[Bug 2258519] New: Review Request: frugally-deep - Header-only library for using Keras (TensorFlow) models in C++

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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: nobody@xxxxxxxxxxxxxxxxx
          Reporter: trix@xxxxxxxxxx
        QA Contact: extras-qa@xxxxxxxxxxxxxxxxx
                CC: package-review@xxxxxxxxxxxxxxxxxxxxxxx
  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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