tf.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
)
Args |
|---|
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.
|
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