leopd

sudo zfs allow -s @allperm allow,clone,create,destroy,mount,promote,receive,rename,rollback,send,share,snapshot rustinside
sudo zfs allow leo @allperm rustinside

leopd

package algostudy;

import java.util.ArrayList;
import java.util.Comparator;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.PriorityQueue;
import java.util.Queue;

leopd

# This code doesn't work, and isn't intended to.
# The goal of this code is to explain how attention mechansisms work, in code.
# It is deliberately not vectorized to make it clearer.

def attention(self, X_in:List[Tensor]):
    # For every token transform previous layer's out
    for i in range(self.sequence_length):
        query[i] = self.Q * X_in[i]
        key[i]   = self.K * X_in[i]
        value[i] = self.V * X_in[i]

leopd

#!/bin/bash

git config --global alias.dag "log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset%n' --abbrev-commit --date=relative --branches"

# This adds a line to ~/.gitconfig like
# [alias]
#   dag = log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset%n' --abbrev-commit --date=relative --branches

leopd

import functools
from torch.utils.data import Dataset

class BlockCachingDatasetWrapper(Dataset):
    """Wraps a pytorch dataset with an LRU cache
    that fetches an entire block of records at once.
    """

    def __init__(self, base_dataset:Dataset, block_size:int=16):
        self._dataset = base_dataset

leopd

#!/bin/bash

git checkout -b main
git branch --delete master
git push origin main
echo "Go to github, Settings, Branches, Default branch, main, update."
read -p "Press enter when done: " dontcare
git push origin --delete master

leopd

# How to use a specific GPU other than the first one.
# Put this at the very top of your python script or notebook.
# Do this before importing your deep learning libraries.
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'

leopd

from functools import lru_cache
import subprocess
from torch.utils.data import Dataset

class FileReaderDataset(Dataset):
    """Exposes a line-delimited text file as a PyTorch Dataset.
    Maintains an LRU cache of lines it has read, while supporting random access into
    files too large to hold in memory. Memory requirement still scales by O(N), but just
    for pointers into the file, about 8 bytes per line.  After the file has been scanned,
    random access will be very fast - as fast as the disk plus the OS's cache of it.

leopd

class Node:
    def __init__(self,value=None,kids=[]):
        self.value = value
        self.kids = kids


def rdfs(n, previsit, postvisit):
    '''Recursive depth-first traversal'''
    previsit(n)
    for k in n.kids:

leopd

" turn off compatibility with the old vi
set nocompatible

set ts=4
set et

" turn syntax highlighting on by default
syntax on

" set auto-indenting on for programming