TF-TRT Warning: Could not find TensorRT

Issue type
Bug
Have you reproduced the bug with TensorFlow Nightly?
No
Source
source
TensorFlow version
2.15.0
Custom code
No
OS platform and distribution
Ubuntu 22.04
Mobile device
No response
Python version
3.11
Bazel version
No response
GCC/compiler version
11.2
CUDA/cuDNN version
12.4
GPU model and memory
RTX 3050 TI
Current behavior?
Hey everybody,
I have tried installing tensorflow from my ubuntu 22.04 terminal with anaconda environment.  I am getting Can not find TensorRT.  I have a 4 monitor setup with Ubuntu and the 550 NVIDIA driver for the RTX 3050 TI does not work on my machine.  I have to run the 535 driver, and I think 12.4 CUDA is supported.  It installed the 550 driver automatically and I revert back to the 535 driver.  But after all that I still ended up with the error below.
I have tried uninstalling, reinstalling, and can't get it to work.  I am a graduate student taking a Machine Learning course.  I am spending more time debugging this issue then actually coding.
Could somebody help me resolve?
Standalone code to reproduce the issue
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.161.08             Driver Version: 535.161.08   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 3050 ...    Off | 00000000:01:00.0  On |                  N/A |
| N/A   66C    P8               8W /  35W |   1011MiB /  4096MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A      2812      G   /usr/lib/xorg/Xorg                          648MiB |
|    0   N/A  N/A      3023      G   /usr/bin/gnome-shell                        148MiB |
|    0   N/A  N/A     78082      G   ...az/anaconda3/envs/skynet/bin/python        1MiB |
|    0   N/A  N/A     78475      G   ...seed-version=20240329-165146.919000       96MiB |
|    0   N/A  N/A     79970      G   ...gnu/webkit2gtk-4.0/WebKitWebProcess       68MiB |
+---------------------------------------------------------------------------------------+
(base) thenaz@Skynet:~/Downloads$ lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation GA107M [GeForce RTX 3050 Ti Mobile] (rev a1)
01:00.1 Audio device: NVIDIA Corporation Device 2291 (rev a1)
(base) thenaz@Skynet:~/Downloads$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Feb_27_16:19:38_PST_2024
Cuda compilation tools, release 12.4, V12.4.99
Build cuda_12.4.r12.4/compiler.33961263_0

Relevant log output
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2024-03-30 14:29:58.668656: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-30 14:29:58.691384: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-03-30 14:29:58.691405: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-03-30 14:29:58.692006: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-03-30 14:29:58.695703: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-30 14:29:59.119119: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-03-30 14:29:59.660586: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-30 14:29:59.675221: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-30 14:29:59.675340: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

I have same issue on python 3.8 image on docker

I'm having the same problem, manage to solve it?
tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

i think pip install tensorflow[cuda] will solve the issue but i am not quite sure

Did you install TensorRT? You can install from here:
https://developer.nvidia.com/tensorrt

Hi @MachinesOnTheRise ,
You might need to install compatible TensorRT version from Nvidia. Since this is a optimization library for Inferences and developed by Nvidia for their specific GPUs, it may not come bundled with TF package. You can use tensorflow[and-cuda] package for installing TF with required cuda/cudnn packages as below.But they seems don't have tensorrt bundled.
https://github.com/tensorflow/tensorflow/blob/ced28148be8cec1f234886f21c7e528c374032b8/tensorflow/tools/pip_package/setup.py#L151-L166

W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
I'm getting this error while running dreambooth lora on colab .
pip install tensorflow[and-cuda] -- This also not solved the problem.
Any solutions ?

You need to install TensorRT separately. It's not bundled with tensorflow.

W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
I get this error in docker

@MachinesOnTheRise @vinay-colaco @SuryanarayanaY @Dheeraj2002
Any update? Sadly been getting the same issue for about a month now.
This what i am getting:
2024-04-21 08:00:13.414353: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-21 08:00:13.552935: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-21 08:00:14.655097: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-21 08:00:16.735268: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:17:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-04-21 08:00:16.828498: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.

I have manually installed TensorRT and still have the same error
Here is my Dockerfile for reference:
FROM nvidia/cuda:12.0.0-devel-ubuntu20.04

ENV DEBIAN_FRONTEND noninteractive

RUN apt-get update && \
    apt-get install -y git ffmpeg libssl-dev lsb-release wget curl

ENV PATH="/root/miniconda3/bin:${PATH}"
ARG PATH="/root/miniconda3/bin:${PATH}"

RUN wget \
    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
    && mkdir /root/.conda \
    && bash Miniconda3-latest-Linux-x86_64.sh -b \
    && rm -f Miniconda3-latest-Linux-x86_64.sh s

RUN conda --version
# conda 24.1.2

# CUDNN
RUN apt clean
RUN apt update
RUN apt autoremove

RUN wget https://developer.download.nvidia.com/compute/cudnn/9.1.0/local_installers/cudnn-local-repo-ubuntu2004-9.1.0_1.0-1_amd64.deb
RUN dpkg -i cudnn-local-repo-ubuntu2004-9.1.0_1.0-1_amd64.deb
RUN cp /var/cudnn-local-repo-ubuntu2004-9.1.0/cudnn-*-keyring.gpg /usr/share/keyrings/
RUN apt-get update
RUN apt-get -y install cudnn-cuda-12

# CUBLASS
RUN curl https://developer.download.nvidia.com/hpc-sdk/ubuntu/DEB-GPG-KEY-NVIDIA-HPC-SDK |  gpg --dearmor -o /usr/share/keyrings/nvidia-hpcsdk-archive-keyring.gpg
RUN echo 'deb [signed-by=/usr/share/keyrings/nvidia-hpcsdk-archive-keyring.gpg] https://developer.download.nvidia.com/hpc-sdk/ubuntu/amd64 /' |  tee /etc/apt/sources.list.d/nvhpc.list
RUN apt-get update -y
RUN apt-get install -y nvhpc-24-3
RUN apt install -f

#TENSORRT
RUN wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/local_repos/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb
RUN dpkg -i nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb
RUN cp /var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/*-keyring.gpg /usr/share/keyrings/
RUN apt-get update
RUN apt-get install -y tensorrt
RUN apt-get install -y libnvinfer-lean8
RUN apt-get install -y libnvinfer-vc-plugin8
RUN python3 -m pip install numpy

RUN dpkg-query -W tensorrt
#tensorrt	<IP_ADDRESS>-1+cuda12.4

RUN conda install python=3.9.18

RUN pip install boto3 spleeter tensorrt cuda-python
RUN pip install git+https://github.com/facebookresearch/audiocraft.git
RUN pip install torch==2.1.0+cu121 --index-url https://download.pytorch.org/whl/cu121
RUN pip install tensorflow==2.16.1

RUN find / -name '*libcudart*'
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart_static.a
/root/miniconda3/lib/python3.9/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12
/root/miniconda3/lib/python3.9/site-packages/torch/lib/libcudart-9335f6a2.so.12
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12.0.107
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart_static.a

RUN find / -name '*tensorrt*'
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101
/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart_static.a
/root/miniconda3/lib/python3.9/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12
/root/miniconda3/lib/python3.9/site-packages/torch/lib/libcudart-9335f6a2.so.12
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12.0.107
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so
/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart_static.a
Expand

=> Step 35: RUN find / -name '*tensorrt*'
/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings
/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings/tensorrt.so
/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings-8.6.1.dist-info
/root/miniconda3/lib/python3.9/site-packages/tensorrt_libs
/root/miniconda3/lib/python3.9/site-packages/tensorrt_libs-8.6.1.dist-info
/root/miniconda3/lib/python3.9/site-packages/tensorrt
/root/miniconda3/lib/python3.9/site-packages/tensorrt-8.6.1.post1.dist-info
/root/miniconda3/lib/python3.9/site-packages/tensorflow/python/compiler/tensorrt
/root/miniconda3/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v2/experimental/tensorrt
/root/miniconda3/lib/python3.9/site-packages/tensorflow/_api/v2/experimental/tensorrt
/root/miniconda3/lib/python3.9/site-packages/tensorflow/compiler/tf2tensorrt
/root/miniconda3/lib/python3.9/site-packages/tensorflow/include/tensorflow/compiler/tf2tensorrt
/root/miniconda3/lib/python3.9/site-packages/torch/_dynamo/backends/tensorrt.py
/root/miniconda3/lib/python3.9/site-packages/torch/_dynamo/backends/__pycache__/tensorrt.cpython-39.pyc
/root/miniconda3/lib/python3.9/site-packages/torch/ao/quantization/backend_config/tensorrt.py
/root/miniconda3/lib/python3.9/site-packages/torch/ao/quantization/backend_config/__pycache__/tensorrt.cpython-39.pyc
/usr/share/doc/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0
/usr/share/doc/tensorrt-<IP_ADDRESS>
/usr/share/doc/tensorrt
/usr/share/keyrings/nv-tensorrt-local-9A1EDFBA-keyring.gpg
/usr/lib/python3.8/dist-packages/tensorrt
/usr/lib/python3.8/dist-packages/tensorrt/tensorrt.so
/usr/lib/python3.8/dist-packages/tensorrt-10.0.0b6.dist-info
/usr/lib/python3.8/dist-packages/tensorrt_lean
/usr/lib/python3.8/dist-packages/tensorrt_lean/tensorrt_lean.so
/usr/lib/python3.8/dist-packages/tensorrt_lean-10.0.0b6.dist-info
/usr/lib/python3.8/dist-packages/tensorrt_dispatch
/usr/lib/python3.8/dist-packages/tensorrt_dispatch/tensorrt_dispatch.so
/usr/lib/python3.8/dist-packages/tensorrt_dispatch-10.0.0b6.dist-info
/usr/src/tensorrt
/usr/src/tensorrt/samples/python/yolov3_onnx/onnx_to_tensorrt.py
/etc/apt/sources.list.d/nv-tensorrt-local-ubuntu2004-8.6.1-cuda-12.0.list
/var/lib/apt/lists/_var_nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_InRelease
/var/lib/apt/lists/_var_nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_Packages.lz4
/var/lib/dpkg/info/tensorrt.md5sums
/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.list
/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.conffiles
/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.md5sums
/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.postinst
/var/lib/dpkg/info/tensorrt.list
/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0
/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/nv-tensorrt-local-9A1EDFBA-keyring.gpg
/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt-dev_<IP_ADDRESS>-1+cuda12.0_amd64.deb
/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt-libs_<IP_ADDRESS>-1+cuda12.0_amd64.deb
/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt_<IP_ADDRESS>-1+cuda12.0_amd64.deb
/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb

