In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
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wangruohui
/ Install NVIDIA Driver and CUDA.md
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June 29, 2024 09:06
Install NVIDIA Driver and CUDA on Ubuntu / CentOS / Fedora Linux OS
cbaziotis
/ Attention.py
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March 28, 2023 11:50
Keras Layer that implements an Attention mechanism for temporal data. Supports Masking. Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
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| from keras import backend as K, initializers, regularizers, constraints | |
| from keras.engine.topology import Layer | |
| def dot_product(x, kernel): | |
| """ | |
| Wrapper for dot product operation, in order to be compatible with both | |
| Theano and Tensorflow | |
| Args: |
cbaziotis
/ AttentionWithContext.py
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April 25, 2022 14:37
Keras Layer that implements an Attention mechanism, with a context/query vector, for temporal data. Supports Masking. Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf] "Hierarchical Attention Networks for Document Classification"
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| def dot_product(x, kernel): | |
| """ | |
| Wrapper for dot product operation, in order to be compatible with both | |
| Theano and Tensorflow | |
| Args: | |
| x (): input | |
| kernel (): weights | |
| Returns: | |
| """ | |
| if K.backend() == 'tensorflow': |
wangruohui
/ intel-nvidia.md
Last active
July 17, 2024 00:58
Intel for display, Nvidia for computing
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)
normandipalo
/ dinobot_oneshot_il.py
Last active
July 16, 2024 13:44
Code snippet for the one-shot imitation learning phase of DINOBot (alignment + replay).
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| """ | |
| In this script, we demonstrate how to use DINOBot to do one-shot imitation learning. | |
| You first need to install the following repo and its requirements: https://github.com/ShirAmir/dino-vit-features. | |
| You can then run this file inside that repo. | |
| There are a few setup-dependent functions you need to implement, like getting an RGBD observation from the camera | |
| or moving the robot, that you will find on top of this file. | |
| """ | |
| import torch | |
| import numpy as np |