tf.distribute.experimental.partitioners.FixedShardsPartitioner
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Partitioner that allocates a fixed number of shards.
Inherits From: Partitioner
tf.distribute.experimental.partitioners.FixedShardsPartitioner(
num_shards
)
Examples:
# standalone usage:
partitioner = FixedShardsPartitioner(num_shards=2)
partitions = partitioner(tf.TensorShape([10, 3]), tf.float32)
[2, 1]
# use in ParameterServerStrategy
# strategy = tf.distribute.experimental.ParameterServerStrategy(
# cluster_resolver=cluster_resolver, variable_partitioner=partitioner)
Args |
num_shards
|
int , number of shards to partition.
|
Methods
__call__
View source
__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.
|
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Last updated 2024-01-23 UTC.
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