MilesCranmer

def preprocess_config(s: str):
  """Remove imports from a string representation of a python file"""
  # We assume that imports are not multi-line.
  lines = s.splitlines()
  out_lines = []

  for line in lines:
    # Skip lines with import in them:
    if 'import' in line:
      continue

MilesCranmer

// Count elements of an array by using a lookup table.
#include <stdio.h>
#include <stdlib.h>
#include <stdbool.h>
#include <time.h>

int main(int argc, char *argv[])
{
    // Generate random array of integers, with
    // size given by args.

MilesCranmer

import re

def reduce_precision_of_constants_in_string(s, precision=3):
    # Find all constants in the string:
    constants = re.findall(r"\b[-+]?\d*\.\d+|\b[-+]?\d+\.?\d*", s)
    for c in constants:
        reduced_c = "{:.{precision}g}".format(float(c), precision=precision)
        s = s.replace(c, reduced_c)
 
    return s

MilesCranmer

#main-window[titlepreface*="[Sidebery]"] #TabsToolbar {
    visibility: collapse !important;
}
#sidebar-header {
    display: none;
}
#main-window #TabsToolbar {
    overflow: hidden;
    transition: height .3s .3s !important;

MilesCranmer

<?php

require_once 'vendor/autoload.php';

use Gregwar\Captcha\CaptchaBuilder;

// Create a captcha with the text "MilesCranmer@mastodon.social"
$builder = new CaptchaBuilder('MilesCranmer@mastodon.social');
$builder->setMaxBehindLines(10);
$builder->setMaxFrontLines(10);

MilesCranmer

from argparse import ArgumentParser
import time

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from torch.optim import Adam
from torch.nn import functional as F

MilesCranmer

using BenchmarkTools
using ForwardDiff
using ReverseDiff
using Random
using Plots
using Statistics: mean, quantile, std
using Measurements
using Printf: @sprintf
using Colors

MilesCranmer

using Flux
using Fluxperimental: @compact

nf = 10
nb = 32
nt = 100
d_attn = 64
d_value = 128
d_head = 16
d_out = 256

MilesCranmer

┌ Warning: Recursive type
│   T = Node{Float64}
└ @ Enzyme /dev/shm/.julia/packages/GPUCompiler/BxfIW/src/utils.jl:56
┌ Warning: Recursive type
│   T = Node{Float64}
└ @ Enzyme /dev/shm/.julia/packages/GPUCompiler/BxfIW/src/utils.jl:56
┌ Warning: Recursive type
│   T = Node{Float64}
└ @ Enzyme /dev/shm/.julia/packages/GPUCompiler/BxfIW/src/utils.jl:56
┌ Warning: Recursive type

MilesCranmer

# Apply a function to any slurm job matching a regexp.
# For example, `son 'my_job_32.*' scancel {}` would run `scancel <job>` on any
# job matching the regexp.
function son {
    squeue -u $USER --format="%i %j" | awk "/${1}/"' {print $1}' | xargs -I {} ${@:2}
}


# Watch a detailed `squeue` output (from Siavash Golkar)
function sqw {