RUN python3 -c "import tensorrt; print(tensorrt.__path__)"
RUN python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
RUN python -c "import tensorflow.compiler as tf_cc; print(tf_cc.tf2tensorrt._pywrap_py_utils.get_linked_tensorrt_version())"
RUN pip list

RUN nvcc --version
#nvcc: NVIDIA (R) Cuda compiler driver
#Copyright (c) 2005-2023 NVIDIA Corporation
#Built on Fri_Jan__6_16:45:21_PST_2023
#Cuda compilation tools, release 12.0, V12.0.140
#Build cuda_12.0.r12.0/compiler.32267302_0

RUN lsb_release -a
# No LSB modules are available.
# Distributor ID: Ubuntu
# Description:    Ubuntu 20.04.5 LTS
# Release:        20.04
# Codename:       focal

Hi @MachinesOnTheRise ,
Can you confirm the output of this code that is in your docker file?
python3 -c "import tensorrt; print(tensorrt.path)"

python3 -c "import tensorrt; print(tensorrt.path)"

you should better run
python3 -c "import tensorrt; print(tensorrt.file)"

That works. Here's the value I get:
/home/azureuser/miniconda3/envs/tensorflow-2.16.1/lib/python3.12/site-packages/tensorrt/__init__.py

I am having the same issues and have tried all the listed remedies to no avail. I followed nvidia instructions and installed from tar.gz, as per the latest above I do verify the import does see tensorrt. I am trying to run it in a simple anaconda virtual environment. This is the only library I am having issues with, no matter what I do, no change in outcome, I am wondering if we have a real bug here.

Just commenting to say I am experiencing the exact same issue.  Some follow up information.
which python3
>>> /home/bplaster/anaconda3/bin/python3

python3 -c "import tensorrt; print(tensorrt.__file__)"
>>> /home/bplaster/anaconda3/lib/python3.11/site-packages/tensorrt/__init__.py

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices())"
>>> 2024-05-02 15:35:07.243819: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-05-02 15:35:07.269922: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-02 15:35:07.628229: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-05-02 15:35:08.153905: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-05-02 15:35:08.156012: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]

nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Thu_Nov_18_09:45:30_PST_2021
Cuda compilation tools, release 11.5, V11.5.119
Build cuda_11.5.r11.5/compiler.30672275_0

Some more information for the devs maintaining Tensorflow.
When attempting to run the following in terminal:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
I get the following error:
2024-05-03 14:51:51.684995: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-05-03 14:51:52.375372: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]

TensorRT is installed and I'm quite certain that I have all required nvidia drivers, CUDA, cuDNN toolkits, etc. installed...
nvidia-smi results in:
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| 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  Tesla V100-PCIE-16GB           Off |   00000001:00:00.0 Off |                  Off |
| N/A   29C    P0             25W /  250W |     102MiB /  16384MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A       926      G   /usr/lib/xorg/Xorg                             87MiB |
|    0   N/A  N/A      1009      G   /usr/bin/gnome-shell                           13MiB |
+-----------------------------------------------------------------------------------------+

I am able to run other libraries on the GPU (e.g. XGBoost) with no problem. Consequently, this is a Tensorflow issue.
Here's my very basic dev environment:
# packages in environment at /home/azureuser/miniconda3/envs/tensorflow-2.16.1:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       2_gnu    conda-forge
absl-py                   2.1.0                    pypi_0    pypi
astunparse                1.6.3                    pypi_0    pypi
bzip2                     1.0.8                hd590300_5    conda-forge
ca-certificates           2024.2.2             hbcca054_0    conda-forge
certifi                   2024.2.2                 pypi_0    pypi
charset-normalizer        3.3.2                    pypi_0    pypi
flatbuffers               24.3.25                  pypi_0    pypi
gast                      0.5.4                    pypi_0    pypi
google-pasta              0.2.0                    pypi_0    pypi
grpcio                    1.63.0                   pypi_0    pypi
h5py                      3.11.0                   pypi_0    pypi
idna                      3.7                      pypi_0    pypi
keras                     3.3.3                    pypi_0    pypi
ld_impl_linux-64          2.40                 h55db66e_0    conda-forge
libclang                  18.1.1                   pypi_0    pypi
libexpat                  2.6.2                h59595ed_0    conda-forge
libffi                    3.4.2                h7f98852_5    conda-forge
libgcc-ng                 13.2.0               h77fa898_6    conda-forge
libgomp                   13.2.0               h77fa898_6    conda-forge
libnsl                    2.0.1                hd590300_0    conda-forge
libsqlite                 3.45.3               h2797004_0    conda-forge
libuuid                   2.38.1               h0b41bf4_0    conda-forge
libxcrypt                 4.4.36               hd590300_1    conda-forge
libzlib                   1.2.13               hd590300_5    conda-forge
markdown                  3.6                      pypi_0    pypi
markdown-it-py            3.0.0                    pypi_0    pypi
markupsafe                2.1.5                    pypi_0    pypi
mdurl                     0.1.2                    pypi_0    pypi
ml-dtypes                 0.3.2                    pypi_0    pypi
namex                     0.0.8                    pypi_0    pypi
ncurses                   6.4.20240210         h59595ed_0    conda-forge
numpy                     1.26.4                   pypi_0    pypi
nvidia-cublas-cu12        <IP_ADDRESS>                 pypi_0    pypi
nvidia-cuda-cupti-cu12    12.3.101                 pypi_0    pypi
nvidia-cuda-nvcc-cu12     12.3.107                 pypi_0    pypi
nvidia-cuda-nvrtc-cu12    12.3.107                 pypi_0    pypi
nvidia-cuda-runtime-cu12  12.3.101                 pypi_0    pypi
nvidia-cudnn-cu12         <IP_ADDRESS>                 pypi_0    pypi
nvidia-cufft-cu12         <IP_ADDRESS>                pypi_0    pypi
nvidia-curand-cu12        <IP_ADDRESS>               pypi_0    pypi
nvidia-cusolver-cu12      <IP_ADDRESS>               pypi_0    pypi
nvidia-cusparse-cu12      <IP_ADDRESS>               pypi_0    pypi
nvidia-nccl-cu12          2.19.3                   pypi_0    pypi
nvidia-nvjitlink-cu12     12.3.101                 pypi_0    pypi
openssl                   3.3.0                hd590300_0    conda-forge
opt-einsum                3.3.0                    pypi_0    pypi
optree                    0.11.0                   pypi_0    pypi
packaging                 24.0                     pypi_0    pypi
pip                       24.0               pyhd8ed1ab_0    conda-forge
protobuf                  4.25.3                   pypi_0    pypi
pygments                  2.17.2                   pypi_0    pypi
python                    3.11.9          hb806964_0_cpython    conda-forge
readline                  8.2                  h8228510_1    conda-forge
requests                  2.31.0                   pypi_0    pypi
rich                      13.7.1                   pypi_0    pypi
setuptools                69.5.1             pyhd8ed1ab_0    conda-forge
six                       1.16.0                   pypi_0    pypi
tensorboard               2.16.2                   pypi_0    pypi
tensorboard-data-server   0.7.2                    pypi_0    pypi
tensorflow                2.16.1                   pypi_0    pypi
tensorflow-io-gcs-filesystem 0.37.0                   pypi_0    pypi
tensorrt                  10.0.1                   pypi_0    pypi
tensorrt-cu12             10.0.1                   pypi_0    pypi
termcolor                 2.4.0                    pypi_0    pypi
tk                        8.6.13          noxft_h4845f30_101    conda-forge
typing-extensions         4.11.0                   pypi_0    pypi
tzdata                    2024a                h0c530f3_0    conda-forge
urllib3                   2.2.1                    pypi_0    pypi
werkzeug                  3.0.2                    pypi_0    pypi
wheel                     0.43.0             pyhd8ed1ab_1    conda-forge
wrapt                     1.16.0                   pypi_0    pypi
xz                        5.2.6                h166bdaf_0    conda-forge

Any luck in replicating the issue?

