If you working with Machine Learning using GPU this story is the answer.

The story behind this is when I try to run a tensorflow python script, it comes with this warning message :

2020-07-29 17:11:59.393693: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory

So i search on google what is libcudart.so. And I found that libcudart is CUDA library from NVIDIA that can use GPU to run.

wow… that is something for me, because so far i only used GPU for gaming, and now i can code with GPU.

OK… let’s get started…

CUDA

We need bridge from our code to access GPU. If you used NVIDIA for your GPU then CUDA is the answer

Hardware requirements

• NVIDIA® GPU card with CUDA® architectures 3.5 or higher. See the list of CUDA®-enabled GPU cards.

Software requirements

The following NVIDIA® software must be installed on your system:

• NVIDIA® GPU drivers — CUDA® 10.1 requires 418.x or higher.
• CUDA® Toolkit — TensorFlow supports CUDA® 10.1 (TensorFlow >= 2.1.0)
• cuDNN SDK 7.6

#python #tensorflow #gpu #cuda