Speed up TensorFlow inference by compiling it from source
The most simple way to install TensorFlow is to work in a virtual Python environment and simply to use either the TensorFlow official packages in pip or use one of the official wheels for distributions. There is one big problem with that technique and it's the fact that the binaries are precompiled so that they fit as many hardware configuration as possible. This is normal from Google since generating precompiled binaries for all the possible combinations of processor capabilities would be a nightmare. This is not a problem for GPU since the CUDA Libraries will take care of the difference from one graphics card to another. But it is a problem with CPU performance. Indeed, different processors have different capabilities. For instance, the vectorization capabilities are different from processor to processor (SSE, AVX, AVX2, AVX-512F, FMA, ...). All those options can make a significant difference in the performance of the programs. Although most of the machine learning training occurs on GPU most of the time, the inference is mostly done on the CPU. Therefore, it probably remains important to be as fast as possible on CPU.
So if you care about performance on CPU, you should install TensorFlow from sources directly yourself. This will allow compilation of the TensorFlow sources with -march=native which will enable all the hardware capabilities of machine on which you are compiling the library.
Depending on your problem, this may give you some nice speedup. In my case, on a very small Recurrent Neural Network, it made inference about 20% faster. On a larger problem and depending on your processor, you may gain much more than that. If you are training on CPU, this may make a very large difference in total time.
Installing TensorFlow is sometimes a bit cumbersome. You'll likely have to compile Bazel from sources as well and depending on your processor, it may take a long time to finish. Nevertheless, I have successfully compiled TensorFlow from sources on several machines now without too many problems. Just pay close attention to the options you are setting while configuring TensorFlow, for instance CUDA configuration if you want GPU support.
I hope this little trick will help you gain some time :)
Here is the link to compile TensorFlow from source.
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