# tf.contrib.tpu.TPUEstimator

## Class TPUEstimator

Inherits From: Estimator

Estimator with TPU support.

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.

TPUEstimatorSpec.eval_metrics is a tuple of metric_fn and tensors, where tensors could be a list of Tensors or dict of names to 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).

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.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:
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions={
'predictions': output,
})

tpu_est = TPUEstimator(
model_fn=model_fn,
...,
predict_batch_size=16)

# 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'


# Exporting

Exporting SavedModel support on TPU is not yet implemented. So, export_savedmodel is executed on CPU, even if use_tpu is true.

## Properties

### model_fn

Returns the model_fn which is bound to self.params.

#### Returns:

The model_fn with following signature: def model_fn(features, labels, mode, config)

## Methods

### __init__

__init__(
model_fn=None,
model_dir=None,
config=None,
params=None,
use_tpu=True,
train_batch_size=None,
eval_batch_size=None,
predict_batch_size=None,
batch_axis=None
)


Constructs an TPUEstimator instance.

#### Args:

• model_fn: Model function as required by Estimator. For training, the returned EstimatorSpec cannot have hooks as it is not supported in TPUEstimator.
• 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.
• Predict still happens on CPU.
• 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.

#### Raises:

• ValueError: params has reserved keys already.

### evaluate

evaluate(
input_fn,
steps=None,
hooks=None,
checkpoint_path=None,
name=None
)


Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until: - steps batches are processed, or - input_fn raises an end-of-input exception (OutOfRangeError or StopIteration).

#### Args:

• 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 'tf.data.Dataset' object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
• A tuple (features, labels): Where features is a 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 evaluate model. If None, evaluates until input_fn raises an end-of-input exception.

• hooks: List of SessionRunHook subclass instances. Used for callbacks inside the evaluation call.

• checkpoint_path: Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used.

• 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.

#### Returns:

A dict containing the evaluation metrics specified in model_fn keyed by name, as well as an entry global_step which contains the value of the global step for which this evaluation was performed.

#### Raises:

• ValueError: If steps <= 0.
• ValueError: If no model has been trained, namely model_dir, or the given checkpoint_path is empty.

### export_savedmodel

export_savedmodel(
export_dir_base,
assets_extra=None,
as_text=False,
checkpoint_path=None,
strip_default_attrs=False
)


Exports inference graph as a SavedModel into given dir.

For a detailed guide, see Using SavedModel with Estimators.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single MetaGraphDef saved from this session.

The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

#### Args:

• export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
• serving_input_receiver_fn: A function that takes no argument and returns a ServingInputReceiver or TensorServingInputReceiver.
• assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
• as_text: whether to write the SavedModel proto in text format.
• checkpoint_path: The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
• strip_default_attrs: Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see Stripping Default-Valued Attributes.

#### Returns:

The string path to the exported directory.

#### Raises:

• ValueError: if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

### get_variable_names

get_variable_names()


Returns list of all variable names in this model.

List of names.

#### Raises:

• ValueError: If the Estimator has not produced a checkpoint yet.

### get_variable_value

get_variable_value(name)


Returns value of the variable given by name.

#### Args:

• name: string or a list of string, name of the tensor.

#### Returns:

Numpy array - value of the tensor.

#### Raises:

• ValueError: If the Estimator has not produced a checkpoint yet.

### latest_checkpoint

latest_checkpoint()


Finds the filename of latest saved checkpoint file in model_dir.

#### Returns:

The full path to the latest checkpoint or None if no checkpoint was found.

### predict

predict(
input_fn,
predict_keys=None,
hooks=None,
checkpoint_path=None,
yield_single_examples=True
)


Yields predictions for given features.

#### Args:

• input_fn: A function that constructs the features. Prediction continues until input_fn raises an end-of-input exception (OutOfRangeError or StopIteration). See Premade Estimators for more information. The function should construct and return one of the following:

• A 'tf.data.Dataset' object: Outputs of Dataset object must have same constraints as below.
• features: A Tensor or a dictionary of string feature name to Tensor. features are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
• A tuple, in which case the first item is extracted as features.
• predict_keys: list of str, name of the keys to predict. It is used if the EstimatorSpec.predictions is a dict. If predict_keys is used then rest of the predictions will be filtered from the dictionary. If None, returns all.

• hooks: List of SessionRunHook subclass instances. Used for callbacks inside the prediction call.

• checkpoint_path: Path of a specific checkpoint to predict. If None, the latest checkpoint in model_dir is used.

• yield_single_examples: If False, yield the whole batch as returned by the model_fn instead of decomposing the batch into individual elements. This is useful if model_fn returns some tensors whose first dimension is not equal to the batch size.

#### Yields:

Evaluated values of predictions tensors.

#### Raises:

• ValueError: Could not find a trained model in model_dir.
• ValueError: If batch length of predictions is not the same and yield_single_examples is True.
• ValueError: If there is a conflict between predict_keys and predictions. For example if predict_keys is not None but EstimatorSpec.predictions is not a dict.

### train

train(
input_fn,
hooks=None,
steps=None,
max_steps=None,
saving_listeners=None
)


Trains a model given training data input_fn.

#### Args:

• input_fn: A function that provides input data for training as minibatches. See Premade Estimators for more information. The function should construct and return one of the following:

• A 'tf.data.Dataset' object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
• A tuple (features, labels): Where features is a 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.
• hooks: List of SessionRunHook subclass instances. Used for callbacks inside the training loop.

• steps: Number of steps for which to train model. If None, train forever or train until input_fn generates the OutOfRange error or StopIteration exception. 'steps' works incrementally. If you call two times train(steps=10) then training occurs in total 20 steps. If OutOfRange or StopIteration occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please set max_steps instead. If set, max_steps must be None.

• max_steps: Number of total steps for which to train model. If None, train forever or train until input_fn generates the OutOfRange error or StopIteration exception. If set, steps must be None. If OutOfRange or StopIteration occurs in the middle, training stops before max_steps steps. Two calls to train(steps=100) means 200 training iterations. On the other hand, two calls to train(max_steps=100) means that the second call will not do any iteration since first call did all 100 steps.

• saving_listeners: list of CheckpointSaverListener objects. Used for callbacks that run immediately before or after checkpoint savings.

#### Returns:

self, for chaining.

#### Raises:

• ValueError: If both steps and max_steps are not None.
• ValueError: If either steps or max_steps is <= 0.