{"record":{"id":"ac6681e33322c1ce","repo":"tensorflow/models","slug":"the-tf-data-service-flag-requires-tensorflow-versi","errorCode":null,"errorMessage":"The tf_data_service flag requires Tensorflow version >= 2.3.0, but the version is {}","messagePattern":"The tf_data_service flag requires Tensorflow version >= 2\\.3\\.0, but the version is (.+?)","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"official/legacy/image_classification/dataset_factory.py","lineNumber":464,"sourceCode":"            '`per_replica_batch_size` and enable '\n            '`use_per_replica_batch_size=True`.'.format(\n                self.config.num_devices))\n\n      # The batch size of the dataset will be multiplied by the number of\n      # replicas automatically when strategy.distribute_datasets_from_function\n      # is called, so we use local batch size here.\n      dataset = dataset.batch(\n          self.local_batch_size, drop_remainder=self.is_training)\n    else:\n      dataset = dataset.batch(\n          self.global_batch_size, drop_remainder=self.is_training)\n\n    # Prefetch overlaps in-feed with training\n    dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n\n    if self.config.tf_data_service:\n      if not hasattr(tf.data.experimental, 'service'):\n        raise ValueError('The tf_data_service flag requires Tensorflow version '\n                         '>= 2.3.0, but the version is {}'.format(\n                             tf.__version__))\n      dataset = dataset.apply(\n          tf.data.experimental.service.distribute(\n              processing_mode='parallel_epochs',\n              service=self.config.tf_data_service,\n              job_name='resnet_train'))\n      dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n\n    return dataset\n\n  def parse_record(self, record: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:\n    \"\"\"Parse an ImageNet record from a serialized string Tensor.\"\"\"\n    keys_to_features = {\n        'image/encoded': tf.io.FixedLenFeature((), tf.string, ''),\n        'image/format': tf.io.FixedLenFeature((), tf.string, 'jpeg'),\n        'image/class/label': tf.io.FixedLenFeature([], tf.int64, -1),\n        'image/class/text': tf.io.FixedLenFeature([], tf.string, ''),","sourceCodeStart":446,"sourceCodeEnd":482,"githubUrl":"https://github.com/tensorflow/models/blob/e006f5f0d534913e49c1f1dae87364039fa607e2/official/legacy/image_classification/dataset_factory.py#L446-L482","documentation":"Error \"The tf_data_service flag requires Tensorflow version >= 2.3.0, but the version is {}\" thrown in tensorflow/models.","triggerScenarios":"Thrown at official/legacy/image_classification/dataset_factory.py:464 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":[],"exampleFix":null,"handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"e006f5f0d534913e49c1f1dae87364039fa607e2","analyzedAt":"2026-08-24T14:09:15.576Z","schemaVersion":2},"datasetVersion":"2026-08-24T17:17:21.512Z"}