MXNet - Perl API
MXNet supports the Perl programming language. The MXNet Perl package brings flexible and efficient GPU computing and state-of-art deep learning to Perl. It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl. It also lets you construct and customize the state-of-art deep learning models in Perl, and apply them to tasks, such as image classification and data science challenges.
One important thing to internalize is that Perl interface is written to be as close as possible to the Python’s API, so most if not all of Python’s documentation and examples should just work in Perl after making few changes in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ; and in 99% of cases that’s all that is needed there. In addition please refer to excellent metacpan doc interface and to very detailed MXNet Python API Documentation.
AI::MXNet supports new imperative PyTorch like Gluon MXNet interface. Please get acquainted with this new interface at Dive into Deep Learning.
For specific Perl Gluon usage please refer to Perl examples and tests directories on github, but be assured that the Python and Perl usage are extremely close in order to make the use of the Python Gluon docs and examples as easy as possible.
AI::MXNet is seamlessly glued with PDL, the C++ level state can be easily initialized from PDL and the results can be transferred to PDL objects in order to allow you to use all the glory and power of the PDL!
Here is how you can perform tensor or matrix computation in Perl with AI::MXNet and PDL:
pdl> use AI::MXNet qw(mx); # creates 'mx' module on the fly with the interface close to the Python's API pdl> print $arr = mx->nd->ones([2, 3]) <AI::MXNet::NDArray 2x3 @cpu(0)> pdl> print Data::Dumper::Dumper($arr->shape) $VAR1 = [ 2, 3 ]; pdl> print (($arr*2)->aspdl) ## converts AI::MXNet::NDArray object to PDL object [ [2 2 2] [2 2 2] ] pdl> print $arr = mx->nd->array([[1,2],[3,4]]) ## init the NDArray from Perl array ref given in PDL::pdl constructor format <AI::MXNet::NDArray 2x2 @cpu(0)> pdl> print $arr->aspdl [ [1 2] [3 4] ] ## init the NDArray from PDL but be aware that PDL methods expect the dimensions order in column major format ## AI::MXNet::NDArray is row major pdl> print mx->nd->array(sequence(2,3))->aspdl ## 3 rows, 2 columns [ [0 1] [2 3] [4 5] ]
Export/import to/from sparse MXNet tensors are supported via PDL::CCS. Please check out the examples directory for the examples on how to use the sparse matrices.