tf.contrib.training.GreedyLoadBalancingStrategy

Class GreedyLoadBalancingStrategy

Defined in tensorflow/contrib/training/python/training/device_setter.py.

Returns the least-loaded ps task for op placement.

The load is calculated by a user-specified load function passed in at construction. There are no units for load, and the load function is responsible for providing an internally consistent measure.

Note that this strategy is very sensitive to the exact order in which ps ops (typically variables) are created, as it greedily places ops on the least-loaded ps at the point each op is processed.

One reasonable heuristic is the byte_size_load_fn, which estimates load as the number of bytes that would be used to store and transmit the entire variable. More advanced load functions could consider the difference in access patterns across ops, or trade off CPU-intensive ops with RAM-intensive ops with network bandwidth.

This class is intended to be used as a ps_strategy in tf.train.replica_device_setter.

Methods

__init__

__init__(
    num_tasks,
    load_fn
)

Create a new LoadBalancingStrategy.

Args:

  • num_tasks: Number of ps tasks to cycle among.
  • load_fn: A callable that takes an Operation and returns a numeric load value for that op.

__call__

__call__(op)

Choose a ps task index for the given Operation.

Args:

  • op: A Operation to be placed on ps.

Returns:

The next ps task index to use for the Operation. Greedily places the op on the least-loaded ps task so far, as determined by the load function.