tensorflow: Fail to build TF 1.15 on Cuda 11.1

System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows
TensorFlow installed from (source or binary): source
TensorFlow version: 1.15
Python version: 3.6
Installed using virtualenv? pip? conda?: pip
Bazel version (if compiling from source): 0.25.3
GCC/Compiler version (if compiling from source): MSVC 2017
CUDA/cuDNN version: 11.1 / 7.6.0
GPU model and memory: RTX 2080 TI

Describe the problem

unable to build TF 1.15 on Cuda 11.1

Any other info / logs

Execution platform: @bazel_tools//platforms:host_platform
tensorflow/core/kernels/cuda_sparse.cc(212): error C2065: 'cusparseSgtsv': undec
lared identifier
tensorflow/core/kernels/cuda_sparse.cc(212): error C2065: 'cusparseDgtsv': undec
lared identifier
tensorflow/core/kernels/cuda_sparse.cc(212): error C2065: 'cusparseCgtsv': undec
lared identifier
tensorflow/core/kernels/cuda_sparse.cc(212): error C2065: 'cusparseZgtsv': undec
lared identifier
tensorflow/core/kernels/cuda_sparse.cc(224): error C2065: 'cusparseSgtsv_nopivot
': undeclared identifier
tensorflow/core/kernels/cuda_sparse.cc(224): error C2065: 'cusparseDgtsv_nopivot
': undeclared identifier
tensorflow/core/kernels/cuda_sparse.cc(224): error C2065: 'cusparseCgtsv_nopivot
': undeclared identifier
tensorflow/core/kernels/cuda_sparse.cc(224): error C2065: 'cusparseZgtsv_nopivot
': undeclared identifier
tensorflow/core/kernels/cuda_sparse.cc(250): error C2065: 'cusparseSgtsvStridedB
atch': undeclared identifier
tensorflow/core/kernels/cuda_sparse.cc(250): error C2065: 'cusparseDgtsvStridedB
atch': undeclared identifier
tensorflow/core/kernels/cuda_sparse.cc(250): error C2065: 'cusparseCgtsvStridedB
atch': undeclared identifier
tensorflow/core/kernels/cuda_sparse.cc(250): error C2065: 'cusparseZgtsvStridedB
atch': undeclared identifier

About this issue

Original URL
State: closed
Created 4 years ago
Comments: 22 (7 by maintainers)

Most upvoted comments

My big app is created using TF 1

I cannot upgrade it to TF 2 API, because it requires a lot of modifications and testings from scratch, which is time and money consuming task.

Seems like Tensorflow™ cannot provide backward compatibility for new CUDA versions. So even 2 years old app will not support new cards. It is serious impact to business and companies which are using TF. Where is the guarantee that it will not happen again?

I am already very sorry that I did not choose pytorch at first. Burn in hell google, die tensorflow.

iperov on Sep 28, 2020

To use tensorflow 1.1x on CUDA11.x, I think you should use nvidia-tensorflow. The installation is quite simple. (https://developer.nvidia.com/blog/accelerating-tensorflow-on-a100-gpus/)

muskie82 on Jan 7, 2021

Install NVIDIA drivers(455.23). After installing it check the status of GPU using nvidia-smi. Then install tf-1.15 as follows:

sudo apt update
sudo apt install -y python3-dev python3-pip git
pip3 install --upgrade pip setuptools requests

pip install -U virtualenv
virtualenv --system-site-packages -p python3 /venv
source /venv/bin/activate

pip install nvidia-pyindex
pip install nvidia-tensorflow[horovod]

This should install tf-1.15 with cuda 11.1 support. Test it as follows: python -c 'import tensorflow as tf; print(tf.__version__)'

python -c "import tensorflow as tf; print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))"

anshkumar on Dec 21, 2020

@iperov why do you think so ? This worked for me. See (https://developer.nvidia.com/blog/accelerating-tensorflow-on-a100-gpus/)

anshkumar on Dec 21, 2020

I think you should use nvidia-tensorflow. (https://developer.nvidia.com/blog/accelerating-tensorflow-on-a100-gpus/)

Any possible ways to compile working libtensorflow.dll with it? I installed cuda 10.0 to system with 3070 and tensorflow.dll 1.15 that i’m currently using doesn’t give me any errors, but initialization and inference are extremely slow.

VladislavAD on Dec 8, 2020