tf.compat.v1.estimator.tpu.TPUEstimator

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.

Evaluation

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(
        mode=mode,
        loss=loss,
        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(
        mode=mode,
        loss=loss,
        eval_metrics=(metric_fn, {
            'labels': labels,
            'logits': final_layer_output,
        }))

Prediction

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 = tf.data.Dataset.from_tensor_slices(images)
  dataset = dataset.map(lambda 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:
    re