ImportError: Could not find 'cudart64_100.dll

Question

I\'m trying to install tensorflow-gpu==2.0.0-beta1 on my Windows 10 machine and got this error:

ImportError: Could not find \'cudart64_10

Answer 1

I was having the same issue on windows 10. I tried to use CUDA 11 with TensorFlow 2.2

TensorFlow 2.2 try to import cudart64_101.dll, cusparse64_10.dll and cublas64_10.dll The above dll files are missing in CUDA 11. One possible solution is to install cuda 10 and take the dll's from there. But i didn't want to do that and experiment with this trick which solve my issue. I download the missing dll from the post in the first answer! Thank you very much to the author of this post ! https://www.joe0.com/2019/10/19/how-resolve-tensorflow-2-0-error-could-not-load-dynamic-library-cudart64_100-dll-dlerror-cudart64_100-dll-not-found/

• Download cudart64_100.dll_.zip
• extract the file
• rename it to cudart64_101.dll

go to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin

• coppy the downloaded and renamed cudart64_101.dll in that directory
• coppy cusparse64_11.dll and rename it to cusparse64_10.dll
• coppy cublas64_11.dll and rename it ot cublas64_10.dll

After that TensorFlow 2.20 started to import successfully and it was able to "see" my gpu

Other important thing is to check system environment PATH variable Make sure that above lines are included.

• C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin
• C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\libnvvp

Or if you have multiple CUDA versions installed on your machine make sure this one which you want to use is above the old one

Answer 2

The simplest way to fix is to install the latest ‘NVIDIA GPU Computing Toolkit’, because if it's not there, you'll be missing the 'cudart64_100.dll' library. The only issue is that the latest copy of CUDA has this particular library upgraded to 'cudart64_101.dll', while the latest TensorFlow still requires the older 'cudart64_100.dll'. Anyways, one way to deal with this issue is to install the latest CUDA + CUDA from September 2018 and then copy 'cudart64_100.dll' library from old install to the new one.

Or just visit my site where I linked the 'cudart64_100.dll' library downloaded from the CUDA Toolkit 10.0 (Sept 2018), to make it easier to copy it into the latest CUDA directory.

Here are some screenshots to illustrate the process: https://www.joe0.com/2019/10/19/how-resolve-tensorflow-2-0-error-could-not-load-dynamic-library-cudart64_100-dll-dlerror-cudart64_100-dll-not-found/

Answer 3

you can find the cudart64_100.dll file in this website link. and extract it, add cudart64_100.dll to your C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin after you run your python script you will see: Successfully opened dynamic library cudart64_100.dll

Answer 4

Just rename

\program files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin\cudart64_102.dll 

to cudart100.dll

Answer 5

I had similar error:

cudart64_101.dll not found

It is because the latest version of CUDA requires older CUDA-version.dll files to work properly. The solution would be try installing the previous version of CUDA.

Once you have downloaded CUDA 10.1 run the .exe file which will first extract the necessary files in C:\Users\your_user_name\AppData\Local\Temp\CUDA.

Once the extraction is completed do not proceed with the installation navigate to the directory C:\Users\your_user_name\AppData\Local\Temp\CUDA\cudart\bin and here you will find the missing DLL file cudart64_101.dll and cudart32_101.dll copy both the files to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin and then cancel the installation.

Follow the same steps for any CUDA version will work for sure. Hope this helps thank you!!!

Answer 6

I also was dealing with the current situation that tensorflow expects cudart64_101.dll and NVIDIA offers Version 10.2 as main version (including cudart64_102.dll).

I simply installed both versions. I have both versions in the windows path. Beside disc space, I did not have problems so far and GPU is used in tensorflow.