(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
// Lefalet shortcuts for common tile providers - is it worth adding such 1.5kb to Leaflet core? | |
L.TileLayer.Common = L.TileLayer.extend({ | |
initialize: function (options) { | |
L.TileLayer.prototype.initialize.call(this, this.url, options); | |
} | |
}); | |
(function () { | |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
from __future__ import division | |
from sklearn.cluster import KMeans | |
from numbers import Number | |
from pandas import DataFrame | |
import sys, codecs, numpy | |
class autovivify_list(dict): | |
'''Pickleable class to replicate the functionality of collections.defaultdict''' | |
def __missing__(self, key): | |
value = self[key] = [] |
library(tabulizer) | |
library(tidyverse) | |
library(stringr) | |
library(abjutils) | |
u <- "http://legis.senado.leg.br/sdleg-getter/documento/download/a5e8c92c-86c1-45bb-99bd-3ad098905e81" | |
tab <- tabulizer::extract_tables(u) | |
arrumar <- function(x) { | |
x %>% | |
tolower() %>% |
Kong, Traefik, Caddy, Linkerd, Fabio, Vulcand, and Netflix Zuul seem to be the most common in microservice proxy/gateway solutions. Kubernetes Ingress is often a simple Ngnix, which is difficult to separate the popularity from other things.
This is just a picture of this link from March 2, 2019
Originally, I had included some other solution
achievement,achievement_code,additional_value,author,author_page_summary,category,department,destination,file_generation_date,file_generation_time,intervention,intervention_code,justification,location,number,proposed_wording,reference,total_page_summary,type,commitment_info_url,url | |
Implantação/Aparelham/Adequação Unid Saúde/ Aquis Unid Móvel,552,5.343.000,3230 - Jaime Martins,1 de 13,Individual,Saúde,ESPELHO DE EMENDA DE APROPRIAÇÃO DE DESPESA,2013-12-02,22:03,Atenção Especializada:Hospitais/Policlínicas/Unid.Especializ,003,,3100000 - Minas Gerais,32300001,,,3497 de 8807,Apropriação - Inclusão,http://inteligenciadenegocios3.camara.gov.br/painel/redirectorcamento.jsp?urlbo=iDocID=79334%26sOutputFormat=P%26sRefresh=Y%26lsSANO=2009%26lsSMES=12%26lsSORGAO=%26lsSUO=55901%26lsSACAO=2B31%26lsSSUBTITULO=0031,http://www.camara.gov.br/internet/comissao/index/mista/orca/orcamento/or2009/emendas/despesa/DANIELRJ_AV_LOA_AUTOR2_3230.pdf | |
Implantação/Aparelham/Adequação Unid Saúde/ Aquis Unid Móvel,552,2.000.000,3230 - Jaime |
#!/usr/bin/perl -w | |
# act as a KSysGuard sensor | |
# provides NVIDIA GPU info via `nvidia-smi` | |
# Usage: | |
# 1. Save this script, make it executable and move it to a directory in your $PATH | |
# 2. Save this ksysguard sensor file for Nvidia: https://gist.github.com/Sporif/31f0d8d9efc3315752aa4031f7080d79 | |
# 2. In KSysGuard's menu, open "File > Import Tab From File option" | |
# 3. Open the sensor file (nvidia.srgd) |
Install HF Code Autocomplete VSCode plugin.
We are not going to set an API token. We are going to specify an API endpoint.
We will try to deploy that API ourselves, to use our own GPU to provide the code assistance.
We will use bigcode/starcoder
, a 15.5B param model.
We will use NF4 4-bit quantization to fit this into 10787MiB VRAM.
It would require 23767MiB VRAM unquantized. (still fits on a 4090, which has 24564MiB)!