AI::MXNet::Executor::Group - Manager for a group of executors working in different contexts.
DataParallelExecutorGroup is a group of executors that lives on a group of devices. This is a helper class used to implement data parallelization. Each mini-batch will be split and run on the devices. Parameters for constructor ---------- symbol : AI::MXNet::Symbol The common symbolic computation graph for all executors. contexts : ArrayRef[AI::MXNet::Context] A array ref of contexts. workload : ArrayRef[Num] If not undef, could be an array ref of numbers that specify the workload to be assigned to different context. Larger number indicate heavier workload. data_shapes : ArrayRef[NameShape|AI::MXNet::DataDesc] Should be a array ref of [name, shape] array refs, for the shapes of data. Note the order is important and should be the same as the order that the `DataIter` provide the data. label_shapes : Maybe[ArrayRef[NameShape|AI::MXNet::DataDesc]] Should be a array ref of [$name, $shape] array refs, for the shapes of label. Note the order is important and should be the same as the order that the `DataIter` provide the label. param_names : ArrayRef[Str] A array ref of strings, indicating the names of parameters (e.g. weights, filters, etc.) in the computation graph. for_training : Bool Indicate whether the executors should be bind for training. When not doing training, the memory for gradients will not be allocated. inputs_need_grad : Bool Indicate whether the gradients for the input data should be computed. This is currently not used. It will be useful for implementing composition of modules. shared_group : AI::MXNet::DataParallelExecutorGroup Default is undef. This is used in bucketing. When not undef, it should be a executor group corresponding to a different bucket. In other words, it will correspond to a different symbol with the same set of parameters (e.g. unrolled RNNs with different lengths). In this case the memory regions of the parameters will be shared. logger : Logger Default is AI::MXNet::Logging->get_logger. fixed_param_names: Maybe[ArrayRef[Str]] Indicate parameters to be fixed during training. Parameters in this array ref will not allocate space for gradient, nor do gradient calculation. grad_req : ArrayRef[GradReq]|HashRef[GradReq]|GradReq Requirement for gradient accumulation. Can be 'write', 'add', or 'null' (default to 'write'). Can be specified globally (str) or for each argument (array ref, hash ref). state_names: Maybe[ArrayRef[Str]]
Bind executors on their respective devices. Parameters ---------- $data_shapes : ArrayRef[AI::MXNet::DataDesc] $label_shapes : Maybe[ArrayRef[AI::MXNet::DataDesc]] $shared_group : Maybe[AI::MXNet::DataParallelExecutorGroup] $reshape : Bool
Reshape executors. Parameters ---------- $data_shapes : ArrayRef[AI::MXNet::DataDesc] $label_shapes : Maybe[ArrayRef[AI::MXNet::DataDesc]]
Assign, i.e. copy parameters to all the executors. Parameters ---------- $arg_params : HashRef[AI::MXNet::NDArray] A dictionary of name to AI::MXNet::NDArray parameter mapping. $aux_params : HashRef[AI::MXNet::NDArray] A dictionary of name to AI::MXNet::NDArray auxiliary variable mapping.
Copy data from each executor to arg_params and aux_params. Parameters ---------- $arg_params : HashRef[AI::MXNet::NDArray] target parameter arrays $aux_params : HashRef[AI::MXNet::NDArray] target aux arrays Notes ----- - This function will inplace update the NDArrays in arg_params and aux_params.
Split the data_batch according to a workload and run forward on each devices. Parameters ---------- data_batch : AI::MXNet::DataBatch Or could be any object implementing similar interface. is_train : bool The hint for the backend, indicating whether we are during training phase. Default is undef, then the value $self->for_training will be used.
Gets outputs of the previous forward computation. Parameters ---------- merge_multi_context : bool Default is 1. In the case when data-parallelism is used, the outputs will be collected from multiple devices. A 1 value indicates that we should merge the collected results so that they look like from a single executor. Returns ------- If merge_multi_context is 1, it is [$out1, $out2]. Otherwise, it is [[$out1_dev1, $out1_dev2], [$out2_dev1, $out2_dev2]]. All the output elements are `AI::MXNet::NDArray`.
Get the gradients with respect to the inputs of the module. Parameters ---------- merge_multi_context : bool Default is 1. In the case when data-parallelism is used, the outputs will be collected from multiple devices. A 1 value indicates that we should merge the collected results so that they look like from a single executor. Returns ------- If merge_multi_context is 1, it is [$grad1, $grad2]. Otherwise, it is [[$grad1_dev1, $grad1_dev2], [$grad2_dev1, $grad2_dev2]]. All the output elements are AI::MXNet::NDArray.
Run backward on all devices. A backward should be called after a call to the forward function. Backward cannot be called unless $self->for_training is 1. Parameters ---------- out_grads : NDArray or array ref of NDArray, optional Gradient on the outputs to be propagated back. This parameter is only needed when bind is called on outputs that are not a loss function.
Accumulate the performance according to eval_metric on all devices. Parameters ---------- eval_metric : AI::MXNet::EvalMetric The metric used for evaluation. labels : array ref of NDArray Typically comes from label of AI::MXNet::DataBatch.
Get the sliced shapes for the i-th executor. Parameters ---------- shapes : array ref of (str, array ref) The original (name, shape) pairs. i : int Which executor we are dealing with.
Install monitor on all executors Parameters ---------- $mon : AI::MXNet::Monitor
1 POD Error
The following errors were encountered while parsing the POD:
=cut found outside a pod block. Skipping to next block.
To install AI::MXNet, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::MXNet
CPAN shell
perl -MCPAN -e shell install AI::MXNet
For more information on module installation, please visit the detailed CPAN module installation guide.