Builder ffmpeg-solaris10-sparc Build #13240

Results:

Failed shell_2 shell_3 shell_4 shell_5

SourceStamp:

BuildSlave:

Reason:

The SingleBranchScheduler scheduler named 'schedule-ffmpeg-solaris10-sparc' triggered this build

Steps and Logfiles:

1. git update ( 8 secs )
   1. stdio
2. shell 'gsed -i ...' ( 0 secs )
   1. stdio
3. shell_1 'gsed -i ...' ( 0 secs )
   1. stdio
4. shell_2 'gsed -i ...' failed ( 0 secs )
   1. stdio
5. shell_3 './configure --samples="../../../ffmpeg/fate-suite" ...' failed ( 7 secs )
   1. stdio
   2. config.log
6. shell_4 'gmake fate-rsync' failed ( 0 secs )
   1. stdio
7. shell_5 '../../../ffmpeg/fate.sh ../../../ffmpeg/fate_config.sh' failed ( 2 secs )
   1. stdio
   2. configure.log
   3. compile.log
   4. test.log

Build Properties:

Forced Build Properties:

Responsible Users:

1. Raja-89

   imraja729ohnoyoudont@gmail.com

Timing:

All Changes:

:

1. Change #259538

   Category	ffmpeg
   Changed by	Raja-89 <imraja729ohnoyoudont@gmail.com>
   Changed at	Mon 02 Mar 2026 14:35:17
   Repository	https://git.ffmpeg.org/ffmpeg.git
   Project	ffmpeg
   Branch	master
   Revision	27cdf70086f1205c7fa7a93ceaf4146ef71355fc

   Comments

   avfilter/dnn: implement asynchronous execution for LibTorch backend
   This patch implements the DNNAsyncExecModule for the LibTorch backend,
   enabling non-blocking inference using the common infrastructure instead
   of custom threading (th_async_module_submit) to align with the
   TensorFlow and OpenVINO backends.
   
   The implementation uses ff_dnn_start_inference_async which provides
   unified async logic across all DNN backends, eliminating the need for
   backend-specific threading code.
   
   Verified with:
     ffmpeg -f lavfi -i testsrc=duration=5:size=320x240:rate=30 -vf dnn_processing=dnn_backend=torch:model=model.pt -y output.mp4
   
   Signed-off-by: Raja Rathour <imraja729@gmail.com>

   Changed files

   • libavfilter/dnn/dnn_backend_torch.cpp