Wanted to add more onto this, I spent a day trying various WSL Tensorflow installation methods and still no luck.
For context, I'm on Windows 11, with an i7-13700 and an RTX A4000 Series GPU.
Here are my printouts:
nvidia-smi
Fri May  3 09:50:02 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.76.01              Driver Version: 552.22         CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| 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 RTX A4000               On  |   00000000:01:00.0 Off |                  Off |
| 45%   37C    P0             35W /  140W |       0MiB /  16376MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0

 which python
/home/bplaster/anaconda3/bin/python

interestingly, after using pip install tensorflow[and-cuda] within the WSL environment, I was getting this:
python3 -c "import tensorrt; print(tensorrt.__file__)"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'tensorrt'

I attempted to resolve this with: pip install tensorrt
Collecting tensorrt
  Downloading tensorrt-10.0.1.tar.gz (16 kB)
  Preparing metadata (setup.py) ... done
Collecting tensorrt-cu12 (from tensorrt)
  Downloading tensorrt-cu12-10.0.1.tar.gz (18 kB)
  Preparing metadata (setup.py) ... done
Building wheels for collected packages: tensorrt, tensorrt-cu12
  Building wheel for tensorrt (setup.py) ... done
  Created wheel for tensorrt: filename=tensorrt-10.0.1-py2.py3-none-any.whl size=16333 sha256=19d103fecc3bfb19836fe815d369e65171978f61bb476e89ae84b857953f64e7
  Stored in directory: /home/bplaster/.cache/pip/wheels/31/90/ef/53ad98d9a1bd660c0177aa1ea91bde288edfbe4d15621ca472
  Building wheel for tensorrt-cu12 (setup.py) ... done
  Created wheel for tensorrt-cu12: filename=tensorrt_cu12-10.0.1-py2.py3-none-any.whl size=17549 sha256=5877c62938e16384e9c969b00a94dea5bda1bea7de2db0249bcfd8520d5082fd
  Stored in directory: /home/bplaster/.cache/pip/wheels/c5/08/30/a058eaf14eeeabb3799e3abd3a2a534649123824dcd9695876
Successfully built tensorrt tensorrt-cu12
Installing collected packages: tensorrt-cu12, tensorrt
Successfully installed tensorrt-10.0.1 tensorrt-cu12-10.0.1

Checking the nvidia compatibility matrrix, this should be good to go:

Now, we see that TensorRT is there when called:
python3 -c "import tensorrt; print(tensorrt.__file__)"
/home/bplaster/anaconda3/lib/python3.11/site-packages/tensorrt/__init__.py

Yet we still get the following message when checking the Tensorflow version:
python -c "import tensorflow as tf; print(tf.__version__)"
2024-05-03 09:51:40.606740: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-05-03 09:51:40.632018: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-03 09:51:41.004536: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2.16.1

Finally, confirming that the GPU cannot be seen by Tensorflow for some reason:
python -c "import tensorflow as tf; print(tf.config.list_physical_devices())"
2024-05-03 10:01:27.648034: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-05-03 10:01:27.673662: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-03 10:01:28.043090: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-05-03 10:01:30.488133: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-05-03 10:01:30.501591: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]

This is all done on a completely fresh WSL2 Ubuntu environment, all done in the (base) conda environment.  Hope this is helpful.

Same issue:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2024-05-08 12:14:54.731695: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-08 12:14:55.226452: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-05-08 12:14:55.882997: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-05-08 12:14:55.911562: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-05-08 12:14:55.911640: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:2b:00.0/numa_node
Your kernel may have been built without NUMA support.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

My nvidia drivers and cuda version:
nvidia-smi
Wed May  8 12:06:07 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.76.01              Driver Version: 552.22         CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| 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 3060 Ti     On  |   00000000:2B:00.0  On |                  N/A |
| 30%   31C    P0             42W /  200W |     979MiB /   8192MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Thu_Nov_18_09:45:30_PST_2021
Cuda compilation tools, release 11.5, V11.5.119
Build cuda_11.5.r11.5/compiler.30672275_0

I've installed tensorflow and tensorrt through pip:
pip install tensorflow[and-cuda]
pip install tensorrt

I have follow the guide: Install TensorFlow with pip and added path as in Install Tensorflow & Pytorch with CUDA [Linux | WSL2] but I'm still getting the same warning (TF-TRT Warning: Could not find TensorRT).
Does any anyone managed to find a solution?

Any solution?

Same issue here, installed tensorrt with pip install --extra-index-url https://pypi.nvidia.com tensorrt-libs followed by pip install tensorflow[and-cuda]==2.15.0. Yet, I get the following when running it in python (3.10.8):
>>> import tensorflow 2024-05-14 17:25:38.312524: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-05-14 17:25:38.312576: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-05-14 17:25:38.314278: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-05-14 17:25:38.322356: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-05-14 17:25:39.497691: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Has anyone managed to make it work ?

I think that the only way is to find the compatible versions for python, tensorflow, nvidia driver, cuda, cudnn and tensorrt. I'll try the tested build configurations from here in WSL and Ubuntu. Hope it works 🙏

I think that the only way is to find the compatible versions for python, tensorflow, nvidia driver, cuda, cudnn and tensorrt. I'll try the tested build configurations from here in WSL and Ubuntu. Hope it works 🙏

So, after trying out pretty much everything else, I can confirm that this worked out and my GPU is now also detected.  I am on Ubuntu  <IP_ADDRESS> running Tensorflow 2.16.
Many thanks @BigKhalPablo, saved my day ;)
Cheers,
Eric

Could you explain how you managed to make it work @eric86y? Did you follow the "build from source" guide for Linux or the "install with pip"? I'm trying in WSL2 but I haven't made any progress yet 😞

Couldn't resolve TF-TRT Warning: Could not find TensorRT
I'm using wsl2 in windows 11.
Distributor ID: Ubuntu
Description:    Ubuntu 22.04.4 LTS
Release:        22.04
Codename:       jammy

Previously, I wasn't able to have GPU as the backend, I had tried all the methods in installing tensorflow in wsl.
But, trying with python3.11 and tensorflow  2.15.1, got me access to the GPU backend.
conda create -n tmpenv python=3.11
conda activate tmpenv
pip install tensorflow[and-cuda]==2.15.1

versions:
tensorflow                    2.15.1
tensorrt                      10.0.1

nvidia-smi:
Tue May 21 15:45:23 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.76.01              Driver Version: 552.44         CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| 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 4080 ...    On  |   00000000:01:00.0 Off |                  N/A |
| N/A   38C    P4             17W /   55W |       0MiB /  12282MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

2024-05-21 15:46:26.487553: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fcc3f7b2c00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-05-21 15:46:26.487663: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 4080 Laptop GPU, Compute Capability 8.9

I've tried all ways of installing tensorrt - pip, .deb, .tar;
updated path variable, etc.
Its importing in python shell
>>> import tensorrt as trt
>>> trt.__version__
'10.0.1'
>>> trt.__file__
'/home/mohan/miniconda3/envs/tmpenv/lib/python3.11/site-packages/tensorrt/__init__.py'

All the files are available (.tar installation method)
(tmpenv) mohan@LAPTOP-INPO8147:~$ ls TensorRT-<IP_ADDRESS>/
bin  data  doc  include  lib  onnx_graphsurgeon  python  samples  targets

(tmpenv) mohan@LAPTOP-INPO8147:~$ ls TensorRT-<IP_ADDRESS>/lib/
libnvinfer.so                          libnvinfer_plugin.so.10.0.1
libnvinfer.so.10                       libnvinfer_plugin_static.a
libnvinfer.so.10.0.1                   libnvinfer_static.a
libnvinfer_builder_resource.so.10.0.1  libnvinfer_vc_plugin.so
libnvinfer_dispatch.so                 libnvinfer_vc_plugin.so.10
libnvinfer_dispatch.so.10              libnvinfer_vc_plugin.so.10.0.1
libnvinfer_dispatch.so.10.0.1          libnvinfer_vc_plugin_static.a
libnvinfer_dispatch_static.a           libnvonnxparser.so
libnvinfer_lean.so                     libnvonnxparser.so.10
libnvinfer_lean.so.10                  libnvonnxparser.so.10.0.1
libnvinfer_lean.so.10.0.1              libnvonnxparser_static.a
libnvinfer_lean_static.a               libonnx_proto.a
libnvinfer_plugin.so                   stubs
libnvinfer_plugin.so.10

(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-<IP_ADDRESS>/python$ ls
tensorrt-10.0.1-cp310-none-linux_x86_64.whl
tensorrt-10.0.1-cp311-none-linux_x86_64.whl
tensorrt-10.0.1-cp312-none-linux_x86_64.whl
tensorrt-10.0.1-cp38-none-linux_x86_64.whl
tensorrt-10.0.1-cp39-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp310-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp312-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp38-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp39-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp310-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp312-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp38-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp39-none-linux_x86_64.whl

I've also used the files from .tar method to install using pip:
(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-<IP_ADDRESS>/python$ python3 -m pip install tensorrt-10.0.1-cp311-none-linux_x86_64.whl
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Processing ./tensorrt-10.0.1-cp311-none-linux_x86_64.whl
tensorrt is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.

(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-<IP_ADDRESS>/python$ python3 -m pip install tensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Processing ./tensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl
tensorrt-dispatch is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.

(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-<IP_ADDRESS>/python$ python3 -m pip install tensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Processing ./tensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl
tensorrt-lean is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.

But still I get this error
2024-05-21 15:46:19.659564: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT I'm getting this error while running dreambooth lora on colab . pip install tensorflow[and-cuda] -- This also not solved the problem. Any solutions ?

