This guide will show you how to use Intel graphics for rendering display and NVIDIA graphics for CUDA computing on Ubuntu 18.04 / 20.04 desktop.
I made this work on an ordinary gaming PC with two graphics devices, an Intel UHD Graphics 630 plus an NVIDIA GeForce GTX 1080 Ti.
Both of them can be shown via lspci | grep VGA.
00:02.0 VGA compatible controller: Intel Corporation Device 3e92
01:00.0 VGA compatible controller: NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)
If you like me use a Linux station to do your development and don't want to use the standard Git diff tool this Gist is for you.
| def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor: | |
| '''Calculate F1 score. Can work with gpu tensors | |
| The original implmentation is written by Michal Haltuf on Kaggle. | |
| Returns | |
| ------- | |
| torch.Tensor | |
| `ndim` == 1. 0 <= val <= 1 | |
This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software
on an Ubuntu Linux system, in particular Ubuntu 20.04.
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
