class_docstr: TFMA benchmark base class.
class_docstr_tok: ["TFMA","benchmark","base","class","."]
docstr: Benchmark using the EvalSavedModel to make predictions.
  
  Runs EvalSavedModel.predict_list and records the wall time taken.
docstr_tok: ["Benchmark","using","the","EvalSavedModel","to","make","predictions",".","Runs","EvalSavedModel.predict_list","and","records","the","wall","time","taken","."]
code: def benchmarkEvalSavedModelPredict(self):
      """Benchmark using the EvalSavedModel to make predictions.
  
      Runs EvalSavedModel.predict_list and records the wall time taken.
      """
      batch_size = 1000
  
      eval_saved_model = load.EvalSavedModel(
          path=self._dataset.tfma_saved_model_path(),
          include_default_metrics=True,
      )
  
      records = self._dataset.read_raw_dataset(
          deserialize=False, limit=self._max_num_examples()
      )
  
      start = time.time()
      for batch in benchmark_utils.batched_iterator(
          records, batch_size
      ):
          eval_saved_model.predict_list(batch)
      end = time.time()
      delta = end - start
      self.report_benchmark(
          iters=1,
          wall_time=delta,
          extras={
              "batch_size": batch_size,
              "num_examples": self._dataset.num_examples(
                  limit=self._max_num_examples()
              ),
          },
      )
  
file_path: tfx/tfx/benchmarks/tfma_benchmark_base.py
imports: import time
  import apache_beam
  import tensorflow_model_analysis
  from tensorflow_model_analysis.eval_saved_model import load
  import tfx
  from tfx.benchmarks import benchmark_utils
  from tfx.benchmarks import benchmark_base
label_desc: Use image node_id 7 for calling the TFMABenchmarkBase obj's underlying member method code with example usage: obj.benchmarkEvalSavedModelPredict() without return types