I was recently working with ffmpeg and NVIDIA T4 GPUs on GKE for a encoding pipeline. To get started with GPUs on GKE, the NVIDIA drivers need to be installed on the nodes. After installing, ffmpeg should be able to access NVIDIA GPU capabilities like
yadif_cuda, etc. One of the filters we needed was
scale which there is a GPU accelerated version called
scale_npp produced corrupt video when used.
This turns out that the drivers install with the daemonset provided by GKE is version 410.79 and has some problems with NVIDIA T4 GPUs. …
A recent task had me taking a look at alternative JSON libraries for the purpose of performance. One of them was python-rapidjson which offered support for SIMD.
To get python-rapidjson to compile with SIMD, we need to define one of the SIMD macros, either
The chosen flag would then need to be passed to pip during install via
CFLAGS. Depending on the flag, you would have to pass some addition options.
One-liner to re-install the currently installed version as the SIMD version.
CFLAGS="-DRAPIDJSON_SSE42=1 -msse4.2" pip -v install -force-reinstall -no-binary python-rapidjson $(pip freeze | grep python-rapidjson)