After chroot-ing into the installation through a live iso. (See my other gist)
Shortlink: https://tinyurl.com/lvm-luks
Target Installation Disk: /dev/sda (yours may be different)
| #!/usr/bin/python3 | |
| # A simple timer with following features | |
| # 1. It will beep when the timer is done | |
| # 2. Press S to start stopwatch and accumulate time | |
| # 3. Press S again to stop stopwatch and print accumulated time | |
| # 4. Press T to start timer and lose from accumulated time | |
| # 5. Press T again to stop timer and print accumulated time | |
| # | |
| # Author: Bishal Santra (http://bsantraigi.github.io) MIT License |
| 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) | |
| Basic outline taken from https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 | |
| """ |
| #include <iostream> | |
| #include <cstring> | |
| #include <cmath> | |
| #include <cstdlib> | |
| #include <vector> | |
| #include <ctime> | |
| using namespace std; | |
| const int inf = 1e6; |
| #include <iostream> | |
| using namespace std; | |
| int main(){ | |
| // IO: | |
| cout << "Enter N and C:" << endl; | |
| int N, C; | |
| cin >> N; | |
| cin >> C; |
| #!/bin/bash | |
| # Syncs the content of the current folder with onedrive. **Upload only** | |
| rclone copy "./" "onedrive:/Projects Backups/" -P |
| # How to run | |
| # chmod +x git_backup_repo.sh | |
| # ./git_backup_repo.sh https://github.com/bsantraigi/MyRepo.git | |
| REPO=$1 | |
| echo "Backup: $REPO" | |
| DIR=$(grep -o -e "[^/]*.git$" <<< $REPO) | |
| # DIR=$? | |
| echo "Repo Cloned To: $DIR" |
| #!/bin/bash | |
| #$ -N qtest | |
| #$ -j y # Merge the IO to standard IO | |
| #$ -pe smp 6 | |
| #$ -cwd # Run in current directory. Otherwise defaults to home folder | |
| #$ -V | |
| #$ -l h_vmem=6G | |
| #$ -l gpu=2 | |
| # For more options, follow the steps from http://bioinformatics.mdc-berlin.de/intro2UnixandSGE/index.html |
| # Check Gradient | |
| for p in model.parameters(): | |
| param_norm = p.grad.data.norm(2) | |
| total_norm += param_norm.item() ** 2 | |
| total_norm = total_norm ** (1. / 2) | |
| # Clip Gradient Norm | |
| optimizer.zero_grad() | |
| loss, hidden = model(data, hidden, targets) | |
| loss.backward() |
