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Estimator with TPU support.

Inherits From: Estimator

TPUEstimator also supports training on CPU and GPU. You don't need to define a separate tf.estimator.Estimator.

TPUEstimator handles many of the details of running on TPU devices, such as replicating inputs and models for each core, and returning to host periodically to run hooks.

TPUEstimator transforms a global batch size in params to a per-shard batch size when calling the input_fn and model_fn. Users should specify global batch size in constructor, and then get the batch size for each shard in input_fn and model_fn by params['batch_size'].

  • For training, model_fn gets per-core batch size; input_fn may get per-core or per-host batch size depending on per_host_input_for_training in TPUConfig (See docstring for TPUConfig for details).

  • For evaluation and prediction, model_fn gets per-core batch size and input_fn get per-host batch size.


model_fn should return TPUEstimatorSpec, which expects the eval_metrics for TPU evaluation. If eval_on_tpu is False, the evaluation will execute on CPU or GPU; in this case the following discussion on TPU evaluation does not apply.

TPUEstimatorSpec.eval_metrics is a tuple of metric_fn and tensors, where tensors could be a list of any nested structure of Tensors (See TPUEstimatorSpec for details). metric_fn takes the tensors and returns a dict from metric string name to the result of calling a metric function, namely a (metric_tensor, update_op) tuple.

One can set use_tpu to False for testing. All training, evaluation, and predict will be executed on CPU. input_fn and model_fn will receive train_batch_size or eval_batch_size unmodified as params['batch_size'].

Current limitations:

  1. TPU evaluation only works on a single host (one TPU worker) except BROADCAST mode.

  2. input_fn for evaluation should NOT raise an end-of-input exception (OutOfRangeError or StopIteration). And all evaluation steps and all batches should have the same size.

Example (MNIST):

# The metric Fn which runs on CPU.
def metric_fn(labels, logits):
  predictions = tf.argmax(logits, 1)
  return {
    'accuracy': tf.compat.v1.metrics.precision(
        labels=labels, predictions=predictions),

# Your model Fn which runs on TPU (eval_metrics is list in this example)
def model_fn(features, labels, mode, config, params):
  logits = ...

  if mode = tf.estimator.ModeKeys.EVAL:
    return tpu_estimator.TPUEstimatorSpec(
        eval_metrics=(metric_fn, [labels, logits]))

# or specify the eval_metrics tensors as dict.
def model_fn(features, labels, mode, config, params):
  final_layer_output = ...

  if mode = tf.estimator.ModeKeys.EVAL:
    return tpu_estimator.TPUEstimatorSpec(
        eval_metrics=(metric_fn, {
            'labels': labels,
            'logits': final_layer_output,


Prediction on TPU is an experimental feature to support large batch inference. It is not designed for latency-critical system. In addition, due to some usability issues, for prediction with small dataset, CPU .predict, i.e., creating a new TPUEstimator instance with use_tpu=False, might be more convenient.

Current limitations:

  1. TPU prediction only works on a single host (one TPU worker).

  2. input_fn must return a Dataset instance rather than features. In fact, .train() and .evaluate() also support Dataset as return value.

Example (MNIST):

height = 32
width = 32
total_examples = 100

def predict_input_fn(params):
  batch_size = params['batch_size']

  images = tf.random.uniform(
      [total_examples, height, width, 3], minval=-1, maxval=1)

  dataset =
  dataset = images: {'image': images})

  dataset = dataset.batch(batch_size)
  return dataset

def model_fn(features, labels, params, mode):
   # Generate predictions, called 'output', from features['image']

  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.contrib.tpu.TPUEstimatorSpec(
            'predictions': output,
            'is_padding': features['is_padding']

tpu_est = TPUEstimator(

# Fully consume the generator so that TPUEstimator can shutdown the TPU
# system.
for item in tpu_est.predict(input_fn=input_fn):
  # Filter out item if the `is_padding` is 1.
  # Process the 'predictions'


export_saved_model exports 2 metagraphs, one with saved_model.SERVING, and another with saved_model.SERVING and saved_model.TPU tags. At serving time, these tags are used to select the appropriate metagraph to load.

Before running the graph on TPU, the TPU system needs to be initialized. If TensorFlow Serving model-server is used, this is done automatically. If not, please use

There are two versions of the API: ExportSavedModelApiVersion.V1 and V2.

