lttlrck

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

ljharb

Array Iteration

https://gist.github.com/ljharb/58faf1cfcb4e6808f74aae4ef7944cff

While attempting to explain JavaScript's reduce method on arrays, conceptually, I came up with the following - hopefully it's helpful; happy to tweak it if anyone has suggestions.

Intro

JavaScript Arrays have lots of built in methods on their prototype. Some of them mutate - ie, they change the underlying array in-place. Luckily, most of them do not - they instead return an entirely distinct array. Since arrays are conceptually a contiguous list of items, it helps code clarity and maintainability a lot to be able to operate on them in a "functional" way. (I'll also insist on referring to an array as a "list" - although in some languages, List is a native data type, in JS and this post, I'm referring to the concept. Everywhere I use the word "list" you can assume I'm talking about a JS Array) This means, to perform a single operation on the list as a whole ("atomically"), and to return a new list - thus making it mu

jshin49

alias upgrade="sudo -- sh -c 'apt-get update; apt-get upgrade -y; apt-get dist-upgrade -y; apt-get autoremove -y; apt-get autoclean -y'"
alias fastgpu="sudo env DISPLAY=:0 nvidia-settings -a GPUFanControlState=1 -a GPUTargetFanSpeed=100"
alias normalgpu="sudo env DISPLAY=:0 nvidia-settings -a GPUFanControlState=0"
alias mediumgpu="sudo env DISPLAY=:0 nvidia-settings -a GPUFanControlState=1 -a GPUTargetFanSpeed=60"

sparkydogX

import os
import torch
from tqdm import tqdm
import time

# declare which gpu device to use
cuda_device = '0'

def check_mem(cuda_device):
    devices_info = os.popen('"/usr/bin/nvidia-smi" --query-gpu=memory.total,memory.used --format=csv,nounits,noheader').read().strip().split("\n")

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