class_docstr: Custom Experiment class for running distributed experiments.
class_docstr_tok: ["Custom","Experiment","class","for","running","distributed","experiments","."]
docstr: Evaluate on train data until checkpoints stop being produced.
code: def continuous_eval_on_train_data(self):
      """Evaluate on train data until checkpoints stop being produced."""
      for ckpt_path in next_checkpoint(
          self._hparams.model_dir, self._hparams.eval_timeout_mins
      ):
          # Skip zero'th step.
          train_step = decoding.get_step_from_ckpt_path(ckpt_path)
          if train_step == 0:
              tf.logging.info("Skipping evaluation at step 0")
              continue
          self.evaluate_on_train_data()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import contextlib
  import json
  import os
  import random
  import numpy
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import decoding
  from tensor2tensor.utils import devices
  from tensor2tensor.utils import hparams_lib
  from tensor2tensor.utils import metrics_hook
  from tensor2tensor.utils import mlperf_log
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  from tensorflow.core.protobuf import rewriter_config_pb2
  from tensorflow.python import debug
label_desc: Use image node_id 13 for calling the T2TExperiment obj's underlying member method code with example usage: obj.continuous_eval_on_train_data() without return types