I downgraded the driver version to R535 and modified the code to facilitate debugging, but the issue persists. The process freezes when declaring Gst.ElementFactory.make("nvstreammux", "Stream-muxer") .
The same code/procedure (pyds 1.1.8) on deepstream 6.3 on this host works fine.
I started new container :
docker run --gpus all \
-it \
--name deepstream_test_ds6.4 \
--net host \
nvcr.io/nvidia/deepstream:6.4-triton-multiarch
Command executed inside container
===============================
DeepStreamSDK 6.4.0
===============================
*** LICENSE AGREEMENT ***
By using this software you agree to fully comply with the terms and conditions
of the License Agreement. The License Agreement is located at
/opt/nvidia/deepstream/deepstream/LicenseAgreement.pdf. If you do not agree
to the terms and conditions of the License Agreement do not use the software.
=============================
== Triton Inference Server ==
=============================
NVIDIA Release 23.08 (build 66820947)
Triton Server Version 2.37.0
Copyright (c) 2018-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
root@omnidev:/opt/nvidia/deepstream/deepstream-6.4#
Nvidia-smi
Fri Jan 5 13:36:24 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.146.02 Driver Version: 535.146.02 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| 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 NVIDIA GeForce RTX 4090 Off | 00000000:09:00.0 Off | Off |
| 36% 28C P8 35W / 450W | 1365MiB / 24564MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
+---------------------------------------------------------------------------------------+
Instalation of python pyds
root@omnidev:/opt/nvidia/deepstream/deepstream-6.4# ./user_deepstream_python_apps_install.sh --build-bindings
####################################
Downloading necessary pre-requisites
####################################
Get:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease [1581 B]
Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
Get:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [119 kB]
Get:4 http://security.ubuntu.com/ubuntu jammy-security InRelease [110 kB]
Get:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [643 kB]
.
.
.
Preparing metadata (setup.py) ... done
Requirement already satisfied: PyGObject in /usr/lib/python3/dist-packages (from pyds==1.1.10) (3.42.1)
Collecting pycairo>=1.16.0 (from PyGObject->pyds==1.1.10)
Downloading pycairo-1.25.1.tar.gz (347 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 347.1/347.1 kB 1.1 MB/s eta 0:00:00
Installing build dependencies ... done
Getting requirements to build wheel ... done
Installing backend dependencies ... done
Preparing metadata (pyproject.toml) ... done
Building wheels for collected packages: pgi, pycairo
Building wheel for pgi (setup.py) ... done
Created wheel for pgi: filename=pgi-0.0.11.2-py3-none-any.whl size=181777 sha256=b1b64617d9573874898e6f771df59451235007a3e0bf84cd8e21786b14ea1c2e
Stored in directory: /root/.cache/pip/wheels/fc/c3/1b/a1f2776e8cf1a8a190322b87dfd9d4153fd3d78c899d58515d
Building wheel for pycairo (pyproject.toml) ... done
Created wheel for pycairo: filename=pycairo-1.25.1-cp310-cp310-linux_x86_64.whl size=320972 sha256=531678177ca3e150ecb21c7266449c4a069c58dbef0352a0b03ca844b39048cf
Stored in directory: /root/.cache/pip/wheels/d6/d8/c4/9bb1adbc349a349ed4718627f0afffeae26d9982060568cd30
Successfully built pgi pycairo
Installing collected packages: pgi, pycairo, pyds
Successfully installed pgi-0.0.11.2 pycairo-1.25.1 pyds-1.1.10
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
[notice] A new release of pip is available: 23.3.1 -> 23.3.2
[notice] To update, run: python3 -m pip install --upgrade pip
the code:
test.py
################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2020-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
import pyds
# Standard GStreamer initialization
Gst.init(None)
# Create gstreamer elements */
# Create Pipeline element that will form a connection of other elements
print("Creating Pipeline \n ")
pipeline = Gst.Pipeline()
if not pipeline:
sys.stderr.write(" Unable to create Pipeline \n")
print("Creating streamux \n ")
# Create nvstreammux instance to form batches from one or more sources.
streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
if not streammux:
sys.stderr.write(" Unable to create NvStreamMux \n")
pipeline.add(streammux)
output:
python3 test.py
(python3:2651): GStreamer-WARNING **: 13:50:42.846: External plugin loader failed. This most likely means that the plugin loader helper binary was not found or could not be run. You might need to set the GST_PLUGIN_SCANNER environment variable if your setup is unusual. This should normally not be required though.
(python3:2651): GStreamer-WARNING **: 13:50:44.273: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_udp.so': librivermax.so.1: cannot open shared object file: No such file or directory
Creating Pipeline
Creating streamux
I did not found R535.104.12