I got exactly the same question like you, any ideas?

W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT I'm getting this error while running dreambooth lora on colab . pip install tensorflow[and-cuda] -- This also not solved the problem. Any solutions ?

I got exactly the same question like you, any ideas?

still no

Setting TF_CPP_MAX_VLOG_LEVEL=3 in my environment (WSL2 Ubuntu) gives the following specific error:
2024-06-04 15:42:11.291621: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer.so.8.6.1'; dlerror: libnvinfer.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/zilla/miniconda3/lib/:/home/zilla/miniconda3/lib/python3.12/site-packages/nvidia/cudnn/lib
2024-06-04 15:42:11.291675: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer_plugin.so.8.6.1'; dlerror: libnvinfer_plugin.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/zilla/miniconda3/lib/:/home/zilla/miniconda3/lib/python3.12/site-packages/nvidia/cudnn/lib
2024-06-04 15:42:11.291687: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

While I have TensorRT, and thus libnvinfer, installed, the actual libraries are named libnvinfer.so.10 and libnvinfer_plugin.so.10
Just as a test, I did the following:
~/miniconda3/lib$ ln -s python3.12/site-packages/tensorrt_libs/libnvinfer.so.10 libnvinfer.so.8.6.1
~/miniconda3/lib$ ln -s python3.12/site-packages/tensorrt_libs/libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1

And the warning about not finding TensorRT diapered.
Of course, if the libraries are not binary compatible, this might lead to further issues down the line, but at least it hints at what the problem is.

//cc @learning-to-play

@tilakrayal This issue seems to be related to TensorRT and GPU. Could you inform them?

Setting TF_CPP_MAX_VLOG_LEVEL=3 in my environment (WSL2 Ubuntu) gives the following specific error:
2024-06-04 15:42:11.291621: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer.so.8.6.1'; dlerror: libnvinfer.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/zilla/miniconda3/lib/:/home/zilla/miniconda3/lib/python3.12/site-packages/nvidia/cudnn/lib
2024-06-04 15:42:11.291675: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer_plugin.so.8.6.1'; dlerror: libnvinfer_plugin.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/zilla/miniconda3/lib/:/home/zilla/miniconda3/lib/python3.12/site-packages/nvidia/cudnn/lib
2024-06-04 15:42:11.291687: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

While I have TensorRT, and thus libnvinfer, installed, the actual libraries are named libnvinfer.so.10 and libnvinfer_plugin.so.10 Just as a test, I did the following:
~/miniconda3/lib$ ln -s python3.12/site-packages/tensorrt_libs/libnvinfer.so.10 libnvinfer.so.8.6.1
~/miniconda3/lib$ ln -s python3.12/site-packages/tensorrt_libs/libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1

And the warning about not finding TensorRT diapered. Of course, if the libraries are not binary compatible, this might lead to further issues down the line, but at least it hints at what the problem is.

Yes. It works.
I have the miniconda env "cuda12", cuda 12, tensorrt 10 on Ubuntu 22, WSL2.
That is my commands:
cd ~/miniconda3/envs/cuda12/lib/python3.11/site-packages/tensorrt_libs/
ln -s libnvinfer.so.10 libnvinfer.so.8.6.1
ln -s libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1

Got exactly into the same problem with Python 3.11, CUDA 12.4 and TensorRT 10.1. Tried suggestion by @PavelValentov - it appears it's a working solution / workaround.
What I did:
`
export TF_CPP_MAX_VLOG_LEVEL=3
Run my Python program and got:
2024-06-17 21:54:19.501338: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer.so.8.6.1'; dlerror: libnvinfer.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:/usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:
2024-06-17 21:54:19.501403: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer_plugin.so.8.6.1'; dlerror: libnvinfer_plugin.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:/usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:
`
Then I did:
`
cd /usr/local/lib/python3.11/dist-packages/tensorrt/lib
sudo ln -s libnvinfer.so.10 libnvinfer.so.8.6.1
`
My application is not complaining about missing TensorRT library.

I am having the same issue while installing Tensorflow.
I am using Ubuntu (WSL)
TF-TRT Warning: Could not find TensorRT

nvidia-smi

nvcc --version

Have added the tensorrt_libs to LD_LIBRARY_PATH.
Still getting the same error. Any suggestion to solve this will be welcome.

I am having the same issue while installing Tensorflow. I am using Ubuntu (WSL)
TF-TRT Warning: Could not find TensorRT
nvidia-smi
nvcc --version
Have added the tensorrt_libs to LD_LIBRARY_PATH. Still getting the same error. Any suggestion to solve this will be welcome.

See my answer above...

Got exactly into the same problem with Python 3.11, CUDA 12.4 and TensorRT 10.1. Tried suggestion by @Zilla - it appears it's a working solution / workaround.
What I did:
export TF_CPP_MAX_VLOG_LEVEL=3
# Ran my Python program and got:
2024-06-17 21:54:19.501338: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer.so.8.6.1'; dlerror: libnvinfer.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:/usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:
2024-06-17 21:54:19.501403: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer_plugin.so.8.6.1'; dlerror: libnvinfer_plugin.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:/usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:

Then I did:
cd /usr/local/lib/python3.11/dist-packages/tensorrt/lib
sudo ln -s libnvinfer.so.10 libnvinfer.so.8.6.1
sudo ln -s libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1

My application is not complaining about missing TensorRT library.

This hint saved me after two endless weeks of trying to load tensorrt. My environment is Docker on Ubuntu 22.04 WSL2 .  Python 3.11, CUDA 12.4 and TensorRT 10.1. I was able to reproduce this solution several times.
A more detailed step-by-step reminder for myself (first of all) and for everyone in need:

Install TensorRT. I did it this way:

RUN python3 -m pip install --upgrade pip
RUN python3 -m pip install wheel
RUN python3 -m pip install --upgrade tensorrt

Find where your tensorrt_libs is
find / -xdev 2>/dev/null -name "tensorrt"
IN my case it is /usr/local/lib/python3.11/site-packages/tensorrt_libs/

(sudo) nano ~/.bashrc
Add this path and switch on logging

export TF_CPP_MAX_VLOG_LEVEL=2
export LD_LIBRARY_PATH==$LD_LIBRARY_PATH:/usr/local/lib/python3.11/site-packages/tensorrt_libs

I found level 2 sufficient for debugging.

Create symlinks

ln -s /usr/local/lib/python3.11/site-packages/tensorrt_libs/libnvinfer.so.10 /usr/local/lib/python3.11/site-packages/tensorrt_libs/libnvinfer.so.8.6.1

ln -s /usr/local/lib/python3.11/site-packages/tensorrt_libs/libnvinfer_plugin.so.10 /usr/local/lib/python3.11/site-packages/tensorrt_libs/libnvinfer_plugin.so.8.6.1

Restart docker

Test. Launch python and type in LINE BY LINE

import tensorflow
tensorflow.__version__
from tensorflow.python.compiler.tensorrt import trt_convert as trt
trt.trt_utils._pywrap_py_utils.get_linked_tensorrt_version()
trt.trt_utils._pywrap_py_utils.get_loaded_tensorrt_version()

Line 1 must return the following (alongside with a bunch of useless informational messages)
2024-06-18 20:39:02.652705: I external/local_tsl/tsl/platform/default/dso_loader.cc:57] Successfully opened dynamic library libnvinfer_plugin.so.8.6.1
2024-06-18 20:39:02.652795: I external/local_tsl/tsl/platform/default/dso_loader.cc:57] Successfully opened dynamic library libnvinfer.so.8.6.1

Line 4 mist return smth like (8,6,1)
Line 5 must return smth like (10,0,0) – if error, then still not fixed. Go back and re-check steps.

For completeness, I've extended @maxgorbachevskiy 's answer a bit.
The following setup did work for me on Docker 26.1.4, Debian 6.1.90.

Create requirements.txt file as follows:

tensorflow==2.16.1
tensorrt==10.1.0

Create tensorflow_tensorrt_test.py file as follows (sort of a demo application):

import tensorflow as tf
from tensorflow.python.compiler.tensorrt import trt_convert as trt

print("TensorFlow version:", tf.__version__)
print("TensorRT version:", trt.trt_utils._pywrap_py_utils.get_linked_tensorrt_version())

Create Dockerfile as follows. It create Docker virtual environment (probably, not required) and runs application:

FROM python:3.11

WORKDIR /app
COPY requirements.txt /app
COPY tensorflow_tensorrt_test.py /app

# Set up a virtual environment
ENV VIRTUAL_ENV=/app/.venv
RUN python3 -m venv $VIRTUAL_ENV
ENV PATH="$VIRTUAL_ENV/bin:$PATH"

# Setup path to libraries
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/app/.venv/lib/python3.11/site-packages/tensorrt_libs

# Install TensorRT
RUN pip install --upgrade pip \
    && pip install wheel \
    && pip install --no-cache-dir -r /app/requirements.txt

# Hack - create proper symlinks
RUN cd /app/.venv/lib/python3.11/site-packages/tensorrt_libs \
    && ln -s libnvinfer.so.10 libnvinfer.so.8.6.1 \
    && ln -s libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1

CMD python tensorflow_tensorrt_test.py

Make Docker image and run it:
docker build -t tensorrt-test .
docker run -it tensorrt-test

Output:
TensorFlow version: 2.16.1
TensorRT version: (8, 6, 1)

Probably not the most elegant solution, however - it works.

