Backend libraries¶
Fully supported¶
Every component implements this backend
NumPy¶
numpy
provides a powerful yet flexible implementation of n-dimension arrays,
and is used as a base by most of the other libraries. It is required by
dockerasmus.pdb
, since Protein
instances have properties and methods
that directly return numpy.ndarray
objects.
NumPy can only perform computations on the CPU.
Theano¶
Theano
is a library which can defined, evaluate, compile and optimise
n-dimension array computations. It works seamlessly with NumPy array, but
is a lot more efficient than interpreted computations while adding only
a light overhead. Theano is very useful to compute the same mathematical
expression a lot of times, which is what happend with a scoring function.
Theano can perform computations on the GPU using a configuration file. Read the official documentation to find out how.
Partially supported¶
Only some components implement these backend
MXNet¶
mxnet
is a deep-learning library developed that supports both symbolic and
imperative programming in order to perform efficient computations. It is
maintained by the developers previously behind minerva
, cxxnet
and
purine2
.
Tensorflow¶
Considered¶
Could possibly be implemented as backends
- PyTorch
- PyCUDA
Hint
Know a library not listed here that works well for your other projects ? Submit a feature request to the issue tracker or even better, fork the project, try implementing a backend, add a test, and submit a merge request !