winhamwr

"""
Celery base task aimed at longish-running jobs that return a result.

``AwesomeResultTask`` adds thundering herd avoidance, result caching, progress
reporting, error fallback and JSON encoding of results.
"""
from __future__ import division

import logging
import simplejson

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

Zearin

NOTE: This is a question I found on StackOverflow which I’ve archived here, because the answer is so effing phenomenal.

---

Q: How can I make a chain of function decorators in Python?

---

If you are not into long explanations, see [Paolo Bergantino’s answer][2].

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

bendc

:root {
  --ease-in-quad: cubic-bezier(.55, .085, .68, .53);
  --ease-in-cubic: cubic-bezier(.550, .055, .675, .19);
  --ease-in-quart: cubic-bezier(.895, .03, .685, .22);
  --ease-in-quint: cubic-bezier(.755, .05, .855, .06);
  --ease-in-expo: cubic-bezier(.95, .05, .795, .035);
  --ease-in-circ: cubic-bezier(.6, .04, .98, .335);

  --ease-out-quad: cubic-bezier(.25, .46, .45, .94);
  --ease-out-cubic: cubic-bezier(.215, .61, .355, 1);

nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

HarshTrivedi

import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
#     Step 1: Construct Vocabulary
#     Step 2: Load indexed data (list of instances, where each instance is list of character indices)

charlesroper

How to setup a practically free CDN using Backblaze B2 and Cloudflare

⚠️ Note 2023-01-21
Some things have changed since I originally wrote this in 2016. I have updated a few minor details, and the advice is still broadly the same, but there are some new Cloudflare features you can (and should) take advantage of. In particular, pay attention to Trevor Stevens' comment here from 22 January 2022, and Matt Stenson's useful caching advice. In addition, Backblaze, with whom Cloudflare are a Bandwidth Alliance partner, have published their own guide detailing how to use Cloudflare's Web Workers to cache content from B2 private buckets. That is worth reading,

aditya-malte