I am having the same issue while installing Tensorflow. I am using Ubuntu (WSL)
Have added the tensorrt_libs to LD_LIBRARY_PATH. Still getting the same error. Any suggestion to solve this will be welcome.

See my answer above...

I tried the above steps. But it is still not working

I am having the same issue while installing Tensorflow. I am using Ubuntu (WSL)
Have added the tensorrt_libs to LD_LIBRARY_PATH. Still getting the same error. Any suggestion to solve this will be welcome.

See my answer above...

I tried the above steps. But it is still not working

Supposedly your LD_LIBRARY_PATH  is not read correctly from bashrc.

Here is the path of LD_LIBRARY_PATH

Here is the path of LD_LIBRARY_PATH

Your LD_LIBRARY_PATH is pointing to an entirely different and incorrect location. As said by several folks above, please follow the manuals above very precisely.

Thank you all for your help. I was able to resolve it by adding the tensorrtlibs to LD_LIBRARY_PATH.

I'm frustrated with tensorflow, 1 decade after launch and its still a nightmare to get it to work with gpu

Hi, I was able to get it fixed. Here are the steps:

download Tensor RT from https://developer.nvidia.com/tensorrt/download
now extract and move that folder to /usr/local/TensorRT-8.6.1

extract and move that folder to /usr/local/TensorRT-8.6.1

tar -xzvf TensorRT-<IP_ADDRESS>.Linux.x86_64-gnu.cuda-12.0.tar.gz

sudo mv TensorRT-<IP_ADDRESS> /usr/local/TensorRT-8.6.1

Update the path

code ~/.bashrc

export PATH=/usr/local/cuda-12.1/bin:/usr/local/TensorRT-8.6.1/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64:/usr/local/TensorRT-8.6.1/lib:$LD_LIBRARY_PATH

source ~/.bashrc

*now go to the extracted folder and install tensorrt from there
cd /usr/local/TensorRT-8.6.1/python

pip install tensorrt-8.6.1-cp310-none-linux_x86_64.whl

pip install tensorrt_dispatch-8.6.1-cp310-none-linux_x86_64.whl

pip install tensorrt_lean-8.6.1-cp310-none-linux_x86_64.whl

Hope this helps,
i was using python 3.10 tensor rt folder has lib for other python versions to

Hi, I was able to get it fixed. Here are the steps:

download Tensor RT from https://developer.nvidia.com/tensorrt/download
now extract and move that folder to /usr/local/TensorRT-8.6.1

extract and move that folder to /usr/local/TensorRT-8.6.1

tar -xzvf TensorRT-<IP_ADDRESS>.Linux.x86_64-gnu.cuda-12.0.tar.gz

sudo mv TensorRT-<IP_ADDRESS> /usr/local/TensorRT-8.6.1

Update the path

code ~/.bashrc

export PATH=/usr/local/cuda-12.1/bin:/usr/local/TensorRT-8.6.1/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64:/usr/local/TensorRT-8.6.1/lib:$LD_LIBRARY_PATH

source ~/.bashrc

*now go to the extracted folder and install tensorrt from there
cd /usr/local/TensorRT-8.6.1/python

pip install tensorrt-8.6.1-cp310-none-linux_x86_64.whl

pip install tensorrt_dispatch-8.6.1-cp310-none-linux_x86_64.whl

pip install tensorrt_lean-8.6.1-cp310-none-linux_x86_64.whl

Hope this helps, i was using python 3.10 tensor rt folder has lib for other python versions to

thanks finally worked for me

extract and move that folder to /usr/local/TensorRT-8.6.1

tar -xzvf TensorRT-<IP_ADDRESS>.Linux.x86_64-gnu.cuda-12.0.tar.gz

sudo mv TensorRT-<IP_ADDRESS> /usr/local/TensorRT-8.6.1

code ~/.bashrc

export PATH=/usr/local/cuda-12.1/bin:/usr/local/TensorRT-8.6.1/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64:/usr/local/TensorRT-8.6.1/lib:$LD_LIBRARY_PATH

source ~/.bashrc

cd /usr/local/TensorRT-8.6.1/python

pip install tensorrt-8.6.1-cp310-none-linux_x86_64.whl

pip install tensorrt_dispatch-8.6.1-cp310-none-linux_x86_64.whl

pip install tensorrt_lean-8.6.1-cp310-none-linux_x86_64.whl

This could still have unknown issues, according to https://www.tensorflow.org/install/source version 2.16.1 supports cuda 12.3, it's not clear if this would be an error or not

Got exactly into the same problem with Python 3.11, CUDA 12.4 and TensorRT 10.1. Tried suggestion by @Zilla - it appears it's a working solution / workaround.
What I did:
export TF_CPP_MAX_VLOG_LEVEL=3
# Ran my Python program and got:
2024-06-17 21:54:19.501338: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer.so.8.6.1'; dlerror: libnvinfer.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:/usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:
2024-06-17 21:54:19.501403: I external/local_tsl/tsl/platform/default/dso_loader.cc:70] Could not load dynamic library 'libnvinfer_plugin.so.8.6.1'; dlerror: libnvinfer_plugin.so.8.6.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:/usr/local/cuda-12.4/lib64:/usr/local/lib/python3.11/dist-packages/tensorrt/lib:

Then I did:
cd /usr/local/lib/python3.11/dist-packages/tensorrt/lib
sudo ln -s libnvinfer.so.10 libnvinfer.so.8.6.1
sudo ln -s libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1

My application is not complaining about missing TensorRT library.

This hint saved me after two endless weeks of trying to load tensorrt. My environment is Docker on Ubuntu 22.04 WSL2 . Python 3.11, CUDA 12.4 and TensorRT 10.1. I was able to reproduce this solution several times. I suspect this is a bug and will be fixed in future versions. But as of now this is a circus.
A more detailed step-by-step reminder for myself (first of all) and for everyone in need:
Pre-reqiosites: installed cuda-toolkit-12-2 , nvidia-container-toolkit , nvidia-docker2 , tensorflow[and-cuda]

Install TensorRT. I did it this way(RUN is for Docker). This part can be tricky and subject to your environment. Do trial-and-error...

RUN python3 -m pip install --upgrade pip
RUN python3 -m pip install wheel
RUN python3 -m pip install --upgrade tensorrt

I still could not make it fully automated, sop I have to run the last command manually by..

logging into complied docker with
docker exec -it dockerimagename bash
Stay in bash and find where your tensorrt_libs is
find / -xdev 2>/dev/null -name "tensorrt_libs"
In my case it is
— either /usr/local/lib/python3.11/site-packages/tensorrt_libs/ , if step 1 is done manually
— or /opt/venv/lib/python3.11/site-packages/tensorrt_libs/ , if step 1 is done via Docker install script
If not found, then step 1 failed, go back and install tensorrt package.
Run (sudo) nano ~/.bashrc
Add he path and switch on logging

export TF_CPP_MAX_VLOG_LEVEL=2
export LD_LIBRARY_PATH==$LD_LIBRARY_PATH:/usr/local/lib/python3.11/site-packages/tensorrt_libs

I found level 2 sufficient for debugging, use =3 if needed.

Create symlinks (mind the proper path)

cd /usr/local/lib/python3.11/site-packages/
ln -s libnvinfer.so.10 libnvinfer.so.8.6.1
ln -s libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1

Restart docker container / reboot
Test. Launch python and type

import tensorflow
tensorflow.__version__
from tensorflow.python.compiler.tensorrt import trt_convert as trt
trt.trt_utils._pywrap_py_utils.get_linked_tensorrt_version()
trt.trt_utils._pywrap_py_utils.get_loaded_tensorrt_version()

Line 1 is expected to return the following (alongside with a bunch of useless informational messages)
2024-06-18 20:39:02.652705: I external/local_tsl/tsl/platform/default/dso_loader.cc:57] Successfully opened dynamic library libnvinfer_plugin.so.8.6.1
2024-06-18 20:39:02.652795: I external/local_tsl/tsl/platform/default/dso_loader.cc:57] Successfully opened dynamic library libnvinfer.so.8.6.1

Line 4 must return smth like (8,6,1) Line 5 must return smth like (10,0,0) – if error, then still not fixed. Go back and re-check steps.

I had the same problem for a few days.
My graphics is "RTX 4060" and I use tensorflow 2.17
Finally, after a few days of messing with this problem, I finally think I found a solution to my problem.
I'm writing it so that if someone doesn't get a result from the above solutions (which others have said), they can try my solution.
Well, first let me say that the solution is not particularly complicated and it is better to first test the solution in the comment that I replied it and if you don't get the expected result as I did, continue with my solution.
If you also get error "getInferLibVersion symbol not found" in the last line of the 6th step, or if you receive the "TF-TRT Warning: Could not find TensorRT" warning again after resetting the system, then this solution will probably solve your problem.
After installing python(3.12), TensorFlow (2.17), Cuda (12.6) and TensorRT (8.6.1), you must do steps 2 and 3 of the replied comment. (The versions mentioned are the versions tested by myself.)
Then do the 4th step with a difference:
In the second and third lines of the code, put 8 instead of 10.
Finally, after completing the previous step, you should create two more shortcuts in the place where you want to call Python (or Jupiter Notebook).
These two shortcuts are similar to the previous step, with the difference that they are created at the location of the call and point to the main folder.
First, go to the folder where Python is called (Jupiter Notebook). (for example "main" folder):
cd ~/main
Then make two shortcuts with the following command:
*You must point to the original file. (If the installation path of Python and the location of the mentioned driver are different for you, then replace your own path with the path of the code below.)
sudo ln -s ~/usr/local/lib/python3.12/site-packages/tensorrt_libs/libnvinfer.so.8 libnvinfer.so.8.6.1
sudo ln -s ~/usr/local/lib/python3.12/site-packages/tensorrt_libs/libnvinfer_plugin.so.8 libnvinfer_plugin.so.8.6.1

congratulations. My solution is complete. The only thing left is to hope that the problem is solved and just run Python (or Jupyter Notebook) and run step 6 of the replied message.
If both versions received are 8.6.1, then your problem is probably solved.
Finally, I apologize if I used inappropriate terms to explain, because I am not a professional Linux user, and I also hope that I have been able to solve someone's problem....

