def Run()

in perfkitbenchmarker/linux_benchmarks/inception3_benchmark.py [0:0]

• 91 lines of code
• 7 McCabe index (conditional complexity)

def Run(benchmark_spec):
  """Run Inception V3 on the cluster.

  Args:
    benchmark_spec: The benchmark specification. Contains all data that is
      required to run the benchmark.

  Returns:
    A list of sample.Sample objects.
  """
  _UpdateBenchmarkSpecWithFlags(benchmark_spec)
  vm = benchmark_spec.vms[0]
  inception3_benchmark_script = (
      'tpu/models/experimental/inception/inception_v3.py'
  )
  inception3_benchmark_cmd = (
      '{env_cmd} && python {script} '
      '--learning_rate={learning_rate} '
      '--iterations={iterations} '
      '--use_tpu={use_tpu} '
      '--use_data={use_data} '
      '--train_steps_per_eval={steps_per_eval} '
      '--data_dir={data_dir} '
      '--model_dir={model_dir} '
      '--save_checkpoints_secs={save_checkpoints_secs} '
      '--train_batch_size={train_batch_size} '
      '--eval_batch_size={eval_batch_size} '
      '--precision={precision}'.format(
          env_cmd=benchmark_spec.env_cmd,
          script=inception3_benchmark_script,
          learning_rate=benchmark_spec.learning_rate,
          iterations=benchmark_spec.iterations,
          use_tpu=bool(benchmark_spec.tpus),
          use_data=benchmark_spec.use_data,
          steps_per_eval=benchmark_spec.steps_per_eval,
          data_dir=benchmark_spec.data_dir,
          model_dir=benchmark_spec.model_dir,
          save_checkpoints_secs=benchmark_spec.save_checkpoints_secs,
          train_batch_size=benchmark_spec.train_batch_size,
          eval_batch_size=benchmark_spec.eval_batch_size,
          precision=benchmark_spec.precision,
      )
  )
  if FLAGS.tf_device == 'gpu':
    inception3_benchmark_cmd = '{env} {cmd}'.format(
        env=tensorflow.GetEnvironmentVars(vm), cmd=inception3_benchmark_cmd
    )
  samples = []
  metadata = _CreateMetadataDict(benchmark_spec)
  elapsed_seconds = 0
  steps_per_eval = benchmark_spec.steps_per_eval
  train_steps = benchmark_spec.train_steps
  for step in range(
      steps_per_eval, train_steps + steps_per_eval, steps_per_eval
  ):
    step = min(step, train_steps)
    inception3_benchmark_cmd_step = '{cmd} --train_steps={step}'.format(
        cmd=inception3_benchmark_cmd, step=step
    )
    if benchmark_spec.mode in ('train', 'train_and_eval'):
      if benchmark_spec.tpus:
        tpu = benchmark_spec.tpu_groups['train'].GetName()
        num_shards = '--num_shards={}'.format(
            benchmark_spec.tpu_groups['train'].GetNumShards()
        )
      else:
        tpu = num_shards = ''
      inception3_benchmark_train_cmd = (
          '{cmd} --tpu={tpu} --mode=train {num_shards}'.format(
              cmd=inception3_benchmark_cmd_step, tpu=tpu, num_shards=num_shards
          )
      )
      start = time.time()
      stdout, stderr = vm.RobustRemoteCommand(inception3_benchmark_train_cmd)
      elapsed_seconds += time.time() - start
      samples.extend(
          mnist_benchmark.MakeSamplesFromTrainOutput(
              metadata, stdout + stderr, elapsed_seconds, step
          )
      )
    if benchmark_spec.mode in ('train_and_eval', 'eval'):
      if benchmark_spec.tpus:
        tpu = benchmark_spec.tpu_groups['eval'].GetName()
        num_shards = '--num_shards={}'.format(
            benchmark_spec.tpu_groups['eval'].GetNumShards()
        )
      else:
        tpu = num_shards = ''
      inception3_benchmark_eval_cmd = (
          '{cmd} --tpu={tpu} --mode=eval {num_shards}'.format(
              cmd=inception3_benchmark_cmd_step, tpu=tpu, num_shards=num_shards
          )
      )
      stdout, stderr = vm.RobustRemoteCommand(inception3_benchmark_eval_cmd)
      samples.extend(
          resnet_benchmark.MakeSamplesFromEvalOutput(
              metadata, stdout + stderr, elapsed_seconds
          )
      )
  return samples