We all know how important machine learning has become and developers are trying to train their models from high-end servers to low-tech PCs. I own Dell XPS 15 9570 and have faced several issues while installing resources and libraries on my device. A lack of documentation can make your setup worse and my own GPU lacks that.
Some developers have even shown their disappointment thinking they can’t set up a machine-learning environment on this GPU, because of a lack of enough resources. I’m going to keep it simple and device-specific. For a complete tutorial, you can follow this article – Setup a Python Environment for Machine Learning and Deep Learning and come back here if got stuck anywhere.
conda update conda
conda update --all
"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin"to both System and User Environment Paths. We’re trying to avoid future errors here.
pip install tensorflow-gpu
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
pip install keras
Now run your Jupyter notebook to test out your models. For starters, give this example a try. Let me know if you received any errors while Installing these libraries, I can test them out.