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 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.distribute.experimental.partitioners.FixedShardsPartitioner\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#L83-L111) |\n\nPartitioner that allocates a fixed number of shards.\n\nInherits From: [`Partitioner`](../../../../tf/distribute/experimental/partitioners/Partitioner) \n\n tf.distribute.experimental.partitioners.FixedShardsPartitioner(\n num_shards\n )\n\n#### Examples:\n\n # standalone usage:\n partitioner = FixedShardsPartitioner(num_shards=2)\n partitions = partitioner(tf.TensorShape([10, 3]), tf.float32)\n [2, 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| `num_shards` | `int`, number of shards to partition. |\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#L107-L111) \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