tf.compat.v1.estimator.tpu.TPUConfig

TPU related configuration required by TPUEstimator.

tf.compat.v1.estimator.tpu.TPUConfig(
    iterations_per_loop=2, num_shards=None, num_cores_per_replica=None,
    per_host_input_for_training=True, tpu_job_name=None,
    initial_infeed_sleep_secs=None, input_partition_dims=None,
    eval_training_input_configuration=InputPipelineConfig.PER_HOST_V1,
    experimental_host_call_every_n_steps=1,
    experimental_allow_per_host_v2_parallel_get_next=False,
    experimental_feed_hook=None
)

Migrate to TF2

TPU Estimator manages its own TensorFlow graph and session, so it is not compatible with TF2 behaviors. We recommend that you migrate to the newer tf.distribute.TPUStrategy. See the TPU guide for details.

Description

iterations_per_loop This is the number of train steps running in TPU system before returning to CPU host for each Session.run. This means global step is increased iterations_per_loop times in one Session.run. It is recommended to be set as number of global steps for next checkpoint. Note that in evaluation don't use this value, instead we run total eval steps on TPU for a single Session.run. [Experimental]: iterations_per_loop can be specified as a time interval. To specify N seconds in one Session.run, one can specify it as Ns and substitute the N with the N with the number of desired seconds. Alternatively, the unit of time can also be specified in minutes or hours, e.g. 3600s or 60m or 1h.
num_shards (Deprecated, ignored by TPUEstimator). The number of model replicas in the system. For non-model-parallelism case, this number equals the total number of TPU cores. For model-parallelism, the total number of TPU cores equals num_cores_per_replica * num_shards.
num_cores_per_replica Defaults to None, which disables model parallelism. An integer which describes the number of TPU cores per model replica. This is required by model-parallelism which enables partitioning the model to multiple cores. Currently num_cores_per_replica must be 1, 2, 4, or 8.
per_host_input_for_training If True, for PER_HOST_V1, the input_fn is invoked once on each host, and the number of hosts must be smaller or equal to the number of replicas. For PER_HOST_V2, the input_fn is invoked once for each host (if the number of hosts is less than the number of replicas) or replica (if the number of replicas is less than the number of hosts. With the per-core input pipeline configuration, it is invoked once for each core. With a global batch size train_batch_size in TPUEstimator constructor, the batch size for each shard is train_batch_size // #hosts in the True or PER_HOST_V1 mode. In PER_HOST_V2 mode, it is train_batch_size // #cores. In BROADCAST mode, input_fn is only invoked once on host 0 and the tensors are broadcasted to all other replicas. The batch size equals to train_batch_size. With the per-core input pipeline configuration, the shard batch size is also train_batch_size // #cores. Note: per_host_input_for_training==PER_SHARD_V1 only supports mode.TRAIN.
tpu_job_name The name of the TPU job. Typically, this name is auto-inferred within TPUEstimator, however when using ClusterSpec propagation in more esoteric cluster configurations, you may need to specify the job name as a string.
initial_infeed_sleep_secs The number of seconds the infeed thread should wait before enqueueing the first batch. This helps avoid timeouts for models that require a long compilation time.
input_partition_dims A nested list to describe the partition dims for all the tensors from input_fn(). The structure of input_partition_dims must match the structure of features and labels from input_fn(). The total number of partitions must match num_cores_per_replica. For example, if input_fn() returns two tensors: images with shape [N, H, W, C] and labels [N]. input_partition_dims = [[1, 2, 2, 1], None] will split the images to 4 pieces and feed into 4 TPU cores. labels tensor are directly broadcasted to all the TPU cores since the partition dims is None. Current limitations: This feature is only supported with the PER_HOST_V2 input mode.
eval_training_input_configuration If SLICED, input_fn is only invoked once on host 0 and the tensors are broadcasted to all other replicas. Unlike per_host_input_for_training=BROADCAST, each replica will only get a slice of the data instead of a whole copy. If PER_HOST_V1, the behaviour is determined by per_host_input_for_training.
experimental_host_call_every_n_steps Within a training loop, this argument sets how often host calls are performed during training. Host calls will be evaluated every n steps within a training loop where n is the value of this argument.
experimental_allow_per_host_v2_parallel_get_next When enabled, allows concurrent execution of dataset get next calls when using PER_HOST_V2 input. May result in a performance increase for models with a small step time, but as a consequence TPUEstimator may non-deterministically distribute batches to different cores, rather than guaranteeing round robin behavior.
experimental_feed_hook This is a class which user can provide to the TPU estimator to override the default TPUInfeedOutfeedSessionHook implementation and add customized implementatioin to handle infeed outfeed logic. If given class is None, TPU estimator uses default TPUInfeedOutfeedSessionHook implementation in tpu_estimator.py. If not None, TPU estimator uses this customized tpu infeed outfeed session hook class rather to override the default one.

ValueError If num_cores_per_replica is not 1, 2, 4, 8, ..., 128.

iterations_per_loop

num_shards

num_cores_per_replica

per_host_input_for_training

tpu_job_name

initial_infeed_sleep_secs

input_partition_dims

eval_training_input_configuration

experimental_host_call_every_n_steps

experimental_allow_per_host_v2_parallel_get_next

experimental_feed_hook