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Enabling GPU video decoder/encoder

TorchAudio can make use of hardware-based video decoding and encoding supported by underlying FFmpeg libraries that are linked at runtime.

Using NVIDIA’s GPU decoder and encoder, it is also possible to pass around CUDA Tensor directly, that is decode video into CUDA tensor or encode video from CUDA tensor, without moving data from/to CPU.

This improves the video throughput significantly. However, please note that not all the video formats are supported by hardware acceleration.

This page goes through how to build FFmpeg with hardware acceleration. For the detail on the performance of GPU decoder and encoder please see NVDEC tutoial and NVENC tutorial.


Using them in TorchAduio requires additional FFmpeg configuration.

In the following, we look into how to enable GPU video decoding with NVIDIA’s Video codec SDK. To use NVENC/NVDEC with TorchAudio, the following items are required.

  1. NVIDIA GPU with hardware video decoder/encoder.

  2. FFmpeg libraries compiled with NVDEC/NVENC support. †

  3. PyTorch / TorchAudio with CUDA support.

TorchAudio’s official binary distributions are compiled to work with FFmpeg libraries, and they contain the logic to use hardware decoding/encoding.

In the following, we build FFmpeg 4 libraries with NVDEC/NVENC support. You can also use FFmpeg 5 or 6.

The following procedure was tested on Ubuntu.

† For details on NVDEC/NVENC and FFmpeg, please refer to the following articles.

Check the GPU and CUDA version

First, check the available GPU. Here, we have Tesla T4 with CUDA Toolkit 11.2 installed.

$ nvidia-smi

Fri Oct  7 13:01:26 2022
| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   56C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |

| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|  No running processes found                                                 |

Checking the compute capability

Later, we need the version of compute capability supported by this GPU. The following page lists the GPUs and corresponding compute capabilities. The compute capability of T4 is 7.5.


Install NVIDIA Video Codec Headers

To build FFmpeg with NVDEC/NVENC, we first need to install the headers that FFmpeg uses to interact with Video Codec SDK.

Since we have CUDA 11 working in the system, we use one of n11 tag.

git clone https://git.videolan.org/git/ffmpeg/nv-codec-headers.git
cd nv-codec-headers
git checkout n11.0.10.1
sudo make install

The location of installation can be changed with make PREFIX=<DESIRED_DIRECTORY> install.

Cloning into 'nv-codec-headers'...
remote: Enumerating objects: 819, done.
remote: Counting objects: 100% (819/819), done.
remote: Compressing objects: 100% (697/697), done.
remote: Total 819 (delta 439), reused 0 (delta 0)
Receiving objects: 100% (819/819), 156.42 KiB | 410.00 KiB/s, done.
Resolving deltas: 100% (439/439), done.
Note: checking out 'n11.0.10.1'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:

  git checkout -b <new-branch-name>

HEAD is now at 315ad74 add cuMemcpy
sed 's#@@PREFIX@@#/usr/local#' ffnvcodec.pc.in > ffnvcodec.pc
install -m 0755 -d '/usr/local/include/ffnvcodec'
install -m 0644 include/ffnvcodec/*.h '/usr/local/include/ffnvcodec'
install -m 0755 -d '/usr/local/lib/pkgconfig'
install -m 0644 ffnvcodec.pc '/usr/local/lib/pkgconfig'

Install FFmpeg dependencies

Next, we install tools and libraries required during the FFmpeg build. The minimum requirement is Yasm. Here we additionally install H264 video codec and HTTPS protocol, which we use later for verifying the installation.

sudo apt -qq update
sudo apt -qq install -y yasm libx264-dev libgnutls28-dev
... Omitted for brevity ...

STRIP   install-libavutil-shared
Setting up libx264-dev:amd64 (2:0.152.2854+gite9a5903-2) ...
Setting up yasm (1.3.0-2build1) ...
Setting up libunbound2:amd64 (1.6.7-1ubuntu2.5) ...
Setting up libp11-kit-dev:amd64 (0.23.9-2ubuntu0.1) ...
Setting up libtasn1-6-dev:amd64 (4.13-2) ...
Setting up libtasn1-doc (4.13-2) ...
Setting up libgnutlsxx28:amd64 (3.5.18-1ubuntu1.6) ...
Setting up libgnutls-dane0:amd64 (3.5.18-1ubuntu1.6) ...
Setting up libgnutls-openssl27:amd64 (3.5.18-1ubuntu1.6) ...
Setting up libgmpxx4ldbl:amd64 (2:6.1.2+dfsg-2) ...
Setting up libidn2-dev:amd64 (2.0.4-1.1ubuntu0.2) ...
Setting up libidn2-0-dev (2.0.4-1.1ubuntu0.2) ...
Setting up libgmp-dev:amd64 (2:6.1.2+dfsg-2) ...
Setting up nettle-dev:amd64 (3.4.1-0ubuntu0.18.04.1) ...
Setting up libgnutls28-dev:amd64 (3.5.18-1ubuntu1.6) ...
Processing triggers for man-db (2.8.3-2ubuntu0.1) ...
Processing triggers for libc-bin (2.27-3ubuntu1.6) ...

