The Jax developers optimized a differential equation benchmark in this issue which used DiffEqFlux.jl as a performance baseline. The Julia code from there was updated to include some standard performance tricks and is the benchmark code here. Thus both codes have been optimized by the library developers.
| # may go to startup file to make it always available | |
| # https://github.com/MasonProtter/ReplMaker.jl#creating-a-repl-mode-at-startup-time | |
| using ReplMaker | |
| function powershell_command(s) | |
| # adapted from https://github.com/JuliaLang/julia/blob/7a3fff1ea76b62b7681dbed5cc2dee43c64d9a51/base/client.jl#L45 | |
| args = String.(split(s)) | |
| if args[1] == "cd" | |
| new_oldpwd = pwd() | |
| if length(args) > 2 | |
| throw(ArgumentError("cd method only takes one argument")) |
A lot of GitHub projects need to have pretty math formulas in READMEs, wikis or other markdown pages. The desired approach would be to just write inline LaTeX-style formulas like this:
$e^{i \pi} = -1$Unfortunately, GitHub does not support inline formulas. The issue is tracked here.
| """ | |
| Generate table assignments for 'n' customers, | |
| according to a Chinese Restaurant Process with dispersion parameter 'α' | |
| Returns an array of integer table assignments | |
| """ | |
| function crp(n, α) | |
| @assert n > 0 | |
| table_assignments = zeros(Int, n) | |
| table_assignments[1] = 1 # first customer sits at table 1 |
| border: no | |
| height: 600 | |
| license: gpl-3.0 |
| """Provides methods around syncing and usage of AWS s3 buckets as local caches rather than individually | |
| downloading every file""" | |
| import os | |
| import shutil | |
| import boto3 as boto | |
| import multiprocessing | |
| import copy | |
| import hashlib |
| #Set up data partition | |
| sudo mkdir /data | |
| sudo chmod 777 /data | |
| sudo "echo /dev/xvdb /data ext4 rw,user,exec,comment=cloudconfig 0 2 >> /etc/fstab" | |
| sudo mount /data | |
| #Install build environment | |
| sudo sed -i "s/enabled=0/enabled=1" /etc/yum.repos.d/epel.epo | |
| sudo yum -y update | |
| sudo yum -y upgrade |
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
| #!/usr/bin/env python | |
| class Vertex: | |
| def __init__(self, value): | |
| self.value = value | |
| self.edges = {} | |
| def degree(self): | |
| return len(self.edges) | |
| def __str__(self): | |
| return str(self.value) |