Fix not load dynamic library for Tensorflow GPU

Question

I want to use my GPU for Tensorflow.

I tried this Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation

Unfortunately, I keep getting an error Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found. How can I fix this? Python-version: 3.8.3, CUDA 10.1

2020-11-03 12:30:28.832014: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2020-11-03 12:30:28.832688: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found
2020-11-03 12:30:28.833342: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found
2020-11-03 12:30:28.833994: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found
2020-11-03 12:30:28.834645: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found
2020-11-03 12:30:28.835297: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
2020-11-03 12:30:28.835948: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found
2020-11-03 12:30:28.836594: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found
2020-11-03 12:30:28.836789: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1761] 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...
2020-11-03 12:30:28.837575: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-11-03 12:30:28.838495: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1265] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-11-03 12:30:28.838708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1271]      
2020-11-03 12:30:28.838831: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set

Are you planning to use a GPU at all? If not, you can ignore those and use TF in CPU-only mode. — AKX, Nov 03 '20 at 11:37
@AKX Thank you for your comment. Forgot to say I want to use the GPU. — , Nov 03 '20 at 11:38
Then have you followed the instructions in the link in the exception? https://www.tensorflow.org/install/gpu — AKX, Nov 03 '20 at 11:40

CypherX · Answer 1 · 2021-12-31T02:18:16.793

Solution

I would suggest you to use conda (Ananconda/Miniconda) to create a separate environment and install tensorflow-gpu, cudnn and cudatoolkit. Miniconda has a much smaller footprint than Anaconda. I would suggest you to install Miniconda if you do not have conda already.

Quick Installation

# Quick and dirty: with channel specification
conda create -n tf_gpu_env python=3.8 
conda activate tf_gpu_env
conda install tensorflow-gpu -c anaconda
conda install cudnn -c conda-forge 
conda install cudatoolkit -c anaconda

Check that Tensorflow is using the GPU

For more details see this: https://www.tensorflow.org/guide/gpu

# Sanity check for validating 
# visibility of the GPUs to TF
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

Easily Reproducible Installation with Environment File: `environment.yml`

Although you can quickly create a conda (Ananconda/Miniconda) environment as shown earlier, it is much more desirable to make the installation process as reproducible as possible: enters the environment.yml file.

Save the environment file at the root of your repository (or project) and run the following command to install all the packages in an isolated conda environment (here I have named it tfgpu_env) using the environment file. The top part of the environment file contains some useful commands.

conda env create -f environment.yml

Save the following as environment.yml under your repository. And consider pinning the following three libraries:

An example:

tensorflow-gpu=2.4
cudnn=8
cudatoolkit=11

## filename: environment.yml

## Environment File Definition

name: tfgpu_env # tensorflow-gpu environment
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - python=3.8
  ## Core Necessities
  - numpy # -c conda-forge, anaconda
  - pandas # -c conda-forge, anaconda
  - tabulate # -c conda-forge, anaconda  # necessary for df.to_markdown() in pandas
  - scipy # -c conda-forge, anaconda
  - matplotlib # -c conda-forge, anaconda
  ## Jupyter Related
  - jupyter # -c anaconda, conda-forge
  - jupyterlab # -c anaconda, conda-forge
  - jupyter_dashboards # -c conda-forge  (see: https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e)
  - jupyter_contrib_nbextensions # -c conda-forge
  ## Progressbar
  - tqdm # -c conda-forge, anaconda
  ## Machine Learning
  - tensorflow-gpu # -c anaconda | version: 2.4.1 (linux), 2.3.0 (windows)
  # - tensorflow # -c anaconda | version: 2.2.0 (linux), 2.1.0 (windows)
  - cudnn # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #       # -c anaconda | version: 7.6.5 (linux/windows)
  - cudatoolkit # -c conda-forge | version: 8.1.0.77 (linux/windows)
  #             # -c anaconda | version: 11.0.221 (linux/windows)
  - scikit-learn # -c conda-forge, anaconda
  ## Hyperparameter Optimization
  - optuna # -c conda-forge # works for pytorch, tf/keras, mxnet, scikit-learn, xgboost, lightgbm
  - keras-tuner # -c conda-forge
  ## Image Processing
  - opencv # -c conda-forge, anaconda
  - imageio # -c anaconda, conda-forge
  ## Image Augmentation
  - albumentations # -c conda-forge
  - imgaug # -c conda-forge
  ## Code Linting
  - pylint # -c conda-forge, anaconda
  - autopep8 # -c conda-forge, anaconda
  ## Installations with pip
  - pip:
    ## Web App Framework
    # - Flask-Testing
    - streamlit # https://docs.streamlit.io/en/stable/troubleshooting/clean-install.html

Useful Instructions

You might as well copy and paste the following instructions in the environment file itself, to keep them handy.

