In Git you can add a submodule to a repository. This is basically a repository embedded in your main repository. This can be very useful. A couple of usecases of submodules:
- Separate big codebases into multiple repositories.
Option Explicit | |
Private Function XLMod(a, b) | |
'สำหรับใช้แทน mod ของ vba เนื่องจาก mod operator ของ vba ไม่รองรับเลขทศนิยม | |
XLMod = a - b * Int(a / b) | |
End Function | |
Function AthikaMas(iYear As Integer) As Boolean | |
'สูตรสำหรับคำนวณปีอธิกมาส | |
'Return True if the specified year is AthikaMas. |
# This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py), | |
# adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs. | |
# | |
# Usage: | |
# - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate` | |
# - Install deepspeed: `pip install deepspeed==0.9.5` | |
# - Install TRL from main: pip install git+https://github.com/huggingface/trl.git | |
# - Clone the repo: git clone github.com/huggingface/trl.git | |
# - Copy this Gist into trl/examples/scripts | |
# - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py |
import os | |
import re | |
import sys | |
import glob | |
import nltk | |
import gensim | |
import numpy as np | |
import pandas as pd | |
from tqdm import tqdm | |
from uuid import uuid4 |
#Install Gource in Ubuntu | |
======================== | |
#Go to the folder.... and | |
#see http://tylerfrankenstein.com/code/install-gource-ubuntu-1010-visualize-git-repo | |
# https://github.com/acaudwell/Gource/releases/download/gource-0.49/gource-0.49.tar.gz | |
sudo apt-get update | |
sudo apt-get install libglew-dev | |
sudo apt-get install libsdl2-dev | |
sudo apt install libsdl2-image-dev |
# -*- coding: utf-8 -*- | |
"""Print most frequent N-grams in given file. | |
Usage: python ngrams.py filename | |
Problem description: Build a tool which receives a corpus of text, | |
analyses it and reports the top 10 most frequent bigrams, trigrams, | |
four-grams (i.e. most frequently occurring two, three and four word | |
consecutive combinations). |
# ตาม guru.sanook.com/1520 | |
import re | |
t1 = str.maketrans("กขฃคฅฆงจฉชฌซศษสญยฎดฏตณนฐฑฒถทธบปผพภฝฟมรลฬฤฦวหฮอ", | |
"กกกกกกงจชชชซซซซยยดดตตนนททททททบปพพพฟฟมรรรรรวหหอ") | |
t2 = str.maketrans( | |
"กขฃคฅฆงจฉชซฌฎฏฐฑฒดตถทธศษสญณนรลฬฤฦบปพฟภผฝมำยวไใหฮาๅึืเแโุูอ", | |
"1111112333333333333333333444444445555555667777889AAABCDEEF") | |
def LK82(s): | |
res = [] |
[https://developer.nvidia.com/cuda-downloads]
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub
as root not sudo
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
# Example of `builder' design pattern | |
# Copyright (C) 2011 Radek Pazdera | |
# This program is free software: you can redistribute it and/or modify | |
# it under the terms of the GNU General Public License as published by | |
# the Free Software Foundation, either version 3 of the License, or | |
# (at your option) any later version. |
""" | |
Functions for converting dates to/from JD and MJD. Assumes dates are historical | |
dates, including the transition from the Julian calendar to the Gregorian | |
calendar in 1582. No support for proleptic Gregorian/Julian calendars. | |
:Author: Matt Davis | |
:Website: http://github.com/jiffyclub | |
""" |