ih2502mk

Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

kmhofmann

Installing the NVIDIA driver, CUDA and cuDNN on Linux (Ubuntu 20.04)

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

• NVIDIA graphics card driver (v450.57)
• CUDA (v11.0.2)
• cuDNN (v8.0.2.39)

on an Ubuntu Linux system, in particular Ubuntu 20.04.

SuperShinyEyes

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
    

IvanCl4udio

How configure P4Merge as merge and diff tool on Linux

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.

Installation

1. Download installation from Perforce Web Site

https://www.perforce.com/downloads/visual-merge-tool

wangruohui

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)

baiwfg2

The initial source comes from sdcuike/issueBlog#4

http://greenteapress.com/wp/

https://papers.nips.cc

https://github.com/PacktPublishing free to download books code by Packet

https://github.com/EbookFoundation/free-programming-books Very immense

kmhofmann

Building TensorFlow from source (TF 2.3.0, Ubuntu 20.04)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht

AtulKsol

psql: FATAL: Peer authentication failed for user “postgres” (or any user)

The connection failed because by default psql connects over UNIX sockets using peer authentication, that requires the current UNIX user to have the same user name as psql. So you will have to create the UNIX user postgres and then login as postgres or use sudo -u postgres psql database-name for accessing the database (and psql should not ask for a password).

If you cannot or do not want to create the UNIX user, like if you just want to connect to your database for ad hoc queries, forcing a socket connection using psql --host=localhost --dbname=database-name --username=postgres (as pointed out by @meyerson answer) will solve your immediate problem.

But if you intend to force password authentication over Unix sockets instead of the peer method, try changing the following pg_hba.conf* line:

from

ewenchou

1. Create a service file like dash_sniffer.service
2. Put it in /lib/systemd/system/
3. Reload systemd using command: systemctl daemon-reload
4. Enable auto start using command: systemctl enable dash_sniffer.service

lukas-h

Markdown License badges

Collection of License badges for your Project's README file.
This list includes the most common open source and open data licenses.
Easily copy and paste the code under the badges into your Markdown files.

Notes

• The badges do not fully replace the license informations for your projects, they are only emblems for the README, that the user can see the License at first glance.

Translations: (No guarantee that the translations are up-to-date)

• 🇫🇷 Cette liste en français