# Instruction:
#-----------------------------------------------------------
#
## For an environment installed locally (under: ./.venv)
# mkdir -p .venv && cd .venv
# conda env create --prefix . -f ../environment.yml
## For Updating local environment
# cd .venv
# conda env update --prefix . -f ../environment.yml  --prune
#
## For an environment installed globally
## with a name: fav_env 
# NOTE: The env-name is stored inside the 
#       environment.yml file.
# conda env create -f environment.yml
## For Updating global environment from env-file
# conda env update -f ./environment.yml  --prune
#
## Update conda itself
# conda update -n base -c defaults conda
#
## Creating a global environment in one-line: py37, py38
# conda create -n py37 python=3.7
# conda create -n py38 python=3.8
#
### In each of the envs: base, py37, py38
# conda install jupyter jupyterlab numpy scipy pandas matplotlib scikit-learn scikit-image tqdm plotly imageio requests pylint autopep8 tabulate opencv
#
## Export a platform independent copy of an environment
#  conda env export --from-history > path/to/environment.yml
### Make exports directory (if not present already) and export
# $targetDir = conda_exports
# mkdir ./$targetDir
# conda env export --from-history > ./$targetDir/exported_environment.yml

References

I took the advice and it worked. I updated my question because after doing these steps, it seems like my GPU still isn't being used. — Redgar Pro, Mar 10 '21 at 13:45
You need to ensure that tf is using the GPU first: https://www.tensorflow.org/guide/gpu — CypherX, Mar 10 '21 at 14:42
Since the original problem that you had posted is resolved, I would suggest you to open a new question (if necessary) or look up available answers. Also, please consider **`accepting`** and **`voting-up`** this answer as it resolved the original issue of DLL error. Although, I have shared the link to the TF documentation on how to make TF use your GPUs. Keeping the scope of the questions brief helps people to look them up in future. — CypherX, Mar 10 '21 at 14:55
Been struggling all day installing a bunch of things to get it working. Yet the solution is so easy, thanks so much! — Kenji Bailly, Oct 04 '21 at 00:48
@KenjiBailly I'm glad you found this helpful. Ironically, when I got stuck first time with Anaconda, I had to spend pretty much close to a week to get everything sorted. It's only after this huge wastage of time that I decided to document it well. Now I maintain a git repository to version control all my conda environments. It saves a lot of time in the long run. — CypherX, Oct 04 '21 at 00:54

score 3 · Accepted Answer · answered Nov 03 '20 at 12:43

3

Well, you can see that your Tensorflow installation is looking for Cuda libraries of version 11, 10, while you have 10.1. So in order to fix this, install the proper Cuda version. Why is it looking for 3 different versions, I have no idea. But you can find valid combinations of Cuda, Tensorflow, and CUDNN here.

EDIT: Removed 8 from the Cuda version, Tensorflow is actually looking for CUDNN version 8. So don't forget to install CUDNN as well (my guess is that you are installing the latest version of Tensorflow -> that's why is it looking for the latest Cuda and CUDNN releases.)

answered Nov 03 '20 at 12:43

Vojtech Molek

108
1
8

Thank you very much! First I downloaded `Cuda 11` then I downloaded `Cuda 10.1` with `CUDNN v8.0.4` . `CUDNN` is installed by simply unzipping it and dragging all data from the unzipped bin folder into the `Cuda` bin folder? And the other folders too. – Nov 03 '20 at 13:14
1

Yes, this is snipplet from [NVIDIA CUDNN installation guide](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html): `$ sudo cp cuda/include/cudnn*.h /usr/local/cuda/include $ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 $ sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*` You can see that you copy the content of the folder into the Cuda folder which matches the naming scheme. – Vojtech Molek Nov 03 '20 at 13:22
2

Thank you very much! I installed everything agian. With `Tensorflow 2.3.1` , `CUDNN 7.6` and `Cuda 10.1` and still there is an Error upcoming.. :/ – Nov 03 '20 at 13:32
1

Could you try Tensorflow 2.3.0? That's valid combinations according to the link I posted earlier. – Vojtech Molek Nov 03 '20 at 16:29

score 0 · Answer 3 · answered May 16 '21 at 22:52

The .dll file was not installed when I installed Cuda. But I had installed pytorch and that package included the dll file . so I added that to the path variable and next time when I ran tensorflow, it could find the dll (from my recent path addition) and it worked.

dll path in pytorch lib

C:\Users\YOUR_USER_NAME\AppData\Roaming\Python\Python38\site-packages\torch\lib

Confirmation

print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
Num GPUs Available:  1

Previously it was 0

score 0 · Answer 4 · answered Jan 22 '23 at 02:12

Taken directly from Tensorflow's website: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin

Note: TensorFlow with GPU access is supported for WSL2 on Windows 10 19044 or higher. This corresponds to Windows 10 version 21H2, the November 2021 update. You can get the latest update from here: Download Windows 10. For instructions, see Install WSL2 and NVIDIA’s setup docs for CUDA in WSL.

conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
python3 -m pip install tensorflow

score 0 · Answer 5 · answered Feb 04 '23 at 09:20

0

if you have Cuda installed . For me i was using a RTX 3060 so CUDA 11.2 was installed but for some reason the ddls inside it wasnt getting picked up so i copied the required ddls from that folder and pasted in windows/System32 folder it seems to be working now

answered Feb 04 '23 at 09:20

Krishnanand Anil

1

As it’s currently written, your answer is unclear. Please [edit] to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers [in the help center](/help/how-to-answer). – Community Feb 06 '23 at 20:25

Fix not load dynamic library for Tensorflow GPU

5 Answers5

Solution

Quick Installation

Check that Tensorflow is using the GPU

Easily Reproducible Installation with Environment File: `environment.yml`

Useful Instructions

References

Linked

Related

Fix not load dynamic library for Tensorflow GPU

5 Answers5

Solution

Quick Installation

Check that Tensorflow is using the GPU

Easily Reproducible Installation with Environment File: environment.yml

Useful Instructions

References

Linked

Related

Easily Reproducible Installation with Environment File: `environment.yml`