Builder ffmpeg-solaris10-sparc Build #13955

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 ( 4 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 ( 0 secs )
   1. stdio
   2. configure.log
   3. compile.log
   4. test.log

Build Properties:

Forced Build Properties:

Responsible Users:

1. stevxiao

   steven.xiaoohnoyoudont@amd.com

Timing:

All Changes:

:

1. Change #272105

   Category	ffmpeg
   Changed by	stevxiao <steven.xiaoohnoyoudont@amd.com>
   Changed at	Mon 22 Jun 2026 14:06:40
   Repository	https://git.ffmpeg.org/ffmpeg.git
   Project	ffmpeg
   Branch	master
   Revision	a2856b3c3084ea45ca5b94a5daded25f85c41dac

   Comments

   avfilter/dnn: add ONNX Runtime backend with GPU execution provider support
   This patch adds ONNX Runtime as a new DNN backend for FFmpeg's dnn_processing
   filter, enabling hardware-accelerated neural network inference on multiple
   GPU and NPU platforms.
   
   Execution Providers Supported:
   - CPU execution provider (default)
   - CUDA execution provider (NVIDIA GPUs)
   - DirectML execution provider (AMD/Intel/NVIDIA GPUs on Windows)
   - VitisAI execution provider (AMD Ryzen AI NPU)
   
   The options for dnn_processing with dnn_backend=onnx:
   - device: execution provider — cpu, cuda, dml, or vitisai (default: cpu)
   - device_id: GPU device index (default: 0)
   - threads_per_operation: inference thread count for CPU EP (default: 0, auto)
   - input: input tensor name. When omitted the backend resolves it from loaded session
   - output: output tensor name. When omitted the backend resolves it from loaded session
   
   Example usage:
     # CPU inference
     ffmpeg -i input.mp4 -vf "format=rgb24,dnn_processing=dnn_backend=onnx:model=model.onnx:input=image_in:output=image_out" output.mp4
   
     # CUDA GPU inference
     ffmpeg -i input.mp4 -vf "dnn_processing=dnn_backend=onnx:model=model.onnx:device=cuda:device_id=0" output.mp4
   
     # DirectML GPU inference (Windows)
     ffmpeg -i input.mp4 -vf "dnn_processing=dnn_backend=onnx:model=model.onnx:device=dml:device_id=0" output.mp4
   
     # VitisAI NPU inference
     ffmpeg -i input.mp4 -vf "dnn_processing=dnn_backend=onnx:model=model.onnx:device=vitisai" output.mp4
   
     Note: depending on the model, you may need a format filter (e.g. format=rgb24 or format=grayf32) before dnn_processing to convert the frames to the pixel format the model's input tensor expects.
   
   Signed-off-by: younengxiao <steven.xiao@amd.com>
   Reviewed-by: Guo Yejun <yejun.guo@intel.com>

   Changed files

   • Changelog
   • configure
   • doc/filters.texi
   • doc/general_contents.texi
   • libavfilter/dnn/Makefile
   • libavfilter/dnn/dnn_backend_onnx.c
   • libavfilter/dnn/dnn_interface.c
   • libavfilter/dnn_filter_common.c
   • libavfilter/dnn_interface.h
   • libavfilter/vf_dnn_processing.c