As configured in my dotfiles.
start new:
tmux
start new with session name:
As configured in my dotfiles.
start new:
tmux
start new with session name:
-- Remove the history from | |
rm -rf .git | |
-- recreate the repos from the current content only | |
git init | |
git add . | |
git commit -m "Initial commit" | |
-- push to the github remote repos ensuring you overwrite history | |
git remote add origin git@github.com:<YOUR ACCOUNT>/<YOUR REPOS>.git |
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
from __future__ import print_function | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.autograd import Variable | |
def sample_gumbel(shape, eps=1e-20): | |
U = torch.rand(shape).cuda() | |
return -Variable(torch.log(-torch.log(U + eps) + eps)) |
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 |
# -*- coding: utf-8 -*- | |
# | |
# Copyright © 2019 Shuoyang Ding <shuoyangd@gmail.com> | |
# Created on 2019-04-26 | |
# | |
# Distributed under terms of the MIT license. | |
from enum import Enum | |
import pdb |