Build FFmpeg with NVDEC/NVENC support

Next we download the source code of FFmpeg 4. We use 4.4.2 here.

wget -q https://github.com/FFmpeg/FFmpeg/archive/refs/tags/n4.4.2.tar.gz
tar -xf n4.4.2.tar.gz
cd FFmpeg-n4.4.2

Next we configure FFmpeg build. Note the following:

  1. We provide flags like -I/usr/local/cuda/include, -L/usr/local/cuda/lib64 to let the build process know where the CUDA libraries are found.

  2. We provide flags like --enable-nvdec and --enable-nvenc to enable NVDEC/NVENC.

  3. We also provide NVCC flags with compute capability 75, which corresponds to 7.5 of T4. †

  4. We install the library in /usr/lib/.


† The configuration script verifies NVCC by compiling a sample code. By default it uses old compute capability such as 30, which is no longer supported by CUDA 11. So it is required to set a correct compute capability.


./configure \
  --prefix="${prefix}" \
  --extra-cflags='-I/usr/local/cuda/include' \
  --extra-ldflags='-L/usr/local/cuda/lib64' \
  --nvccflags="-gencode arch=compute_${ccap},code=sm_${ccap} -O2" \
  --disable-doc \
  --enable-decoder=aac \
  --enable-decoder=h264 \
  --enable-decoder=h264_cuvid \
  --enable-decoder=rawvideo \
  --enable-indev=lavfi \
  --enable-encoder=libx264 \
  --enable-encoder=h264_nvenc \
  --enable-demuxer=mov \
  --enable-muxer=mp4 \
  --enable-filter=scale \
  --enable-filter=testsrc2 \
  --enable-protocol=file \
  --enable-protocol=https \
  --enable-gnutls \
  --enable-shared \
  --enable-gpl \
  --enable-nonfree \
  --enable-cuda-nvcc \
  --enable-libx264 \
  --enable-nvenc \
  --enable-cuvid \
install prefix            /usr/
source path               .
C compiler                gcc
C library                 glibc
ARCH                      x86 (generic)
big-endian                no
runtime cpu detection     yes
standalone assembly       yes
x86 assembler             yasm
MMX enabled               yes
MMXEXT enabled            yes
3DNow! enabled            yes
3DNow! extended enabled   yes
SSE enabled               yes
SSSE3 enabled             yes
AESNI enabled             yes
AVX enabled               yes
AVX2 enabled              yes
AVX-512 enabled           yes
XOP enabled               yes
FMA3 enabled              yes
FMA4 enabled              yes
i686 features enabled     yes
CMOV is fast              yes
EBX available             yes
EBP available             yes
debug symbols             yes
strip symbols             yes
optimize for size         no
optimizations             yes
static                    no
shared                    yes
postprocessing support    no
network support           yes
threading support         pthreads
safe bitstream reader     yes
texi2html enabled         no
perl enabled              yes
pod2man enabled           yes
makeinfo enabled          no
makeinfo supports HTML    no

External libraries:
alsa                    libx264                 lzma
bzlib                   libxcb                  zlib
gnutls                  libxcb_shape
iconv                   libxcb_xfixes

External libraries providing hardware acceleration:
cuda                    cuvid                   nvenc
cuda_llvm               ffnvcodec               v4l2_m2m
cuda_nvcc               nvdec

avcodec                 avformat                swscale
avdevice                avutil
avfilter                swresample

ffmpeg                  ffprobe

Enabled decoders:
aac                     hevc                    rawvideo
av1                     mjpeg                   vc1
h263                    mpeg1video              vp8
h264                    mpeg2video              vp9
h264_cuvid              mpeg4

Enabled encoders:
h264_nvenc              libx264

Enabled hwaccels:
av1_nvdec               mpeg1_nvdec             vp8_nvdec
h264_nvdec              mpeg2_nvdec             vp9_nvdec
hevc_nvdec              mpeg4_nvdec             wmv3_nvdec
mjpeg_nvdec             vc1_nvdec

Enabled parsers:
h263                    mpeg4video              vp9

Enabled demuxers:

Enabled muxers:
mov                     mp4

Enabled protocols:
file                    tcp
https                   tls

Enabled filters:
aformat                 hflip                   transpose
anull                   null                    trim
atrim                   scale                   vflip
format                  testsrc2

Enabled bsfs:
aac_adtstoasc           null                    vp9_superframe_split
h264_mp4toannexb        vp9_superframe

Enabled indevs:

Enabled outdevs:

License: nonfree and unredistributable

Now we build and install

make clean
make -j
sudo make install
... Omitted for brevity ...

