Automated adaption of batch_size to available GPU memory

I am running a different set of models upon different input sizes and so it is not very straightforward to choose an optimal batch-size for each model/input combination.

I came up with the following solution I maybe MXNet should provide something similar.

``` julia
global const GPU_MEM_MAX = 11000 # M6000 has 12000 MiB

"""
size dim1...dim3 example (256, 256, 1)
"""
function memoryPerBatch(net, size)
exec = mx.simple_bind(net, mx.gpu(), data=(size..., 1))
dbg_str = mx.debug_str(exec)
mem_str = split(dbg_str, ['\n'])[end-2]
m = match(r"Total (\d+) MB allocated", mem_str)
finalize(exec.handle)
exec = nothing
parse(Int, m.captures[1])
end

function guessBatchSize(net, size, max = GPU_MEM_MAX)
mem = memoryPerBatch(net, size)
info("Memory required per batch: $mem MB")
nbatch = max ÷ mem
nbatch = ispow2(nbatch) ? nbatch : prevpow2(nbatch)
info("Using a batch_size of $nbatch. Total memory ≈ $(nbatch*mem) MB")
return nbatch
end
```

Start with the Julia prototype's memoryPerBatch and guessBatchSize functions, then trace the corresponding mx.simple_bind, mx.gpu, and mx.debug_str entry points. Define how automatic batch sizing should use available GPU memory and what result the library should return for varying models and input sizes.

Written by the indexing model from the issue text.