Help with Jupyter+GPU (noob)

Dear community,

I am really sorry for coming here with common problem. I spend monthes to find correct configuration to connect my notebuuk to laptop NVIDIA. I tried to use suggestion that worked for other people, but it is still not working for me (I am really bad in python configurations). Please, help me =)

Following reccomendations and notebook requirenments I manage to recognize my GPU in notebook, but when I start model training using GPU in Jupyter notebook, kernel duy emidiatly. I belive I have poor compatability between CUDU, CuDNN and TF.

Here is what I am have installed:
cudatoolkit : 11.8.0

tensorflow==2.4.0 and tensorflow-gpu==2.4.0

NVIDIA-SMI 535.98 Driver Version: 535.98 CUDA Version: 12.2

(base) C:>nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:12:52_Pacific_Daylight_Time_2019
Cuda compilation tools, release 10.1, V10.1.243

I will be thankfull for any help


Welcome to the Tensorflow Forum,

As per tested build configurations, you can try with CUDA 11.0 and cuDNN 8.0.

Thank you!

I also am currently having difficulty with Jupyter+GPU.

tf.config.list_physical_devices(‘GPU’) will list the GPU if it is running in a simple python terminal session or in a PyCharm IDE, but produces [ ] (i.e. empty list) when run inside a Jupyter notebook. Note, tf.test.is_built_with_cuda() produces “True” in all 3 scenarios.

I have a conda environment under Windows and I am using versions exactly as per (pip로 TensorFlow 설치) for Windows, i.e. cudatoolkit=11.2 and cudnn=8.1.0 installed using conda, and tensorflow=2.10.1 installed using pip.

Does Jupyter need special configuration to enable use of GPU?


Welcome to the Tensorflow Forum,

Could you please let us know the steps that you have followed to install Tensorflow on Windows?

Thank you!

Thanks @chunduriv,

I believe I have located my error. I had not setup the ipykernel correctly in the conda environment. I could not figure out how to setup the kernel spec manually, so I have used Option 3. of which suggests adding nb_conda_kernels to the base environment and ipykernel to each conda environment. This automatically create the correct jupyter kernelspec for each conda environment.

Having done this, my installation of cudatoolkit=11.2 cudnn=8.1.0 and tensorflow=2.10.1, as per the tensorflow installation page, works perfectly in my jupyter notebook and uses the GPU as expected.

Thanks for your offer of help,


I just managed to connect notebook to GPU. Following suggestion found on forum I created in my working directory new folder and subfolder .\nvvm\libdevice and copied inside files (libdevice.10.bc and nvvm64_40_0.dll) that I found in C:\Users\username\AppData\Local\anaconda3\pkgs\cudatoolkit-11.8.0-hd77b12b_0\DLLs

this instruction was found here Can't find libdevice directory ${CUDA_DIR}/nvvm/libdevice · Issue #56927 · tensorflow/tensorflow · GitHub