atenni

Problem: When linking to the raw version of a gist, the link changes with each revision.

Solution:

To return the first file from a gist: https://gist.github.com/[gist_user]/[gist_id]/raw/

• Example: https://gist.github.com/atenni/5604522/raw/
• Works even when you change the filename.

To get a file from multi–file gist: https://gist.github.com/[gist_user]/[gist_id]/raw/[file_name]

acamino

Shortcut — Action

• CTRL + A — Move to the beginning of the line
• CTRL + E — Move to the end of the line
• CTRL + [left arrow] — Move one word backward (on some systems this is ALT + B)
• CTRL + [right arrow] — Move one word forward (on some systems this is ALT + F)
• CTRL + U — (bash) Clear the characters on the line before the current cursor position
• CTRL + U —(zsh) If you're using the zsh, this will clear the entire line
• CTRL + K — Clear the characters on the line after the current cursor position
• ESC + [backspace] — Delete the word in front of the cursor

joshbuchea

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

jesseengel

import os

import librosa
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['svg.fonttype'] = 'none'
import numpy as np
from scipy.io.wavfile import read as readwav

# Constants

ZijiaLewisLu

In most of deep learning projects, the training scripts always start with lines to load in data, which can easily take a handful minutes. Only after data ready can start testing my buggy code. It is so frustratingly often that I wait for ten minutes just to find I made a stupid typo, then I have to restart and wait for another ten minutes hoping no other typos are made.

In order to make my life easy, I devote lots of effort to reduce the overhead of I/O loading. Here I list some useful tricks I found and hope they also save you some time.

1. use Numpy Memmap to load array and say goodbye to HDF5.

   I used to relay on HDF5 to read/write data, especially when loading only sub-part of all data. Yet that was before I realized how fast and charming Numpy Memmapfile is. In short, Memmapfile does not load in the whole array at open, and only later "lazily" load in the parts that are required for real operations.

Sometimes I may want to copy the full array to memory at once, as it makes later operations

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)

branislav1991

import h5py
import helpers
import numpy as np
from pathlib import Path
import torch
from torch.utils import data

class HDF5Dataset(data.Dataset):
    """Represents an abstract HDF5 dataset.
    

thomwolf

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

jthodge

defaults write com.apple.Dock appswitcher-all-displays -bool true
killall Dock

asehmi

# How to use:
#
# [1] Ensure you have `debugpy` installed:
#
#    > pip install debugpy
#
# [2] In your main streamlit app:
#
#    import streamlit_debug
#    streamlit_debug.set(flag=True, wait_for_client=True, host='localhost', port=8765)