parameters: (self)
return_statement: return self.strategy.conv_workspace_size_limit
docstring: The workspace limit size in MB unit for choosing cuDNN convolution algorithms.
          The inner funciton of cuDNN obtain the fastest suited algorithm that fits within this memory limit.
          Usually, large workspace size may lead to choose faster algorithms,
          but significant increasing memory workspace. Users need to trade-off between memory and speed.
          Default Value: 4000
  
          Examples:
  
            .. code-block:: python
  
              import paddle
              paddle.enable_static()
              import paddle.distributed.fleet as fleet
              strategy = fleet.DistributedStrategy()
              strategy.conv_workspace_size_limit = 1024
  
              optimizer = paddle.optimizer.SGD(learning_rate=0.01)
              optimizer = fleet.distributed_optimizer(optimizer, strategy)
docstring_summary: The workspace limit size in MB unit for choosing cuDNN convolution algorithms.
          The inner funciton of cuDNN obtain the fastest suited algorithm that fits within this memory limit.
          Usually, large workspace size may lead to choose faster algorithms,
          but significant increasing memory workspace. Users need to trade-off between memory and speed.
          Default Value: 4000
docstring_tokens: The workspace limit size in MB unit for choosing cuDNN convolution algorithms . The inner funciton of cuDNN obtain the fastest suited algorithm that fits within this memory limit . Usually large workspace size may lead to choose faster algorithms but significant increasing memory workspace . Users need to trade - off between memory and speed . Default Value : 4000
function: def conv_workspace_size_limit(self):
          """
          The workspace limit size in MB unit for choosing cuDNN convolution algorithms.
          The inner funciton of cuDNN obtain the fastest suited algorithm that fits within this memory limit.
          Usually, large workspace size may lead to choose faster algorithms,
          but significant increasing memory workspace. Users need to trade-off between memory and speed.
          Default Value: 4000
  
          Examples:
  
            .. code-block:: python
  
              import paddle
              paddle.enable_static()
              import paddle.distributed.fleet as fleet
              strategy = fleet.DistributedStrategy()
              strategy.conv_workspace_size_limit = 1024
  
              optimizer = paddle.optimizer.SGD(learning_rate=0.01)
              optimizer = fleet.distributed_optimizer(optimizer, strategy)
  
          """
          return self.strategy.conv_workspace_size_limit
function_tokens: def conv_workspace_size_limit ( self ) : return self . strategy . conv_workspace_size_limit