time() - start_time

      # Call report_benchmark to report a metric value.
          # This value should always be per iteration.

if __name__ == "__main__":

See the full example for SampleBenchmark.

Key points to note in the example above:

  • Benchmark class extends from tf.test.Benchmark.
  • Each benchmark method should start with benchmark prefix.
  • Benchmark method calls report_benchmark to report the metric value.

Adding a bazel Target

We have a special target called tf_py_logged_benchmark for benchmarks defined under TensorFlow github repo. tf_py_logged_benchmark should wrap around a regular py_test target. Running a tf_py_logged_benchmark would print a TestResults proto. Defining a tf_py_logged_benchmark also lets us run it with TensorFlow continuous build.

First, define a regular py_test target. See example below:

  name = "sample_benchmark",
  srcs = [""],
  srcs_version = "PY2AND3",
  deps = [

You can run benchmarks in a py_test target by passing --benchmarks flag. The benchmark should just print out a BenchmarkEntries proto.

bazel test :sample_benchmark --test_arg=--benchmarks=all

Now, add the tf_py_logged_benchmark target (if available). This target would pass in --benchmarks=all to the wrapped py_test target and provide a way to store output for our TensorFlow continuous build. tf_py_logged_benchmark target should be available in TensorFlow repository.

load("//tensorflow/tools/test:performance.bzl", "tf_py_logged_benchmark")

    name = "sample_logged_benchmark",
    target = "//tensorflow/tools/test:sample_benchmark",

Use the following command to run the benchmark target:

bazel test :sample_logged_benchmark