INSTALL libavdevice/libavdevice.so
INSTALL libavfilter/libavfilter.so
INSTALL libavformat/libavformat.so
INSTALL libavcodec/libavcodec.so
INSTALL libswresample/libswresample.so
INSTALL libswscale/libswscale.so
INSTALL libavutil/libavutil.so
INSTALL install-progs-yes
INSTALL ffmpeg
INSTALL ffprobe

Checking the intallation

To verify that the FFmpeg we built have CUDA support, we can check the list of available decoders and encoders.

ffprobe -hide_banner -decoders | grep h264
VFS..D h264                 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10
V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
ffmpeg -hide_banner -encoders | grep 264
V..... libx264              libx264 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10 (codec h264)
V....D h264_nvenc           NVIDIA NVENC H.264 encoder (codec h264)

The following command fetches video from remote server, decode with NVDEC (cuvid) and re-encode with NVENC. If this command does not work, then there is an issue with FFmpeg installation, and TorchAudio would not be able to use them either.

$ src="https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4"

$ ffmpeg -hide_banner -y -vsync 0 \
     -hwaccel cuvid \
     -hwaccel_output_format cuda \
     -c:v h264_cuvid \
     -resize 360x240 \
     -i "${src}" \
     -c:a copy \
     -c:v h264_nvenc \
     -b:v 5M test.mp4

Note that there is Stream #0:0 -> #0:0 (h264 (h264_cuvid) -> h264 (h264_nvenc)), which means that video is decoded with h264_cuvid decoder and h264_nvenc encoder.

Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4':
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp42iso2avc1mp41
    encoder         : Lavf58.76.100
  Duration: 00:03:26.04, start: 0.000000, bitrate: 1294 kb/s
  Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 960x540 [SAR 1:1 DAR 16:9], 1156 kb/s, 29.97 fps, 29.97 tbr, 30k tbn, 59.94 tbc (default)
      handler_name    : ?Mainconcept Video Media Handler
      vendor_id       : [0][0][0][0]
  Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s (default)
      handler_name    : #Mainconcept MP4 Sound Media Handler
      vendor_id       : [0][0][0][0]
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (h264_cuvid) -> h264 (h264_nvenc))
  Stream #0:1 -> #0:1 (copy)
Press [q] to stop, [?] for help
Output #0, mp4, to 'test.mp4':
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp42iso2avc1mp41
    encoder         : Lavf58.76.100
  Stream #0:0(eng): Video: h264 (Main) (avc1 / 0x31637661), cuda(tv, bt709, progressive), 360x240 [SAR 1:1 DAR 3:2], q=2-31, 5000 kb/s, 29.97 fps, 30k tbn (default)
      handler_name    : ?Mainconcept Video Media Handler
      vendor_id       : [0][0][0][0]
      encoder         : Lavc58.134.100 h264_nvenc
    Side data:
      cpb: bitrate max/min/avg: 0/0/5000000 buffer size: 10000000 vbv_delay: N/A
  Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s (default)
      handler_name    : #Mainconcept MP4 Sound Media Handler
      vendor_id       : [0][0][0][0]
frame= 6175 fps=1712 q=11.0 Lsize=   37935kB time=00:03:26.01 bitrate=1508.5kbits/s speed=57.1x
video:34502kB audio:3234kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.526932%

Using the GPU decoder/encoder from TorchAudio

Checking the installation

Once the FFmpeg is properly working with hardware acceleration, we need to check if TorchAudio can pick it up correctly.

There are utility functions to query the capability of FFmpeg in torchaudio.utils.ffmpeg_utils.

You can first use get_video_decoders() and get_video_encoders() to check if GPU decoders and encoders (such as h264_cuvid and h264_nvenc) are listed.

It is often the case where there are multiple FFmpeg installations in the system, and TorchAudio is loading one different than expected. In such cases, use of ffmpeg to check the installation does not help. You can use functions like get_build_config() and get_versions() to get information about FFmpeg libraries TorchAudio loaded.

from torchaudio.utils import ffmpeg_utils

print("Library versions:")
print("\nBuild config:")
print([k for k in ffmpeg_utils.get_video_decoders().keys() if "cuvid" in k])
print([k for k in ffmpeg_utils.get_video_encoders().keys() if "nvenc" in k])
Library versions:
{'libavutil': (56, 31, 100), 'libavcodec': (58, 54, 100), 'libavformat': (58, 29, 100), 'libavfilter': (7, 57, 100), 'libavdevice': (58, 8, 100)}

Build config:
--prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared

['h264_cuvid', 'hevc_cuvid', 'mjpeg_cuvid', 'mpeg1_cuvid', 'mpeg2_cuvid', 'mpeg4_cuvid', 'vc1_cuvid', 'vp8_cuvid', 'vp9_cuvid']

['h264_nvenc', 'nvenc', 'nvenc_h264', 'nvenc_hevc', 'hevc_nvenc']

Using the hardware decoder and encoder

Once the installation and the runtime linking work fine, then you can test the GPU decoding with the following.

For the detail on the performance of GPU decoder and encoder please see NVDEC tutoial and NVENC tutorial.


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