josegonzalez

[user]
    name = MY_NAME
    email = MY_EMAIL_ADDRESS
[git-tmbundle]
    gitnub-path = /Applications/GitNub.app
    gitx-path = /Applications/GitX.app
    show-diff-check = yes
[github]
    user = GITUB_USER
    token = GITHUB_TOKEN

spicycode

# 0 is too far from ` ;)
set -g base-index 1

# Automatically set window title
set-window-option -g automatic-rename on
set-option -g set-titles on

#set -g default-terminal screen-256color
set -g status-keys vi
set -g history-limit 10000

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

BIGBALLON

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

hsm207

ppetrushkov

#include <iostream>
#include <chrono>
#include <cmath>

#include "dnnl.hpp"

#include "fbgemm/Fbgemm.h"


template<typename T>

Smthri

import numpy as np
import torch
import torch.quantization as tq
from torch import nn
from copy import deepcopy

## Manual quantization

# weight, bias, input
kernel_size = 3

Smthri

import os
import sys
import argparse
import numpy as np
import torch
import torch.quantization as tq
from pathlib import Path
from torch import nn
from _collections_abc import Iterable
from collections import OrderedDict