stable_learning_control.utils.mpi_utils.mpi_tools

Module used for managing MPI processes.

Module Contents

Functions

mpi_fork(n[, bind_to_core])	Re-launches the current script with workers linked by MPI.
msg(m[, string])	Send message from one MPI process to the other.
pprint([input_str, end, comm])	Print for MPI parallel programs: Only rank 0 prints str.
proc_id()	Get rank of calling process.
allreduce(*args, **kwargs)	Reduced results of a operation across all processes.
num_procs()	Count active MPI processes.
broadcast(x[, root])	Broadcast variable to other MPI processes.
mpi_op(x, op)	Perform a MPI operation.
mpi_sum(x)	Take the sum of a scalar or vector over MPI processes.
mpi_avg(x)	Average a scalar or vector over MPI processes.
mpi_statistics_scalar(x[, with_min_and_max])	Get mean/std and optional min/max of scalar x across MPI processes.

stable_learning_control.utils.mpi_utils.mpi_tools.mpi_fork(n, bind_to_core=False)

Re-launches the current script with workers linked by MPI.

Also, terminates the original process that launched it.

Taken almost without modification from the Baselines function of the same name.

Parameters:

• n (int) – Number of process to split into.

• bind_to_core (bool, optional) – Bind each MPI process to a core. Defaults to False.

stable_learning_control.utils.mpi_utils.mpi_tools.msg(m, string='')

Send message from one MPI process to the other.

Parameters:

• m (str) – Message you want to send.

• string (str, optional) – Additional process description. Defaults to "".

stable_learning_control.utils.mpi_utils.mpi_tools.pprint(input_str='', end='\n', comm=MPI.COMM_WORLD)

Print for MPI parallel programs: Only rank 0 prints str.

Parameters:

• input_str (str) – The string you want to print.

• end (str) – The print end character.

• comm (mpi4py.MPI.COMM_WORLD) – MPI communicator.

stable_learning_control.utils.mpi_utils.mpi_tools.proc_id()

Get rank of calling process.

stable_learning_control.utils.mpi_utils.mpi_tools.allreduce(*args, **kwargs)

Reduced results of a operation across all processes.

Parameters:

• *args – All args to pass to thunk.

• **kwargs – All kwargs to pass to thunk.

Returns:

Result object.

Return type:

object

stable_learning_control.utils.mpi_utils.mpi_tools.num_procs()

Count active MPI processes.

Returns:

The number of mpi processes.

Return type:

int

stable_learning_control.utils.mpi_utils.mpi_tools.broadcast(x, root=0)

Broadcast variable to other MPI processes.

Parameters:

• x (object) – Variable you want to broadcast.

• root (int, optional) – Rank of the root process. Defaults to 0.

stable_learning_control.utils.mpi_utils.mpi_tools.mpi_op(x, op)

Perform a MPI operation.

Parameters:

• x (object) – Python variable.

• op (mpi4py.MPI.Op) – Operation type

Returns:

Reduced mpi operation result.

Return type:

object

stable_learning_control.utils.mpi_utils.mpi_tools.mpi_sum(x)

Take the sum of a scalar or vector over MPI processes.

Parameters:

x (object) – Python variable.

Returns:

Reduced sum.

Return type:

object

stable_learning_control.utils.mpi_utils.mpi_tools.mpi_avg(x)

Average a scalar or vector over MPI processes.

Parameters:

x (object) – Python variable.

Returns:

Reduced average.

Return type:

object

stable_learning_control.utils.mpi_utils.mpi_tools.mpi_statistics_scalar(x, with_min_and_max=False)

Get mean/std and optional min/max of scalar x across MPI processes.

Parameters:

• x – An array containing samples of the scalar to produce statistics for.

• with_min_and_max (bool, optional) – If true, return min and max of x in addition to mean and std. Defaults to False.

Returns:

Reduced mean and standard deviation.

Return type:

tuple