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
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 by
max_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__
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 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.distribute.experimental.partitioners.MinSizePartitioner\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/distribute/sharded_variable.py#L114-L172) |\n\nPartitioner that allocates a minimum size per shard.\n\nInherits From: [`Partitioner`](../../../../tf/distribute/experimental/partitioners/Partitioner) \n\n tf.distribute.experimental.partitioners.MinSizePartitioner(\n min_shard_bytes=(256 \u003c\u003c 10), max_shards=1, bytes_per_string=16\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|---------------------------------------------------------------------------------------------------------------------------------------|\n| - [Parameter server training with ParameterServerStrategy](https://www.tensorflow.org/tutorials/distribute/parameter_server_training) |\n\nThis partitioner ensures each shard has at least `min_shard_bytes`, and tries\nto allocate as many shards as possible, i.e., keeping shard size as small as\npossible. The maximum number of such shards (upper bound) is given by\n`max_shards`.\n\n#### Examples:\n\n partitioner = MinSizePartitioner(min_shard_bytes=4, max_shards=2)\n partitions = partitioner(tf.TensorShape([6, 1]), tf.float32)\n [2, 1]\n partitioner = MinSizePartitioner(min_shard_bytes=4, max_shards=10)\n partitions = partitioner(tf.TensorShape([6, 1]), tf.float32)\n [6, 1]\n\n # use in ParameterServerStrategy\n # strategy = tf.distribute.experimental.ParameterServerStrategy(\n # cluster_resolver=cluster_resolver, variable_partitioner=partitioner)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------------|--------------------------------------------------------------------------------------------------|\n| `min_shard_bytes` | Minimum bytes of each shard. Defaults to 256K. |\n| `max_shards` | Upper bound on the number of shards. Defaults to 1. |\n| `bytes_per_string` | If the partition value is of type string, this provides an estimate of how large each string is. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `__call__`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/distribute/sharded_variable.py#L166-L172) \n\n __call__(\n shape, dtype, axis=0\n )\n\nPartitions the given `shape` and returns the partition results.\n\nExamples of a partitioner that allocates a fixed number of shards: \n\n partitioner = FixedShardsPartitioner(num_shards=2)\n partitions = partitioner(tf.TensorShape([10, 3], tf.float32), axis=0)\n print(partitions) # [2, 0]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|---------|---------------------------------------------------------------------------|\n| `shape` | a [`tf.TensorShape`](../../../../tf/TensorShape), the shape to partition. |\n| `dtype` | a `tf.dtypes.Dtype` indicating the type of the partition value. |\n| `axis` | The axis to partition along. Default: outermost axis. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A list of integers representing the number of partitions on each axis, where i-th value correponds to i-th axis. ||\n\n\u003cbr /\u003e"]]
View source on GitHub