OpenGL ES Support
MediaPipe supports OpenGL ES up to version 3.2 on Android/Linux and up to ES 3.0 on iOS. In addition, MediaPipe also supports Metal on iOS.
OpenGL ES 3.1 or greater is required (on Android/Linux systems) for running machine learning inference calculators and graphs.
Disable OpenGL ES Support
By default, building MediaPipe (with no special bazel flags) attempts to compile and link against OpenGL ES (and for iOS also Metal) libraries.
On platforms where OpenGL ES is not available (see also OpenGL ES Setup on Linux Desktop), you should disable OpenGL ES support with:
$ bazel build --define MEDIAPIPE_DISABLE_GPU=1 <my-target>
OpenGL ES Setup on Linux Desktop
On Linux desktop with video cards that support OpenGL ES 3.1+, MediaPipe can run GPU compute and rendering and perform TFLite inference on GPU.
To check if your Linux desktop GPU can run MediaPipe with OpenGL ES:
$ sudo apt-get install mesa-common-dev libegl1-mesa-dev libgles2-mesa-dev
$ sudo apt-get install mesa-utils
$ glxinfo | grep -i opengl
For example, it may print:
$ glxinfo | grep -i opengl
...
OpenGL ES profile version string: OpenGL ES 3.2 NVIDIA 430.50
OpenGL ES profile shading language version string: OpenGL ES GLSL ES 3.20
OpenGL ES profile extensions:
If you have connected to your computer through SSH and find when you probe for GPU information you see the output:
glxinfo | grep -i opengl
Error: unable to open display
Try re-establishing your SSH connection with the -X option and try again. For
example:
ssh -X <user>@<host>
Notice the ES 3.20 text above.
You need to see ES 3.1 or greater printed in order to perform TFLite inference on GPU in MediaPipe. With this setup, build with:
$ bazel build --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 <my-target>
If only ES 3.0 or below is supported, you can still build MediaPipe targets that don't require TFLite inference on GPU with:
$ bazel build --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 --copt -DMEDIAPIPE_DISABLE_GL_COMPUTE <my-target>
TensorFlow CUDA Support and Setup on Linux Desktop
MediaPipe framework doesn't require CUDA for GPU compute and rendering. However, MediaPipe can work with TensorFlow to perform GPU inference on video cards that support CUDA.
To enable TensorFlow GPU inference with MediaPipe, the first step is to follow the TensorFlow GPU documentation to install the required NVIDIA software on your Linux desktop.
After installation, update $PATH and $LD_LIBRARY_PATH and run ldconfig
with:
$ export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}}
$ export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64,/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
$ sudo ldconfig
It's recommended to verify the installation of CUPTI, CUDA, CuDNN, and NVCC:
$ ls /usr/local/cuda/extras/CUPTI
/lib64
libcupti.so libcupti.so.10.1.208 libnvperf_host.so libnvperf_target.so
libcupti.so.10.1 libcupti_static.a libnvperf_host_static.a
$ ls /usr/local/cuda-10.1
LICENSE bin extras lib64 libnvvp nvml samples src tools
README doc include libnsight nsightee_plugins nvvm share targets version.txt
$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
$ ls /usr/lib/x86_64-linux-gnu/ | grep libcudnn.so
libcudnn.so
libcudnn.so.7
libcudnn.so.7.6.4
Setting $TF_CUDA_PATHS is the way to declare where the CUDA library is. Note
that the following code snippet also adds /usr/lib/x86_64-linux-gnu and
/usr/include into $TF_CUDA_PATHS for cudablas and libcudnn.
$ export TF_CUDA_PATHS=/usr/local/cuda-10.1,/usr/lib/x86_64-linux-gnu,/usr/include
To make MediaPipe get TensorFlow's CUDA settings, find TensorFlow's
.bazelrc and
copy the build:using_cuda and build:cuda section into MediaPipe's .bazelrc
file. For example, as of April 23, 2020, TensorFlow's CUDA setting is the
following:
# This config refers to building with CUDA available. It does not necessarily
# mean that we build CUDA op kernels.
build:using_cuda --define=using_cuda=true
build:using_cuda --action_env TF_NEED_CUDA=1
build:using_cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
# This config refers to building CUDA op kernels with nvcc.
build:cuda --config=using_cuda
build:cuda --define=using_cuda_nvcc=true
Finally, build MediaPipe with TensorFlow GPU with two more flags --config=cuda
and --spawn_strategy=local. For example:
$ bazel build -c opt --config=cuda --spawn_strategy=local \
--define no_aws_support=true --copt -DMESA_EGL_NO_X11_HEADERS \
mediapipe/examples/desktop/object_detection:object_detection_tensorflow
While the binary is running, it prints out the GPU device info:
I external/org_tensorflow/tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1544] Found device 0 with properties: pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s
I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1686] Adding visible gpu devices: 0
You can monitor the GPU usage to verify whether the GPU is used for model inference.
$ nvidia-smi --query-gpu=utilization.gpu --format=csv --loop=1
0 %
0 %
4 %
5 %
83 %
21 %
22 %
27 %
29 %
100 %
0 %
0%