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Partitioner that keeps shards below max_shard_bytes
.
Inherits From: Partitioner
tf.distribute.experimental.partitioners.MaxSizePartitioner(
max_shard_bytes, max_shards=None, bytes_per_string=16
)
This partitioner ensures each shard has at most max_shard_bytes
, and tries
to allocate as few shards as possible, i.e., keeping shard size as large
as possible.
If the partitioner hits the max_shards
limit, then each shard may end up
larger than max_shard_bytes
. By default max_shards
equals None
and no
limit on the number of shards is enforced.
Examples:
partitioner = MaxSizePartitioner(max_shard_bytes=4)
partitions = partitioner(tf.TensorShape([6, 1]), tf.float32)
[6, 1]
partitioner = MaxSizePartitioner(max_shard_bytes=4, max_shards=2)
partitions = partitioner(tf.TensorShape([6, 1]), tf.float32)
[2, 1]
partitioner = MaxSizePartitioner(max_shard_bytes=1024)
partitions = partitioner(tf.TensorShape([6, 1]), tf.float32)
[1, 1]
# use in ParameterServerStrategy
# strategy = tf.distribute.experimental.ParameterServerStrategy(
# cluster_resolver=cluster_resolver, variable_partitioner=partitioner)
Methods
__call__
__call__(
shape, dtype, axis=0
)
Partitions the given shape
and returns the partition results.
Examples of a partitioner that allocates a fixed number of shards:
partitioner = FixedShardsPartitioner(num_shards=2)
partitions = partitioner(tf.TensorShape([10, 3], tf.float32), axis=0)
print(partitions) # [2, 0]
Args | |
---|---|
shape
|
a tf.TensorShape , the shape to partition.
|
dtype
|
a tf.dtypes.Dtype indicating the type of the partition value.
|
axis
|
The axis to partition along. Default: outermost axis. |
Returns | |
---|---|
A list of integers representing the number of partitions on each axis, where i-th value correponds to i-th axis. |