Identical problem here, except for whatever reason simply adding the symbolic links in python3.10/site-packages/tensorrt_libs (at both user and system level) did not solve things for me.  I ended up putting them in python3.10/site-packages/tensorflow/compiler/tf2tensorrt (pointing back to updated *.so files in tensorrt_libs), and that fixed it.

i've solved this by
ln -s libnvinfer.so.10 libnvinfer.so.8
ln -s libnvinfer_plugin.so.10 libnvinfer_plugin.so.8

Run the following in your environment's cmd/terminal:
pip install nvidia-pyindex
pip install nvidia-tensorrt
https://stackoverflow.com/questions/76028164/tensorflow-object-detection-tf-trt-warning-could-not-find-tensorrt


feat: add fzf-git as recipe

Motivation

We want to use fzf + fzf-git such that when we do git commit --fixup we can do a ctrl-gh and get a list of hashes we want to fix up, right in the terminal
(this is such a game changer!)
Changes

add fzf-git justfile recipe to source fzf-git & fzf --bash
add shellcheck directives to Justfile

Testing
just fzf-git - this nominally requires you to have fzf installed before it's meaningful
The results should be:

should modify your .bashrc (if fzf / fzf-git) are not already in bashrc
fzf-git.sh should be checked out into ~/.local/share/ubuntu-dpm/fzf-git.sh
fzf-tmux should be present in ~/.local/bin

It's possible to lint your justfile by doing this:
  #!/usr/bin/env bash
  set -eo pipefail

  JUSTFILE_JSON=$(just --dump-format json --dump --unstable)
  recipes=$(echo "$JUSTFILE_JSON" | jq -c --raw-output '.recipes | .[] | .name' | sort)
  for recipe in $recipes; do
    output=$(mktemp --tmpdir "justfile-$recipe.XXXXXX")
    just -s "$recipe" | sed -n '/#\!/,$p' > "$output"
    if [[ -s "$output" ]]; then
      shfmt -i 2 -w "$output"
      shellcheck "$output"
    fi
    rm -f $output
  done

The name of the file gives you the 'recipe it's working on'. Fails early.


Delta Lake 2.3.0 support

Fixes #9532.  Adds support for Delta Lake 2.3.0 that matches the same support level for Delta Lake 2.4.0.  Note that not everything is supported, as deletion vectors and merge's NOT MATCHED BY SOURCE clauses will cause fallbacks to the CPU. These are tracked by #8554 and #8415.

build

build


Pipeline Failure on Ubuntu 22.04 Agent Update: NoSuchBeanDefinitionException

Description
We've encountered an issue where our Spring MVC (with Maven) CI pipeline fails when running Maven@3 verify goal due to org.springframework.beans.factory.NoSuchBeanDefinitionException, without any modifications to our codebase. This error emerged following an update to the Azure DevOps hosted agent's runner image for Ubuntu 22.04, pinpointing the update as the cause.
Issue Overview:

Error: org.springframework.beans.factory.NoSuchBeanDefinitionException: No qualifying bean of type [bean name] available at [used location].
Observation: The pipeline succeeds on Ubuntu 20.04's latest image but fails on Ubuntu 22.04 following an image update.

Agent Version and Image Details:

Successful Execution: Agent Version 3.232.3, Ubuntu 22.04 Image Version 20240122.3.0.
Failed Execution: Agent Version 3.232.3, Ubuntu 22.04 Image Version 20240126.1.0.

The failure seems linked to the updated 20240126.1.0 image for Ubuntu 22.04, suggesting a potential incompatibility or missing dependencies affecting our project. We request an investigation into the changes made in this image version to identify and rectify the root cause.
We appreciate your support in ensuring the stability of our CI/CD processes. Thank you.
Platforms affected

[X] Azure DevOps
[ ] GitHub Actions - Standard Runners
[ ] GitHub Actions - Larger Runners

Runner images affected

[ ] Ubuntu 20.04
[X] Ubuntu 22.04
[ ] macOS 11
[ ] macOS 12
[ ] macOS 13
[ ] macOS 13 Arm64
[ ] macOS 14
[ ] macOS 14 Arm64
[ ] Windows Server 2019
[ ] Windows Server 2022

Image version and build link
20240126.1.0
Is it regression?
no
Expected behavior
No failure.
Actual behavior
Failing with exception.
Repro steps

Run Maven@3 task with verify

A customer also sent in a ticket experiencing issues with the latest runner version ubuntu-22.04 Version: 20240126.1.0 when running maven commands. They said the following:

In some of our projects maven unit tests are just stopped working.
The code stayed the same, no changes were made in between. The only difference what we are seeing is, that the successful run was on the image Image: ubuntu-22.04  Version: 20240122.3.0 and the failing ones are on   Image: ubuntu-22.04 Version: 20240126.1.0.

Sample error:
2024-02-02T08:14:52.8864715Z java.lang.NullPointerException: Cannot invoke "org.springframework.security.core.context.SecurityContextHolderStrategy.clearContext()" because "org.springframework.security.core.context.SecurityContextHolder.strategy" is null

Could the team kindly take a look as soon as possible because this is blocking since they can't pin runners to previous versions? Thanks.

My spring boot application hangs before SpringSecurity initialization with Ubuntu 22.04 (20240126), it was working fine until Ubuntu 22.04 (20240116). I can't understand what's going on everything looks the same. Even the jar files are identical. But latest builts won't start! I changed runner to 20.04, it works for now.

Hello, please provide a minimal repro-steps so we could reproduce the issue

I can't reproduce the issue on a public repo. it's a jar file problem. jar file that built by gradle is corrupted somehow.
this command below fails
java -jar app.jar
after extracting app.jar file contents into disk and then directly running java class as below works fine
java org.springframework.boot.loader.launch.JarLauncher
any java expert has any idea?

Hello everyone!
@author4all, thank you for reporting the issue!
Unfortunately, we could not reproduce the issue from our side. For further investigation, it would be helpful if you could provide us with a link to the public repository or create a simple test project where the issue occurs.
As a workaround please use ubuntu-20.04 image

ado version has been updated

Hi, was this issue actually related to azcopy? We are performing ubuntu 22.04 builds right now and are concerned that this will be an issue for our customers who use Maven.

@angaaruriakhil oh, ignore me, I closed the wrong ticket :/

Could someone please test it on a new version of the image?

@mikhailkoliada We have tested this with the newest image and it works. Can you say what is changed to solve this issue?

@akbast little to no idea, I mean, it has probably been caused by some deep dependency we do not track explicitly in the readme files, but track of this dependency does not look prominent now as we have no full minimal repro steps and are not aware in which direction to dig. I'd consider it a luck given that it got self-healed but if it is going to repeat itself we will have to have some repro to start looking under the hood, I guess it can be closed for now.

Could someone please test it on a new version of the image?

I tested too, the issue is gone. my jar running without any problems. thanks for the fix


Extension pre-release vs stable-release is confusing to me

In my day to day use and esp. now that I am doing testing, I find how we distinguish pre-release from stable-release extensions confusing.
To clarify: we show a badge to the left when an extension can be installed as pre-release and a badge to the right when it is pre-release installed:

In my mental model - given VSCode and insiders - I map a blue logo to stable and a green logo to insiders. Maybe we can use something similar for extensions as well?
//cc @isidorn

This is good feedback
When you compare it like that side by side I think the top left green thing deserves to be in both cases.
And maybe in the first case it is blue.

Actually why do I need this badge on the left at all when the button strongly indicates I can install pre-release?

Here is my suggestion:

remove the left hand badge
remove the right hand badge
clearly spell out "Pre-release" as label next to the blue mark

🚀

@bpasero overall I like your suggestion since it simplifies things and the "Install Pre-Release" button is there by default. However pre-release next to the author name feels like it does not belong there.

Right, my point I guess is to have it spelled out somewhere and not convey the info just through a badge or icon.

Actually why do I need this badge on the left at all when the button strongly indicates I can install pre-release?

This is true for not installed extensions. But what about installed extensions? How to indicate that an extension has pre-release or not?

@bpasero look out for extra confusion if you ever need to install pre-release extensions direct from VSIX (e.g. if you need to use proposed APIs in them).
See #141869 and the discussion in the issue it was spun off from.

have we seen similar feedback, overall, from other users?

Not yet.
Badge on the left is to indicate that the extension has a pre-release version, otherwise stable user does not know unless user clicks on the Install dropdown. If extension is already installed, user has to open extension editor or context menu to know there is a pre-release.

Show the same button to switch to pre-release?

This adds more noise to the UI. This button is more annoying to me if I opted to install release version.

I find how we distinguish pre-release from stable-release extensions confusing.

