bebraw

IMPORTANT! Remember to check out the wiki page at https://github.com/bebraw/jswiki/wiki/Game-Engines for the most up to date version. There's also a "notes" column in the table but it simply does not fit there... Check out the raw version to see it.

This table contains primarily HTML5 based game engines and frameworks. You might also want to check out the [[Feature Matrix|Game-Engine-Feature-Matrix]], [[Game Resources]] and [[Scene Graphs]].

Name	Size (KB)	License	Type	Unit Tests	Docs	Repository	Notes
Akihabara	453	GPL2, MIT	Classic Repro	no	API	github	Intended for making classic arcade-style games in JS+HTML5
AllBinary Platform	Platform Dependent	AllBinary	2D/2.5D/3D	n

michilu

#!/usr/bin/env python
from functools32 import lru_cache
from time import time
import random
import sys

def benchtime(fn):
    oldtime = time()
    fn()
    return time() - oldtime

jmikola

<?php

$m = new MongoClient();
$c = $m->test->foo;
$c->drop();
$c->insert(['_id' => 1, 'x' => str_repeat('z', 1024*1024*4)]);
$c->insert(['_id' => 2, 'x' => str_repeat('z', 1024*1024*4)]);
$c->insert(['_id' => 3, 'x' => str_repeat('z', 1024*1024*4)]);
$c->insert(['_id' => 4, 'x' => str_repeat('z', 1024*1024*4)]);
$c->insert(['_id' => 5, 'x' => str_repeat('z', 1024*1024*4)]);

gmr

module("resty.consul", package.seeall)

_VERSION = '0.1.0'

function service_nodes(service)
  local http = require "resty.http"
  local json = require "cjson"
  local hc = http:new()

  local upstream = ""

afolarin

#Container Resource Allocation Options in docker-run

now see: https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources

You have various options for controlling resources (cpu, memory, disk) in docker. These are principally via the docker-run command options.

##Dynamic CPU Allocation -c, --cpu-shares=0
CPU shares (relative weight, specify some numeric value which is used to allocate relative cpu share)

Reddine

sudo apt-get install build-essential
sudo apt-get update
# BLAS → LAPACK → ATLAS → numpy → scipy → Theano
# remove numpy and scipy
sudo apt-get remove python-numpy
sudo apt-get remove python-scipy
# Instalation commands
sudo apt-get install gfortran
sudo apt-get install libopenblas-dev
sudo apt-get install liblapack-dev

vinhkhuc

"""
Vanilla Char-RNN using TensorFlow by Vinh Khuc (@knvinh).
Adapted from Karpathy's min-char-rnn.py
https://gist.github.com/karpathy/d4dee566867f8291f086
Requires tensorflow>=1.0
BSD License
"""
import random
import numpy as np
import tensorflow as tf

akiross

mingfeima

benchmark

Based on huggingface repo for performance evaluation, actual benchmark run script placed at repo. How to reproduce performance:

1. prepare dataset according to link.
2. update GLUE_DIR to actual dataset path in run_inference.sh.
3. change env settings, the default setting is using 20 cores;

MKL v.s. MKLDNN

Inference performance result on Xeon 6148 (2x20 cores), single socket and single thread.

mingfeima

General guidelines for CPU performance on PyTorch

This file serves a BKM to get better performance on CPU for PyTorch, mostly focusing on inference or deployment. Chinese version available here.

1. Use channels last memory format

Right now, on PyTorch CPU path, you may choose to use 3 types of memory formats.

• torch.contiguous_format: default memory format, also referred as NHCW.
• torch.channels_last: also referred as NHWC.
• torch._mkldnn: mkldnn blocked format.