aterko-ConvNet_and_RNN_Benchmarks - #235
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guitargeek merged 10 commits intoSep 5, 2026
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PyMVA was deprecated in ROOT, so it makes no sense to add benchmark coverage for it now. Furthermore, MethodPyKeras terminates the whole benchmark binary with an uncaught exception on machines where TensorFlow is not available, which would also take down the results of the native TMVA benchmarks in the same executable. This reverts the ConvNet benchmark files to their state on master, only keeping the missing-newline-at-end-of-file fixes.
Like for the ConvNet benchmarks, the PyKeras variants of the RNN benchmarks are removed because PyMVA was deprecated in ROOT, and because MethodPyKeras terminates the whole benchmark binary with an uncaught exception on machines without TensorFlow, taking the native TMVA benchmark results down with it. The native TMVA LSTM benchmarks based on TMVA::Types::kDL are kept.
The generated input data and the TMVA output file are now written to the directory pointed to by the RB_TEMP_FS environment variable, like in the other benchmarks that write temporary files. Model persistence is also disabled, since nothing reads the trained weights back and this avoids littering the working directory with a dataset/weights folder.
Register the RNN benchmarks inside the existing conditional blocks for the ConvNet benchmarks instead of repeating the conditions and the DNN_CUDA_LIBRARIES setup.
Each iteration is a full LSTM training that takes about half a minute, so the "short" label is not appropriate. The existing ConvNet benchmarks have the same issue, but relabeling them is left out of this PR to not change the categorization of benchmarks already on master.
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LGTM! I have just added some commits with minor improvements and the removal of the PyMVA benchmarks, because PyMVA is deprecated now in ROOT.
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