Indicator on the right is meant to distinguish if an extension is running pre-release or not.
Summarising the states we represent:

VS Code Stable: Extension has Pre-release

VS Code Insider: Extension has Pre-release

Extension has Pre-release but release version is installed

Extension has Pre-release and pre-release is installed

Badge on the left indicates that the extension has a pre-release
Indicator on the right indicates that extension is running pre-release

How about having badge on the left always - indicating that this extension has pre-release, even though the pre-release is installed?

I was not even aware that we show different buttons depending on being in Code stable vs insiders.
I like the idea of reducing this to 1 badge that is always at the same location, but then we are at risk imho of subtle differences, because if the badge shows it can mean both that pre-release is installed as well as that pre-release is available.
As such, why not just:

drop the badge and purely rely on the install button for picking pre-release
show the badge exclusively for when you have pre-release installed

It seems, I am not clear - I do not want to reduce or remove any. I am recommending to have the badge on the left always for an extension that has pre-release and the indicator on the right is shown only when pre-release version is installed. This was how it was before.

drop the badge and purely rely on the install button for picking pre-release

This might be unintuitive because user has to click on the dropdown to see if the extension has a pre-release or not.

show the badge exclusively for when you have pre-release installed

The indicator on the right is meant for this purpose.

Fair enough, I think that is worth a try 👍

I wish these badges showed a brief explanatory tooltip when hovered over.

@gjsjohnmurray there is no tooltip per html element in the extensions view, only one big tooltip for the whole tree element.
I agree with the proposed change:

to have the badge on the left always for an extension that has pre-release and the indicator on the right is shown only when pre-release version is installed

IMHO indicator on the might be too opaque. It either needs to be stronger or to be changed with text if possible.

Yes, on the single tooltip for the entire element we show whether the prerelease version is installed or available.
I agree the icon itself on the right is a bit subtle especially as I'm scrolling through my list of installed extensions. There have been past explorations of including text with the icon. Maybe we could show icon + 'pre-release' on the right side when the sidebar is wide enough to fit it, and as the view gets narrower we only show the icon.

So this is actually a nice badge:

Yeah but in the Extension Details View we have more space. I am not sure if that would fit in the viewlet. We could be smart - when there is not enough space “Pre-Release“ text gets hidden and only the icon is left. We might be able to do that using flex.

Even if it fits in the viewlet, I would think this adds more noise in the UI.

I think the root problem here is that the badge on the left is confused between whether extension is a pre-release version or extension has a pre-release version. I have tried following to make it clear:

I have removed the indicator on the right
Badge on the left indicates that this is a pre-release version
If a release extension is installed and it has a pre-release version, added an icon action before gear icon for switching to pre-release version

Feedback?

@sandy081 can you show an example with all of the various states?

VS Code Stable: Extension has Pre-release

VS Code Insider: Extension has Pre-release

Extension has Pre-release and pre-release is installed

Extension has Pre-release but release version is installed

@isidorn @lychung7 @misolori @bpasero let me know what you think about above changes?

@sandy081 great work. I like this changes and they make sense to me.
Once they are in insiders I can try them out and provide more feedback.


SEG Fault in image acquisition

I have adapted the Linux-tiscamera-Programming-Samples/simple-snap-image-in-c++ program for image acquistion with qt. However, during some acquisitions, I get a segmentation fault randomly, whereas during other times the program works correctly. Following is my adaptation:
int acquireImage(std::string camName, Ui::MainWindow *ui)
{
    bool fail = true;
    cv::Mat OpenCVImage;
    /* Initialize our TcamCamera object "cam" with the serial number
     * of the camera, which is to be used in this program.
     */
    //TcamImage cam("42719953");
    //TcamCamera cam("00001234");
    TcamImage cam(camName);

    // Set a color video format, resolution and frame rate
    cam.set_capture_format("GRAY8", FrameSize{960, 600}, FrameRate{30,1});

    // Comment following line, if no live video display is wanted.
    //cam.enable_video_display(gst_element_factory_make("ximagesink", NULL));

    // Start the camera
    cam.start();

    std::this_thread::sleep_for(std::chrono::milliseconds(2000)); //2 second margin for camera to attain optimum exposure

    for(auto i = 0; i < init.num_of_images; ++i)
    {
        QString writeout = "Acquiring Image " + QString::number(i + 1) + " of " + QString::number(init.num_of_images) ;
        ui->textBrowser->append(writeout);
        ui->progressBar->setValue( i * 3 );
        QApplication::processEvents();

        // Snap an Image with 60 ms timeout. (DOUBLE THE FRAME RATE) Should be set accordingly to frame rate.
        if( cam.snapImage(500) )
        {
            // On succes do something with the image data. Here we create
            // a cv::Mat and save the image
            OpenCVImage.create( cam.getHeight(),cam.getWidth(),CV_8UC(cam.getBytesPerPixel()));
            memcpy( OpenCVImage.data, cam.getImageData(), cam.getImageDataSize());

            //Perform median smoothing with 7x7 kernel
            cv::medianBlur(OpenCVImage, OpenCVImage, 7);

            //Save Image
            std::string fname = "Images/" + std::to_string(i + 1) + ".bmp";
            cv::imwrite(fname, OpenCVImage);

            if(i == 4)
            {
                //Final state reached no more motor rotation needed
                fail = false;
                break;
            }

            //Rotate by 45 degrees
            SerialDrive(init.cmd);
            std::this_thread::sleep_for(std::chrono::milliseconds(init.milliseconds)); //9 seconds required by controller to rotate motor by 45 degrees
        }
        else
        {
            printf("Timeout at snapImage()\n");
            break;
        }
    }

    cam.stop();
    OpenCVImage.release();

    if(fail)
    {
        return -1;
    }
    return 0;
}

On printing the stack trace I have been able to isolate the issue with cam.stop() call. Since I am quite new to C++ programming, so my diagnosis may be incorrect.
Also, I tried to keep the camera open during full processing cycle (i.e. camera remains open during acquisition as well as processing). However, that leads to seg faults every time during processing.
Kindly help me to fix the issue.
Edit: I am attaching a new core dump file (along with executable) that I was able to generate
seg-fault.zip

Hi,
Thank you for your report.
Did you configure tiscamera with the cmake option -DCMAKE_BUILD_TYPE=Debug?
The segfault does not contain useful information due to missing debug symbols.
Could you either send me the entire code of the project or unset the camName?
From what I can gather you have hardcoded the camera serial.
That prevents me from reproducing the segfault.
You can send the code to<EMAIL_ADDRESS>if you do not want to publicly disclose it.
Furthermore:
Could you give me the output of your application with the following environment variables set.
export TCAM_LOG=DEBUG
export GST_DEBUG=tcamsrc:5
Fizeau

This enables gstreamer logging and tiscamera internal logging to the console giving us an insight as to what is happening.
From the looks of it the camera is not opened and thus crashes later on.
Which still should not happen but one step at a time.
-Edgar

Hello @TIS-Edgar,
Thanks for your reply,

Could you either send me the entire code of the project

I am attaching the full source code.
FizeauCPP.zip
I have added a README_INSTALL file for creating the correct folder structure for the binary.

Could you give me the output of your application with the following environment variables set.

I am attaching output from gstreamer and tiscamera internal logging.
log.txt. On examing the log I am finding an error statement which states:
1548492    <ERROR> /home/bvtest/tiscamera/src/v4l2/V4l2Device.cpp:104: Error closing udev monitoring pipe. write return '18446744073709551615'

Did you configure tiscamera with the cmake option -DCMAKE_BUILD_TYPE=Debug?

I am using the production release version (.deb) of tiscamera (0.11.1) i.e. the one on this link https://www.theimagingsource.com/support/downloads-for-linux/install/tiscamera/. So I have not build the library. However, my Qt project was configured in release mode. I have switched back to debug mode.

From what I can gather you have hardcoded the camera serial.

The camera serial number is a user supplied value, read from a Qt interface variable.

From the looks of it the camera is not opened and thus crashes later on.

The camera gets opened and acquires images. The seg fault occurs only after acquiring all images. Also, the seg fault is random. Generally it occurs when the acquisition------>processing cycle has been completed a few times.
Side Note: There is a bug in tcam-capture utility in version 0.11.1. On trying to invoke it, the error is:
 Traceback (most recent call last): File "/usr/bin/tcam-capture", line 28, in <module> from tcam_capture.TcamDevice import TcamDevice, TcamDeviceIndex ModuleNotFoundError: No module named 'tcam_capture'
This error was not present in the previous release. If the older .deb installer (version 0.10) is used, tcam-capture starts correctly.

I have no idea why your program is triggering this problem and not any of our tests/applications.
You seem to be running in a timing issue with the destruction of buffers after the tcamsrc is already destroyed.
Can you install this version for me:
tiscamera_0.11.1_amd64.deb.zip
It contains a fix for the segfault I was able to identify.
I have not yet had the time to look into the tcam-capture installation issue you are experiencing. That will be next.

I shall try out the version this week at the optical workshop where the software is currently deployed.
Regarding the tcam-capture issue, I noticed that the underlying tcam_capture is installed at location
/usr/lib/python3.5/site-packages/tcam_capture/ . However the default python3 in ubuntu 18.04 is python3.6 and thus python3.5/site-packages is not in the path.
I have Ubuntu 18.04.3 at both my development workstation and deployment site.

