Nvidia cuda toolkit compatibility
21:10:57.123362: W tensorflow/stream_executor/platform/default/dso_:55] Could not load dynamic library 'libcusolver.so.10.0' dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory LD_LIBRARY_PATH: /usr/local/cuda/lib64 21:10:57.123320: W tensorflow/stream_executor/platform/default/dso_:55] Could not load dynamic library 'libcurand.so.10.0' dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory LD_LIBRARY_PATH: /usr/local/cuda/lib64 21:10:57.123279: W tensorflow/stream_executor/platform/default/dso_:55] Could not load dynamic library 'libcufft.so.10.0' dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory LD_LIBRARY_PATH: /usr/local/cuda/lib64
![nvidia cuda toolkit compatibility nvidia cuda toolkit compatibility](https://www.notebookcheck.net/uploads/tx_nbc2/q5010m.jpg)
21:10:57.123237: W tensorflow/stream_executor/platform/default/dso_:55] Could not load dynamic library 'libcublas.so.10.0' dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory LD_LIBRARY_PATH: /usr/local/cuda/lib64 21:10:57.123186: W tensorflow/stream_executor/platform/default/dso_:55] Could not load dynamic library 'libcudart.so.10.0' dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory LD_LIBRARY_PATH: /usr/local/cuda/lib64 Name: NVIDIA GeForce RTX 3090 major: 8 minor: 6 memor圜lockRate(GHz): 1.695 Installing CUDA 10.2 does not work with the following errors: 21:10:57.123081: I tensorflow/core/common_runtime/gpu/gpu_:1618] Found device 0 with properties:
#Nvidia cuda toolkit compatibility install#
For example, if you want to run TensorFlow 1.15, you must install CUDA 10.0. Note that the minor CUDA version is required. Official page with the compatibility chart Linux CPU (Linux) Version Let's solve these compatibility requirements one by one.
#Nvidia cuda toolkit compatibility driver#
NVIDIA driver 410.48 (comes with CUDA 10.0 Toolkit) or NVIDIA 470 (I installed via apt and confirmed that it works).Note: install a different minor version CUDA 10.2 does not work. Python 3.7 (the latest version supported by TF 1.15).
![nvidia cuda toolkit compatibility nvidia cuda toolkit compatibility](https://img-blog.csdnimg.cn/028e31ee3eab4b77b6c636995512e664.png)
![nvidia cuda toolkit compatibility nvidia cuda toolkit compatibility](https://miro.medium.com/max/664/1*LHUgTnUvwbIaCOzTXvYceg.png)
This post will show the compatibility table with references to official pages. Running (training) legacy machine learning models, especially models written for TensorFlow v1, is not a trivial task mostly due to the version incompatibility issue.