tensorflow/models · error · ValueError

`OneDeviceStrategy` can not be used for more than one device

Error message

`OneDeviceStrategy` can not be used for more than one device.

What it means

Error "`OneDeviceStrategy` can not be used for more than one device." thrown in tensorflow/models.

Source

Thrown at official/common/distribute_utils.py:186

          == tf.tpu.experimental.HardwareFeature.EmbeddingFeature.V2  # pyrefly: ignore[missing-attribute]
      ):
        tpu_metadata = cluster_resolver.get_tpu_system_metadata()
        device_assignment = tf.tpu.experimental.DeviceAssignment.build(
            topology, num_replicas=tpu_metadata.num_cores
        )

    return tf.distribute.TPUStrategy(
        cluster_resolver, experimental_device_assignment=device_assignment)

  if distribution_strategy == "multi_worker_mirrored":
    return tf.distribute.experimental.MultiWorkerMirroredStrategy(
        communication=_collective_communication(all_reduce_alg))

  if distribution_strategy == "one_device":
    if num_gpus == 0:
      return tf.distribute.OneDeviceStrategy("device:CPU:0")
    if num_gpus > 1:
      raise ValueError("`OneDeviceStrategy` can not be used for more than "
                       "one device.")
    return tf.distribute.OneDeviceStrategy("device:GPU:0")

  if distribution_strategy == "mirrored":
    if num_gpus == 0:
      devices = ["device:CPU:0"]
    else:
      devices = ["device:GPU:%d" % i for i in range(num_gpus)]
    return tf.distribute.MirroredStrategy(
        devices=devices,
        cross_device_ops=_mirrored_cross_device_ops(all_reduce_alg, num_packs))

  if distribution_strategy == "parameter_server":
    cluster_resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
    return tf.distribute.experimental.ParameterServerStrategy(cluster_resolver)

  raise ValueError("Unrecognized Distribution Strategy: %r" %
                   distribution_strategy)

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/common/distribute_utils.py:186 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/9ceeb3e82eab00ea. Report an issue: GitHub.