soaxelbrooke

Postgrest doesn't like you executing arbitrary queries, but you can get around it by defining a function that executes the query for you:

$ psql mydb
mydb=# create function custom_query(query text) returns setof json as $f$
    begin 
    return query execute format('with tmp as (%s) select row_to_json(tmp.*) from tmp;', query); 
    end
 $f$ language plpgsql;

soaxelbrooke

use yew::events::ChangeData;
use yew::web_sys::File;
use yew::prelude::*;

pub struct MyFileInput {
    link: ComponentLink<Self>,
    file: Option<File>,
}

pub enum Msg {

soaxelbrooke

# Having already:
# export AWS_ACCESS_KEY_ID=youraccesskey
# export AWS_SECRET_ACCESS_KEY=yoursecretkey

import pyarrow.dataset as ds
import polars as pl
import s3fs

S3_ENDPOINT = "http://your.s3.endpoint:3900"

soaxelbrooke

#!/usr/bin/env python3.6
"""
Example usage:

$ python3.6 wvsqlite.py glove.840B.300d.txt

Produces an sqlite database at  with byte strings of floats for each word vector, indexed by 
token for fast lookup for vocabs much smaller than the embedding vocab (aka most real vocabs).

Float size can be set via FLOAT_BYTES env var, and can be 4 or 8, and LIMIT can be set to take 

soaxelbrooke

Adding tailscale to an EdgeRouter (and surviving system upgrades)

I suggest you run sudo bash on all of these so you're the root user.

Installing

1. Download tailscale and put the files in /config/. Find the latest stable or unstable version for your EdgeRouter's processor (ex. ER4 is mips and ERX is mipself)

sudo bash    # if you havent already

soaxelbrooke

systemd Talk

First, let's make ourselves a simple python web server with flask:

from flask import Flask
app = Flask(__name__)
import os

PORT = int(os.getenv('FLASK_PORT', 5000))

soaxelbrooke

#!/usr/bin/env python3


import sqlite3
import csv
import sys

quantize = '--quantize' in sys.argv

soaxelbrooke

// A prototype-based test wrapper based on generators.
// See original Haskell quickcheck paper: http://www.cs.tufts.edu/~nr/cs257/archive/john-hughes/quick.pdf

// --------------------------- //
// Scroll to bottom for usage! //
// --------------------------- //

import R from 'ramda';

const RUNS_PER_TEST = 50;

soaxelbrooke

import keras
import tensorflow
import numpy
import re

# Capturing group is important so it can be left padded with space (token splitter)
token_pattern = r"([\w']+|[,\.\?;\-\(\)])"
substitution = r" \1"

soaxelbrooke

# Need wf - install with `cargo install wf`

mkdir splits wfs
echo 'Splitting file into parts...'
split -a 5 -l 200000 $1 splits/split
ls splits/ | parallel 'echo "Counting {}..."; cat splits/{} | wf > wfs/{}_wf.txt'
echo 'Combining split counts...'
python -c 'from tqdm import tqdm; from functools import reduce; from glob import glob; from collections import Counter; of = open("wfs.txt", "w"); wf = reduce(lambda a, b: a + b, (Counter(dict((pair[0], int(pair[1])) for pair in (line.strip().split() for line in open(fpath)))) for fpath in tqdm(glob("wfs/*"))), Counter()); [of.write("{} {}\n".format(key, count)) for key, count in sorted(wf.items(), key=lambda p: -p[1])]'
rm -rf wfs splits
echo 'Word frequencies written to wfs.txt.'