tensor.io – Tensor IO Ops

File operation

• Load from disk with the function load and its associated op LoadFromDisk

MPI operation

• Non-blocking transfer: isend and irecv.
• Blocking transfer: send and recv

Details

• class theano.tensor.io.LoadFromDisk(dtype, broadcastable, mmap_mode=None)[source]
• An operation to load an array from disk.

See also

load

Notes

Non-differentiable.

• class theano.tensor.io.MPIRecv(source, tag, shape, dtype)[source]
• An operation to asynchronously receive an array to a remote host using MPI.

See also

MPIRecv, MPIWait

Notes

Non-differentiable.

• class theano.tensor.io.MPIRecvWait(tag)[source]
• An operation to wait on a previously received array using MPI.

See also

MPIRecv

Notes

Non-differentiable.

• class theano.tensor.io.MPISend(dest, tag)[source]
• An operation to asynchronously Send an array to a remote host using MPI.

See also

MPIRecv, MPISendWait

Notes

Non-differentiable.

• class theano.tensor.io.MPISendWait(tag)[source]
• An operation to wait on a previously sent array using MPI.

See also

MPISend

Notes

Non-differentiable.

• theano.tensor.io.irecv(shape, dtype, source, tag)[source]
• Non-blocking receive.

• theano.tensor.io.isend(var, dest, tag)[source]
• Non blocking send.

• theano.tensor.io.load(path, dtype, broadcastable, mmap_mode=None)[source]
• Load an array from an .npy file.

Parameters:

• path – A Generic symbolic variable, that will contain a string
• dtype (data-type) – The data type of the array to be read.
• broadcastable – The broadcastable pattern of the loaded array, for instance,(False,) for a vector, (False, True) for a column,(False, False) for a matrix.
• mmap_mode – How the file will be loaded. None means that thedata will be copied into an array in memory, ‘c’ means that the filewill be mapped into virtual memory, so only the parts that areneeded will be actually read from disk and put into memory.Other modes supported by numpy.load (‘r’, ‘r+’, ‘w+’) cannotbe supported by Theano.

Examples

>>> from theano import *
>>> path = Variable(Generic())
>>> x = tensor.load(path, 'int64', (False,))
>>> y = x*2
>>> fn = function([path], y)
>>> fn("stored-array.npy")  
array([0, 2, 4, 6, 8], dtype=int64)

• theano.tensor.io.mpisend_wait_key(_a)[source]
• Wait as long as possible on Waits, Start Send/Recvs early.

• theano.tensor.io.mpitag_key(_a)[source]
• Break MPI ties by using the variable tag - prefer lower tags first.

• theano.tensor.io.recv(shape, dtype, source, tag)[source]
• Blocking receive.

• theano.tensor.io.send(var, dest, tag)[source]
• Blocking send.