4949fa2bc15fcf5364706e2d48eba00f58e4de7a,perfkitbenchmarker/linux_benchmarks/resnet_benchmark.py,,Run,#Any#,219

Before Change

    resnet_benchmark_cmd_step = "{cmd} --train_steps={step}".format(
        cmd=resnet_benchmark_cmd, step=step)
    if benchmark_spec.mode in ("train", "train_and_eval"):
      resnet_benchmark_train_cmd = (
          "{cmd} --tpu={tpu} --mode=train --num_cores={num_cores}".format(
              cmd=resnet_benchmark_cmd_step,
              tpu=benchmark_spec.tpu_train,
              num_cores=benchmark_spec.num_shards_train))
      start = time.time()
      stdout, stderr = vm.RobustRemoteCommand(resnet_benchmark_train_cmd,
                                              should_log=True)
      elapsed_seconds += (time.time() - start)

After Change

      "--num_eval_images={num_eval_images}".format(
          env_cmd=benchmark_spec.env_cmd,
          script=resnet_benchmark_script,
          use_tpu=bool(benchmark_spec.tpus),
          data_dir=benchmark_spec.data_dir,
          model_dir=benchmark_spec.model_dir,
          depth=benchmark_spec.depth,
          train_batch_size=benchmark_spec.train_batch_size,
          eval_batch_size=benchmark_spec.eval_batch_size,
          iterations=benchmark_spec.iterations,
          data_format=benchmark_spec.data_format,
          precision=benchmark_spec.precision,
          skip_host_call=benchmark_spec.skip_host_call,
          num_train_images=benchmark_spec.num_train_images,
          num_eval_images=benchmark_spec.num_eval_images
      ))
  if FLAGS.tf_device == "gpu":
    resnet_benchmark_cmd = "{env} {cmd}".format(
        env=tensorflow.GetEnvironmentVars(vm), cmd=resnet_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)
    resnet_benchmark_cmd_step = "{cmd} --train_steps={step}".format(
        cmd=resnet_benchmark_cmd, step=step)
    if benchmark_spec.mode in ("train", "train_and_eval"):
      resnet_benchmark_train_cmd = (
          "{cmd} --tpu={tpu} --mode=train --num_cores={num_cores}".format(
              cmd=resnet_benchmark_cmd_step,
              tpu=(benchmark_spec.tpu_groups["train"].GetName() if
                   benchmark_spec.tpus else ""),
              num_cores=(benchmark_spec.tpu_groups["train"].GetNumShards() if
                         benchmark_spec.tpus else 0)))
      start = time.time()
      stdout, stderr = vm.RobustRemoteCommand(resnet_benchmark_train_cmd,
                                              should_log=True)
      elapsed_seconds += (time.time() - start)

Instances