Hello Guys How Are You All ? Hope You all are fine. Today I Have Faced Tensorflow GPU Could not load dynamic library ‘cusolver64_10.dll’; dlerror: cusolver64_10.dll not found? In Python. Tensorflow GPU Could not load dynamic library So Here I am Explain to you all the possible solutions Here.
Without Wasting your time, Lets start This Article to Solve This Error in Anaconda
How Tensorflow GPU Could not load dynamic library ‘cusolver64_10.dll’; dlerror: cusolver64_10.dll not found error Occurs?
When i run
import tensorflow as tf tf.test.is_gpu_available( cuda_only=False, min_cuda_compute_capability=None )
I get the following error
How to solve ensorflow GPU Could not load dynamic library ‘cusolver64_10.dll’; dlerror: cusolver64_10.dll not found?
Question: How to solve ensorflow GPU Could not load dynamic library ‘cusolver64_10.dll’; dlerror: cusolver64_10.dll not found?
Answer: Step 1. Move to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin. Step 2. Rename file cusolver64_11.dll To cusolver64_10.dll.
I had the same problem. It turns out that CUDA 11.0 contains cusolver64_10.dll (that’s probably why they indicate CUDA v11.0 in the tensorflow build guide here https://www.tensorflow.org/install/source_windows). Make sure to download cudnn as well!
TensorFlow ver >= 2.4.0 on
Windows, install exactly those versions of
CUDA Toolkit and
cuDNN highlighted below i.e. those listed in the official requirements.(v11.0 as opposed to v11.2)
TensorFlow^ install requirements at the time of writing are as stated here
- NVIDIA® GPU drivers —CUDA® 11.0 requires 450.x or higher.
- CUDA® Toolkit —TensorFlow supports CUDA® 11 (TensorFlow >= 2.4.0)
- CUPTI ships with the CUDA® Toolkit.
- cuDNN SDK 8.0.4.
- (Optional) TensorRT 6.0 to improve latency and throughput for inference on some models.
The problem you are facing has probably to do with the version of CUDA® Toolkit.
Tensorflow is picky about the version of dependencies. Have a look inside
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin**. You should be able to find most^^ of the dlls needed by
TensorFlow there. You may notice that it contains
cusolver64_11.dll as opposed to the expected
cusolver64_10.dll as stated in the output.
Though the renaming hack mentioned in an answer above works, it’s not guaranteed to work reliably all the time. The simple and correct solution is to install the correct dependencies, to begin with.
At the time of writing the compatible versions of
CUDA Toolkit and
CUDA Toolkit 11.0 (May 2020) cuDNN v8.0.4 (September 28th, 2020), for CUDA 11.0
from among the plethora of available versions of both, listed
More recent versions (I tested v11.0 onwards) aren’t yet supported. I remember having the same problems with an earlier version of TensorFlow a few years back.
^ For ver >1.15,
TensorFlow has GPU support included by default hence the CUDA requirements. When unavailable,
TensorFlow works fine – it just reverts to CPU execution.
** Or wherever you installed the toolkit
cudnn64_8.dll comes with
It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you?