@TIS-Edgar
I am trying to disable auto exposure and set the value manually. I have tried following code:
    GValue set_auto = G_VALUE_INIT;
    g_value_init(&set_auto, G_TYPE_BOOLEAN);
    g_value_set_boolean(&set_auto, FALSE);
    sarthcam.set_property("Exposure Auto", set_auto);
    sarthcam.set_property("Gain Auto", set_auto);

    GValue val = G_VALUE_INIT;
    g_value_init(&val, G_TYPE_INT);
    g_value_set_int(&val, 15);
    sarthcam.set_property("Exposure", val);

However, it is having no effect at all. Also I tried to set the Exposure to manually set value using tcam-ctrl by issuing following command:
tcam-ctrl -p -s "Exposure=15" 33810623

The command results in following:
No property with name 'Exposure'
Our camera is DMK23UX236.
Side Note: I am attaching a preliminary draft of a 2 page document where I have compared results from DMK23UX236 and Basler ace1300-30gm. In the table on first page you can see that DMK23UX236 has some wild swings in the measured value. I request you to go through the document and suggest me any acquisition parameter that I can control so as to improve the result.
SG-OSTI-v2.pdf

Instead of "Exposure", you may try "Exposure in (us)". You can use tcam-capture for listing property names.

@TIS-Edgar
Got it. The property is Exposure Time (us). Just tried it in code, it is working and visible in tcam-ctrl also. I shall try it in the shop floor and report back about results.
As discussed above, tcam-capture is currently not working for us.

@TIS-Edgar @TIS-Stefan
I have deployed version 0.12 on production systems at our site. This version solves all of the issues viz. the seg fault issue as well as the tcam-capture startup issue. The development and production system are running Debian 10 amd64. This issue can be closed.


Unable to access CRC Cluster post new user addition

I am running CRC-Windows-1.24.0  on Windows 10.
CodeReady Containers version: 1.24.0+5f06e84b
OpenShift version: 4.7.2 (embedded in executable)
I tried adding new user via the OC command line using the following link.
https://github.com/code-ready/crc/wiki/Add-another-user-to-cluster
Post the execution of this command , I am unable to login either using developer or kubeadmin on the browser.

@krish-sriram In the wiki it is mentioned that it replace existing secret when using oc replace -f so you can now only login with your added user not the existing kubeadmin and developer one. Will try to update it to have some kind of append functionality so existing user always present.

I am unable to access via new user as well.

@krish-sriram We will try those steps again ourself and get back to you.

@praveenkumar  I got logged out of cluster,  I will have to delete and recreate the cluster to try this out. I will try post the result here.

@praveenkumar I got logged out of cluster, I will have to delete and recreate the cluster to try this out. I will try post the result here.

@krish-sriram Sure, and please close this issue it that worked for you.

Closing this since there is no response from reporter now.


Properties evaluated before Condition element

I am trying to use the condition element on several related resources, and one of those kind of depends on the other one, through listKeys function. As both are under same condition criteria, I would expect that such ARM template works, but it fails because it seems that "properties" (and "value" block) are evaluated before, or not related to, condition element.
Example would be:
      "type": "Microsoft.KeyVault/vaults/secrets",
      "condition": "[not(parameters('ShouldSkip'))]",
      "properties": {
        "value": "[listkeys(resourceId('Microsoft.EventHub/namespaces/eventhubs/authorizationRules', variables('namespaceName'), variables('eventHubName'), variables('sendRuleName')), variables('eventHubApiVersion')).primaryConnectionString]"
      },
      "dependsOn": []

The Event Hub resource used in value element is under same condition, hence not deployed.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: ada6b9c1-42f6-5012-9201-04fd463efdfd
Version Independent ID: 16abf6b9-c81a-a6f7-d0fd-b22ef3a4a744
Content: Conditionally deploy a resource in an Azure Resource Manager template
Content Source: docs/building-blocks/extending-templates/conditional-deploy.md
Service: guidance
GitHub Login: @petertay
Microsoft Alias: pnp

Btw, I am not sure if this is the right project for this issue, would fit here better: https://github.com/Azure/azure-resource-manager-schemas/issues ?


App is crashing on Launch Screen

When I'm testing my app on develop mode, everything is ok, but when I'm sending a test flight version of my app to the app store my app is crashing at Launch Screen and I have to open the app from recent list of iPhone.
I really don't know what is my problem and how can I fix that.

Not sure, but I would guess you have to bundle your app. See http://facebook.github.io/react-native/releases/0.19/docs/running-on-device-ios.html

Yeah, you may want to use some crash reporting system to get the error, but it may be that you haven't packaged it correctly. I'm closing this for now because you haven't provided enough information for anybody to reproduce the issue.


Static typing and sizing

Currently Forscape does all type and dimension checking at runtime. We would like to transfer this responsibility to compile time, not only for performance, but also for semantic formatting. We can highlight errors prior to running the interpreter, and even bold matrix identifiers.
There are challenges to consider:

Generic functions
Symbolic dimensions, e.g. an m×n matrix
Aggregate types, e.g. lists of X, sets of X, etc.
Nonhomogeneous aggregate types
Subtypes, e.g. int is a subtype of rational is a subtype of real
Inference
Trying something too difficult and failing

Probably use "call-site template function instantiation", or whatever it's really called. I'm having trouble finding educational material.

With call-site instantiation of generic functions:

The function declaration doesn't give you much, just the number of parameters, number of default args, and their types
A function plus a list of arg types lets you deduce the return type
The function to call is the result of an expression, so you need to be sure it is instantiated, and you need to deduce the return type without being able to statically determine the function to call
Can you return a generic function, then deduce its type at call sites? You need the body to deduce the type. When the called function could evaluate to different options, you need all the options.

I halfway think it can work, but it's doing my head in. Maybe I should sleep.

Actually, I think I'm biting off much more than I can chew with generic functions. I don't need to get bogged down on tangents, just make a restriction that the function declaration includes a fully specified signature, and make a note to relax that restriction later.

Well declare-time deduction is incredibly limited. It has challenges with recursion and functions passed as arguments. The closure examples are a little contrived, but the isolated function definitions have ambiguous signatures. The root finding example fails because for f(x), f could be a function ℝ → ℝ or just a member of ℝ resulting in implicit multiplication. To have any chance of success, you'll need to consider the call site. But then you're statically tying identifiers to functions, which is not cool.

But static typing is the gateway to most of the interesting features, and it should be done non-intrusively. Get it together!

Well this just won't happen quickly. First things first, I need another Very Sophisticated Vector™ to represent types. I expect it will be similar to the "tree" in KiCAS. Then with that support in place, a lot of experimentation to find what works. Probably not a weekend project...

Perhaps a call graph would be helpful?

The problem with any type deduction involving the call site is that we cannot generally determine which function will be called. Take for example:
alg add(x, y) return x + y
alg mult(x, y) return x * y
f = addfalsemulttrue
print(f(3, 3))
However, I believe we can determine all the functions which could possibly be called, and instantiate them with the correct types.

Static type checking basically works. It still lacks some tedious details around references captures, function prototypes, and recursion. It needs to clone functions rather than just use the original definition (vital for recursion) while keeping valid references (can think about frame offsets in the symbol table). Meaningful error messages will require some book-keeping to report at the declare and/or call sites. The whole thing could use a refactor, especially before pressing on to static dimensioning and autodiff. But it works 😭.

I almost have test coverage, but I worry that "cursed_factorial" will expose my recursion strategy as too naive. I had thought that if you hit recursion, you resolve as much as possible in your pass, then take an additional pass to either finish or conclude you have a cycle without an exit. It may not be the simple, but I still think it's feasible. This is a nice puzzle.

It seems to work now. Hopefully the recursion handling doesn't admit invalid Forscape programs. The IDE crashes constantly now, and the typing error messages are terrible, but that's cleanup work.

This is set to close with https://github.com/JohnDTill/Forscape/pull/38. There is still more to explore with instantiation as opposed to just checking, and static dimensioning, but the scope of this issue has been large enough!


Add Clock demo

I worked to fine-tune this example a little bit after I switched to using s. I still think it would be cleaner to have a real anchor point/pivot thing supported in Revery (right now I'm doing some weird math and it's still probably not perfect), so I'm gonna try to design an API for that (probably will submit that PR in a few days).

Oh, didn't realize you already have it written lol


Why close the issue #3423? The problem is obvious in the image i appended in 3423.

Why close the issue #3423? The problem is obvious in the image i appended in 3423.
Could you give some reason for closing #3423?
https://github.com/framework7io/framework7/issues/3423

Your issue is closed because it is not filled correctly. Before you open an issue please review the contributing guideline.
Please fill the issue template correctly and:

Clearly describe the issue including steps to reproduce when it is a bug.
Make sure you fill in the earliest version that you know has the issue.
Provide live link or JSFiddle/Codepen or website with isssue

It will save a lot of time for other contributors to check your issue and fix it.

I describe the problem clearly (both version and link) in issue #3423 and appended a picture, but you give nothing feedback expect closing.


fix(theme-builder): theme builder should include core theme. (#26)

Hi,
I have fixed the bug, hope you like it.
// Rasmus

Good idea. @RasmusTG Thanks for the PR. Looks perfect.

@DevVersion why is this not released?


Fixes semantics_perf_test.dart after profile fixes

I renamed the semantics events label.

Landing on red before tests passed in order to fix tree redness. If this is not clean on post-submit, we'll revert this and https://github.com/flutter/flutter/pull/93086.