tf.distribute.experimental.partitioners.MinSizePartitioner | TensorFlow v2.16.1 (original) (raw)
tf.distribute.experimental.partitioners.MinSizePartitioner
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Partitioner that allocates a minimum size per shard.
Inherits From: Partitioner
tf.distribute.experimental.partitioners.MinSizePartitioner(
min_shard_bytes=(256 << 10), max_shards=1, bytes_per_string=16
)
Used in the notebooks
| Used in the tutorials |
|---|
| Parameter server training with ParameterServerStrategy |
This partitioner ensures each shard has at least min_shard_bytes, and tries to allocate as many shards as possible, i.e., keeping shard size as small as possible. The maximum number of such shards (upper bound) is given bymax_shards.
Examples:
partitioner = MinSizePartitioner(min_shard_bytes=4, max_shards=2)
partitions = partitioner(tf.TensorShape([6, 1]), tf.float32)
[2, 1]
partitioner = MinSizePartitioner(min_shard_bytes=4, max_shards=10)
partitions = partitioner(tf.TensorShape([6, 1]), tf.float32)
[6, 1]
``
# use in ParameterServerStrategy
# strategy = tf.distribute.experimental.ParameterServerStrategy(
# cluster_resolver=cluster_resolver, variable_partitioner=partitioner)
| Args | |
|---|---|
| min_shard_bytes | Minimum bytes of each shard. Defaults to 256K. |
| max_shards | Upper bound on the number of shards. Defaults to 1. |
| bytes_per_string | If the partition value is of type string, this provides an estimate of how large each string is. |
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. |