https://bugzilla.redhat.com/show_bug.cgi?id=2258519
Bug ID: 2258519
Summary: Review Request: frugally-deep - Header-only library
for using Keras (TensorFlow) models in C++
Product: Fedora
Version: rawhide
OS: Linux
Status: NEW
Component: Package Review
Severity: medium
Assignee: [email protected]
Reporter: [email protected]
QA Contact: [email protected]
CC: [email protected]
Target Milestone: ---
Classification: Fedora
Spec URL: https://trix.fedorapeople.org/frugally-deep.spec
SRPM URL: https://trix.fedorapeople.org/frugally-deep-0.15.30-1.fc40.src.rpm
Would you like to build/train a model using Keras/Python? And would
you like to run the prediction (forward pass) on your model in C++
without linking your application against TensorFlow? Then
frugally-deep is exactly for you.
frugally-deep
* is a small header-only library written in modern and pure C++.
* is very easy to integrate and use.
* depends only on FunctionalPlus, Eigen and json - also header-only
libraries.
* supports inference (model.predict) not only for sequential models
but also for computational graphs with a more complex topology,
created with the functional API.
* re-implements a (small) subset of TensorFlow, i.e., the operations
needed to support prediction.
* results in a much smaller binary size than linking against TensorFlow.
* works out-of-the-box also when compiled into a 32-bit executable.
(Of course, 64 bit is fine too.)
* avoids temporarily allocating (potentially large chunks of)
additional RAM during convolutions (by not materializing the im2col
input matrix).
* utterly ignores even the most powerful GPU in your system and uses
only one CPU core per prediction. ;-)
* but is quite fast on one CPU core, and you can run multiple
predictions in parallel, thus utilizing as many CPUs as you like
to improve the overall prediction throughput of your
application/pipeline.
frugally-deep is a buildrequires for MIOpen, a ROCm AI/ML library needed for
PyTorch
Reproducible: Always
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