tensorflow/models · error · ValueError
Distribution strategy is not specified.
Error message
Distribution strategy is not specified.
What it means
Error "Distribution strategy is not specified." thrown in tensorflow/models.
Source
Thrown at official/legacy/bert/run_pretraining.py:157
steps_per_loop=steps_per_loop,
epochs=epochs,
sub_model_export_name='pretrained/bert_model',
explicit_allreduce=explicit_allreduce,
pre_allreduce_callbacks=pre_allreduce_callbacks,
post_allreduce_callbacks=post_allreduce_callbacks,
allreduce_bytes_per_pack=allreduce_bytes_per_pack,
train_summary_interval=train_summary_interval,
custom_callbacks=custom_callbacks)
return trained_model
def run_bert_pretrain(strategy, custom_callbacks=None):
"""Runs BERT pre-training."""
bert_config = configs.BertConfig.from_json_file(FLAGS.bert_config_file)
if not strategy:
raise ValueError('Distribution strategy is not specified.')
# Runs customized training loop.
logging.info('Training using customized training loop TF 2.0 with distributed'
'strategy.')
performance.set_mixed_precision_policy(common_flags.dtype())
# Only when explicit_allreduce = True, post_allreduce_callbacks and
# allreduce_bytes_per_pack will take effect. optimizer.apply_gradients() no
# longer implicitly allreduce gradients, users manually allreduce gradient and
# pass the allreduced grads_and_vars to apply_gradients().
# With explicit_allreduce = True, clip_by_global_norm is moved to after
# allreduce.
return run_customized_training(
strategy,
bert_config,
FLAGS.init_checkpoint, # Used to initialize only the BERT submodel.
FLAGS.max_seq_length,View on GitHub (pinned to e006f5f0d5)
When it happens
Trigger: Thrown at official/legacy/bert/run_pretraining.py:157 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24).
Data as JSON: /api/errors/08b9c154ec790a69.
Report an issue: GitHub.