In V1, the exported CPU graph is model_fn as it is. The exported TPU graph wraps tpu.rewrite() and TPUPartitionedCallOp around model_fn so model_fn is on TPU by default. To place ops on CPU, tpu.outside_compilation(host_call, logits) can be used.


def model_fn(features, labels, mode, config, params):
  logits = ...
  export_outputs = {
    'logits': export_output_lib.PredictOutput(
      {'logits': logits})

  def host_call(logits):
    class_ids = math_ops.argmax(logits)
    classes = string_ops.as_string(class_ids)
    export_outputs['classes'] =

  tpu.outside_compilation(host_call, logits)


In V2, export_saved_model() sets up params['use_tpu'] flag to let the user know if the code is exporting to TPU (or not). When params['use_tpu'] is True, users need to call tpu.rewrite(), TPUPartitionedCallOp and/or batch_function(). Alternatively use inference_on_tpu() which is a convenience wrapper of the three.

  def model_fn(features, labels, mode, config, params):
    # This could be some pre-processing on CPU like calls to input layer with
    # embedding columns.
    x2 = features['x'] * 2

    def computation(input_tensor):
      return layers.dense(
          input_tensor, 1, kernel_initializer=init_ops.zeros_initializer())

    inputs = [x2]
    if params['use_tpu']:
      predictions = array_ops.identity(
          tpu_estimator.inference_on_tpu(computation, inputs,
          num_batch_threads=1, max_batch_size=2, batch_timeout_micros=100),
      predictions = array_ops.identity(
          computation(*inputs), name='predictions')
    key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
    export_outputs = {
        key: export_lib.PredictOutput({'prediction': predictions})

TIP: V2 is recommended as it is more flexible (eg: batching, etc).

model_fn Model function as required by Estimator which returns EstimatorSpec or TPUEstimatorSpec. training_hooks, 'evaluation_hooks', and prediction_hooks must not capure any TPU Tensor inside the model_fn.
model_dir Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. If None, the model_dir in config will be used if set. If both are set, they must be same. If both are None, a temporary directory will be used.
config An tpu_config.RunConfig configuration object. Cannot be None.
params An optional dict of hyper parameters that will be passed into input_fn and model_fn. Keys are names of parameters, values are basic python types. There are reserved keys for TPUEstimator, including 'batch_size'.
use_tpu A bool indicating whether TPU support is enabled. Currently, - TPU training and evaluation respect this bit, but eval_on_tpu can override execution of eval. See below.
train_batch_size An int representing the global training batch size. TPUEstimator transforms this global batch size to a per-shard batch size, as params['batch_size'], when calling input_fn and model_fn. Cannot be None if use_tpu is True. Must be divisible by total number of replicas.
eval_batch_size An int representing evaluation batch size. Must be divisible by total number of replicas.
predict_batch_size An int representing the prediction batch size. Must be divisible by total number of replicas.
batch_axis A python tuple of int values describing how each tensor produced by the Estimator input_fn should be split across the TPU compute shards. For example, if your input_fn produced (images, labels) where the images tensor is in HWCN format, your shard dimensions would be [3, 0], where 3 corresponds to the N dimension of your images Tensor, and 0 corresponds to the dimension along which to split the labels to match up with the corresponding images. If None is supplied, and per_host_input_for_training is True, batches will be sharded based on the major dimension. If tpu_config.per_host_input_for_training is False or PER_HOST_V2, batch_axis is ignored.
eval_on_tpu If False, evaluation runs on CPU or GPU. In this case, the model_fn must return EstimatorSpec when called with mode as EVAL.
export_to_tpu If True, export_saved_model() exports a metagraph for serving on TPU. Note that unsupported export modes such as EVAL will be ignored. For those modes, only a CPU model will be exported. Currently, export_to_tpu only supports PREDICT.
export_to_cpu If True, export_saved_model() exports a metagraph for serving on CPU.
warm_start_from Optional string filepath to a checkpoint or SavedModel to warm-start from, or a tf.estimator.WarmStartSettings object to fully configure warm-starting. If the string filepath is provided instead of a WarmStartSettings, then all variables are warm-started, and it is assumed that vocabularies and Tensor names are unchanged.
embedding_config_spec Optional EmbeddingConfigSpec instance to support using TPU embedding.
export_saved_model_api_version ExportSavedModelApiVersion, V1 or V2. With V1, export_saved_model() adds rewrite() and TPUPartitionedCallOp() for user; while in v2, user is expected to add rewrite(), TPUPartitionedCallOp() etc in their model_fn. A helper function inference_on_tpu is provided for V2. includes examples for both versions i.e. TPUEstimatorExportTest and TPUEstimatorExportV2Test.

ValueError params has reserved keys already.



model_fn Returns the model_fn which is bound to self.params.



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Shows the directory name where evaluation metrics are dumped.

name Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

A string which is the path of directory contains evaluation metrics.


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Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until:

input_fn A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following: * A object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below. * A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
steps Number of steps for which to