Optimizer#

DLS Optimizer is a tool for helping users discover more optimal versions of the pipelines they run on DL Streamer. It will explore different modifications to your pipeline that are known to increase performance and measure them. As a result, you can expect to receive a pipeline that is better suited to your setup.

Limitations#

Currently the DLS Optimizer focuses mainly on DL Streamer elements, specifically the gvadetect and gvaclassify. The produced pipeline could still have potential for further optimization by transforming other elements.

Multi-stream pipelines (those utilizing the tee element) are also currently not supported.

Using the optimizer as a tool#

Note: This example assumes your working directory is the optimizer directory /opt/intel/dlstreamer/scripts/optimizer

python3 . [OPT] -- PIPELINE

Options:
    --search-duration SEARCH_DURATION   How long should the optimizer search for better pipelines
    --sample-duration SAMPLE_DURATION   How long should every pipeline be sampled for performance
    --log-level LEVEL                   Configure the logging detail level

  • Increasing the search duration will increase the chances of discovering more performant pipelines. -> Default: 300 seconds

  • Increasing the sample duration will improve the stability of the search. -> Default: 10 seconds

  • Available log levels are: CRITICAL, FATAL, ERROR, WARN, INFO, DEBUG. -> Default: INFO

Note: Search duration and sample duration both affect the amount of pipelines that will be explored during the search.

Example#

 python3 . -- urisourcebin buffer-size=4096 uri=https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4 ! decodebin ! gvadetect model=/home/optimizer/models/public/yolo11s/INT8/yolo11s.xml ! queue ! gvawatermark ! vah264enc ! h264parse ! mp4mux ! fakesink
[__main__] [    INFO] - GStreamer initialized successfully
[__main__] [    INFO] - GStreamer version: 1.26.6
[__main__] [    INFO] - Detected GPU Device
[__main__] [    INFO] - No NPU Device detected
[__main__] [    INFO] - Sampling for 10 seconds...
FpsCounter(last 1.00sec): total=46.87 fps, number-streams=1, per-stream=46.87 fps
FpsCounter(average 1.00sec): total=46.87 fps, number-streams=1, per-stream=46.87 fps
FpsCounter(last 1.01sec): total=43.70 fps, number-streams=1, per-stream=43.70 fps
FpsCounter(average 2.01sec): total=45.28 fps, number-streams=1, per-stream=45.28 fps

...

FpsCounter(last 1.09sec): total=73.45 fps, number-streams=1, per-stream=73.45 fps
FpsCounter(average 8.70sec): total=73.65 fps, number-streams=1, per-stream=73.65 fps
[__main__] [    INFO] - Best found pipeline: urisourcebin buffer-size=4096 uri=https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4 !decodebin3!gvadetect model=/home/optimizer/models/public/yolo11s/INT8/yolo11s.xml device=GPU pre-process-backend=va-surface-sharing batch-size=2 nireq=2 ! queue ! gvawatermark ! vah264enc ! h264parse ! mp4mux ! fakesink with fps: 81.987923.2

In this case the optimizer started with a pipeline that ran at ~45fps, and found a pipeline that ran at ~82fps instead. The specific improvements were:

  • replacing the decodebin with the decodebin3 element.

  • configuring the gvadetect element to use GPU for processing

  • setting the batch-size parameter to 2

  • setting the nireq parameter to 2

Using the optimizer as a library#

The easiest way of importing the optimizer into your scripts is to include it in your PYTHONPATH environment variable: export PYTHONPATH=/opt/intel/dlstreamer/scripts/optimizer

Targets which are exported in order to facilitate usage inside of scripts:

preprocess_pipeline(pipeline) -> processed_pipeline#

pipeline - A string containing a valid DL Streamer pipeline. processed_pipeline - A string containing the pipeline with all relevant substitutions.

Perform quick search and replace for known combinations of elements with more performant alternatives.

get_optimized_pipeline(pipeline, search_duration, sample_duration) -> (optimized_pipeline, fps)#

pipeline - A string containing a valid DL Streamer pipeline. search_duration - The duration of searching for optimized pipelines in seconds, default 300. sample_duration - The duration of sampling each candidate pipeline in seconds, default 10. optimized_pipeline - A string containing the best performing pipeline that has been found during the search. fps - The measured fps of the best perfmorming pipeline.

Runs a series of optimization steps on the pipeline searching for a better performing versions.

Example#

from optimizer import get_optimized_pipeline

pipeline = "urisourcebin buffer-size=4096 uri=https://videos.pexels.com/video-files/1192116/1192116-sd_640_360_30fps.mp4 ! decodebin ! gvadetect model=/home/optimizer/models/public/yolo11s/INT8/yolo11s.xml ! queue ! gvawatermark ! vah264enc ! h264parse ! mp4mux ! fakesink"

optimized_pipeline, fps = get_optimized_pipeline(pipeline)
print("Best discovered pipeline: " + optimized_pipeline)
print("Measured fps: " + fps)

Controling the measurement#

The point at which performance is being measured can be controlled by pre-emptively inserting a gvafpscounter element into your pipeline definition. For pipelines which lack such an element, the measurement is done after the last inference element supported by the optimizer tool.