Which of the following would be the best answer for the question given in the audio? 0:00 / 0:03

I’m doing some tests for CPU and GPU environment usages for prediction (Predict.py). I’m using an audio file Audio: mp3, 44100 Hz, stereo, fltp, 192 kb/s of duration 00:03:15.29

$ ffprobe /audio/12380187.mp3 ffprobe version 4.0 Copyright (c) 2007-2018 the FFmpeg developers built with Apple LLVM version 9.1.0 (clang-902.0.39.1) configuration: --prefix=/usr/local/Cellar/ffmpeg/4.0 --enable-shared --enable-pthreads --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-gpl --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --enable-videotoolbox --disable-lzma libavutil 56. 14.100 / 56. 14.100 libavcodec 58. 18.100 / 58. 18.100 libavformat 58. 12.100 / 58. 12.100 libavdevice 58. 3.100 / 58. 3.100 libavfilter 7. 16.100 / 7. 16.100 libavresample 4. 0. 0 / 4. 0. 0 libswscale 5. 1.100 / 5. 1.100 libswresample 3. 1.100 / 3. 1.100 libpostproc 55. 1.100 / 55. 1.100 Input #0, mp3, from '/audio/12380187.mp3': Metadata: encoder : Lavf56.40.101 Duration: 00:03:15.29, start: 0.025057, bitrate: 192 kb/s Stream #0:0: Audio: mp3, 44100 Hz, stereo, fltp, 192 kb/s Metadata: encoder : Lavc56.60

On a Intel i7 - 12 core CPU the prediction time log says Completed after 0:03:19

$ time python Predict.py with cfg.full_44KHz input_path=/audio/12380187.mp3 output_path=/audio_sep/ Training full singing voice separation model, with difference output and input context (valid convolutions) and stereo input/output, and learned upsampling layer, and 44.1 KHz sampling rate WARNING - Waveunet Prediction - No observers have been added to this run INFO - Waveunet Prediction - Running command 'main' INFO - Waveunet Prediction - Started Producing source estimates for input mixture file /audio/12380187.mp3 Testing... 2018-11-20 14:54:05.306099: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA Num of variables64 INFO:tensorflow:Restoring parameters from checkpoints/full_44KHz/full_44KHz-236118 INFO - tensorflow - Restoring parameters from checkpoints/full_44KHz/full_44KHz-236118 Pre-trained model restored for song prediction INFO - Waveunet Prediction - Completed after 0:03:19 real 3m26.034s user 13m30.420s sys 4m40.200s

while on Intel Xeon 12 core plus 2x Nvidia GeForce GTX 1080 says Completed after 0:00:16

$ time python Predict.py with cfg.full_44KHz input_path=/audio/12380187.mp3 /usr/local/lib/python2.7/dist-packages/scikits/audiolab/soundio/play.py:48: UserWarning: Could not import alsa backend; most probably, you did not have alsa headers when building audiolab warnings.warn("Could not import alsa backend; most probably, " Training full singing voice separation model, with difference output and input context (valid convolutions) and stereo input/output, and learned upsampling layer, and 44.1 KHz sampling rate WARNING - Waveunet Prediction - No observers have been added to this run INFO - Waveunet Prediction - Running command 'main' INFO - Waveunet Prediction - Started Producing source estimates for input mixture file /audio/12380187.mp3 Testing... 2018-11-21 12:34:13.829481: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2018-11-21 12:34:13.830157: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties: name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.8475 pciBusID: 0000:01:00.0 totalMemory: 7.92GiB freeMemory: 7.46GiB 2018-11-21 12:34:13.961794: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2018-11-21 12:34:13.962562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 1 with properties: name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.8475 pciBusID: 0000:02:00.0 totalMemory: 7.93GiB freeMemory: 7.81GiB 2018-11-21 12:34:13.963292: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0, 1 2018-11-21 12:34:14.531254: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix: 2018-11-21 12:34:14.531305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929] 0 1 2018-11-21 12:34:14.531329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0: N Y 2018-11-21 12:34:14.531336: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 1: Y N 2018-11-21 12:34:14.531830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7209 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1) 2018-11-21 12:34:14.589915: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 7543 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080, pci bus id: 0000:02:00.0, compute capability: 6.1) Num of variables64 INFO:tensorflow:Restoring parameters from checkpoints/full_44KHz/full_44KHz-236118 INFO - tensorflow - Restoring parameters from checkpoints/full_44KHz/full_44KHz-236118 Pre-trained model restored for song prediction INFO - Waveunet Prediction - Completed after 0:00:16 real 0m18.340s user 0m15.972s sys 0m5.528s

I’m not sure from logging if tensorflow is using both gpu devices or gpu 0 only. If I’m not wrong, most of the work is done in the Models.py here https://github.com/f90/Wave-U-Net/blob/master/Models/UnetSpectrogramSeparator.py#L39 when the computation graph is calculated. I assume that these operations go on gpu:0 in this configuration, so gpu:1 will not be used - but I’m not sure of it.

Thank you very much!