Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) GPU • DeepStream Version ds6.3-docker • JetPack Version (valid for Jetson only) • TensorRT Version 8.5 • NVIDIA GPU Driver Version (valid for GPU only) 550.142 • Issue Type( questions, new requirements, bugs)
I use streammux to play multiple streams at the same time. When I detect that a stream has not updated for a long time, I turn it off manually, but when I execute state_return = source_bin.set_state(Gst.State.PLAYING), Sometimes execution gets stuck.
This is my function to close a sourcebin:
def remove_source(self, stream_id):
source_bin = self.source_bins.get(stream_id)
if not source_bin
return False
state_return = source_bin.set_state(Gst.State.NULL)
if state_return == Gst.StateChangeReturn.ASYNC:
timeout_ns = 3 * Gst.SECOND
state_result = source_bin.get_state(timeout_ns)
MyLogger.debug(f" {state_result[0]}")
if state_result[0] == Gst.StateChangeReturn.SUCCESS:
MyLogger.info("STATE CHANGE SUCCESS")
elif state_result[0] == Gst.StateChangeReturn.TIMEOUT:
MyLogger.warning("STATE CHANGE TIMEOUT")
else:
MyLogger.debug("STATE CHANGE Failed")
elif state_return == Gst.StateChangeReturn.SUCCESS:
MyLogger.info("STATE CHANGE SUCCESS")
else:
MyLogger.debug("STATE CHANGE Failed")
pad_name = f"sink_{stream_id}"
sinkpad = self.streammux.get_static_pad(pad_name)
if sinkpad is None:
MyLogger.error("Failed to get sink pad")
else:
sinkpad.send_event(Gst.Event.new_flush_stop(False))
self.streammux.release_request_pad(sinkpad)
self.pipeline.remove(source_bin)
MyLogger.debug(f"remove_source:{self.stream_paths}")
del self.stream_paths[stream_id]
self.number_sources -= 1
self.streammux.set_property("batch-size", self.number_sources)
self.pgie.set_property("batch-size", self.number_sources)
return True
Please upgrade the DeepStream SDK to the latest version (DeepStream 7.1 GA).
The RTSP reconnection handling has been implemented with nvmultiurisrcbin. Please refer to /opt/nvidia/deepstream/deepstream/service-maker/sources/apps/python/pipeline_api/deepstream_test5_app
The program gives an error: > pybind11::handle::inc_ref() is being called while the GIL is either not held or invalid. Please see Miscellaneous - pybind11 documentation for debugging advice.
If you are convinced there is no bug in your code, you can define PYBIND11_NO_ASSERT_GIL_HELD_INCREF_DECREF to disable this check. In that case you have to ensure this define is consistently used for all translation units linked into a given pybind11 extension, otherwise there will be ODR violations. The failing pybind11::handle::inc_ref() call was triggered on a NoneType object.
Traceback (most recent call last):
File “/ opt/nvidia/deepstream deepstream - 7.1 / service - maker/sources/apps/python/pipeline_api/deepstream_test5_app/deepstream_te st5.py”, line 266, in
main(args)
File “/ opt/nvidia/deepstream deepstream - 7.1 / service - maker/sources/apps/python/pipeline_api/deepstream_test5_app/deepstream_te st5.py”, line 100, in main
pipeline = Pipeline(name=pipeline_name, config_file=pipeline_config_file)
The File “/ usr/local/lib/python3.10 / dist - packages/pyservicemaker/pipeline. Py”, line 27, in init
self._instance = _Pipeline(name, config_file) if config_file else _Pipeline(name)
RuntimeError: pybind11::handle::inc_ref() PyGILState_Check() failure.
pybind11::handle::dec_ref() is being called while the GIL is either not held or invalid. Please see Miscellaneous - pybind11 documentation for debugging advice.
If you are convinced there is no bug in your code, you can define PYBIND11_NO_ASSERT_GIL_HELD_INCREF_DECREF to disable this check. In that case you have to ensure this define is consistently used for all translation units linked into a given pybind11 extension, otherwise there will be ODR violations. The failing pybind11::handle::dec_ref() call was triggered on a str object.
terminate called after throwing an instance of ‘std::runtime_error’
what(): pybind11::handle::dec_ref() PyGILState_Check() failure.
Aborted (core dumped)
Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) • DeepStream Version • JetPack Version (valid for Jetson only) • TensorRT Version • NVIDIA GPU Driver Version (valid for GPU only)
• Hardware Platform (Jetson / GPU) GPU • DeepStream Version* ds7.1-docker • JetPack Version (valid for Jetson only) • TensorRT Version 10.3 • NVIDIA GPU Driver Version (valid for GPU only) 550.142
My Sever is ubuntu20.04，in docker is 22.04
FROM nvcr.io/nvidia/deepstream:7.1-<container type>
COPY myapp /root/apps/myapp
# To get video driver libraries at runtime (libnvidia-encode.so/libnvcuvid.so)
ENV NVIDIA_DRIVER_CAPABILITIES $NVIDIA_DRIVER_CAPABILITIES,video
Does this have to be done, should I run it in a terminal inside a container or something
Before using the 560 driver, I want to ask why I can’t run the demo successfully if I follow this note, install cuda12.4-toolkot and set it as default cuda
I ran into other problems when using Nvmultiurisrcbin plugin myself. Can you point out the possible problems? This is a link toTopic,Is it also a driver issue?
