jarohen

(ns rafting
  (:require [clojure.tools.logging :as log])
  (:import java.lang.AutoCloseable
           (java.util.concurrent CompletableFuture Executors TimeUnit)))

(defn new-timeout-ms []
  (+ (System/currentTimeMillis)
     200
     (rand-int 150)))

roaramburu

thomwolf

import os; import psutil; import timeit
from datasets import load_dataset

mem_before = psutil.Process(os.getpid()).memory_info().rss >> 20
wiki = load_dataset("wikipedia", "20200501.en", split='train')
mem_after = psutil.Process(os.getpid()).memory_info().rss >> 20
print(f"RAM memory used: {(mem_after - mem_before)} MB")

s = """batch_size = 1000
for i in range(0, len(wiki), batch_size):

lucj

for node in node1 node2 node3;do
  multipass launch -n $node
done

# Init cluster on node1
multipass exec node1 -- bash -c "curl -sfL https://get.k3s.io | sh -"

# Get node1's IP
IP=$(multipass info node1 | grep IPv4 | awk '{print $2}')

lizthegrey

default['sshd']['sshd_config']['AuthenticationMethods'] = 'publickey,keyboard-interactive:pam'
default['sshd']['sshd_config']['ChallengeResponseAuthentication'] = 'yes'
default['sshd']['sshd_config']['PasswordAuthentication'] = 'no'

cdleon

Works for macOS Sierra and High Sierra (Improved version)

Taken from StackExchange

Thanks to LangLangC

For temperature and other improvements see https://gist.github.com/cdleon/d16e7743e6f056fedbebc329333d79df

This method leaves brightness control enabled

Reset SMC

terry90

FROM clux/muslrust as build

WORKDIR /app/

# Deps caching begins
COPY Cargo.toml .
COPY Cargo.lock .
RUN mkdir src
RUN echo "fn main() {}" > src/main.rs

kayousterhout

Generating Flame Graphs for Apache Spark

Flame graphs are a nifty debugging tool to determine where CPU time is being spent. Using the Java Flight recorder, you can do this for Java processes without adding significant runtime overhead.

When are flame graphs useful?

Shivaram Venkataraman and I have found these flame recordings to be useful for diagnosing coarse-grained performance problems. We started using them at the suggestion of Josh Rosen, who quickly made one for the Spark scheduler when we were talking to him about why the scheduler caps out at a throughput of a few thousand tasks per second. Josh generated a graph similar to the one below, which illustrates that a significant amount of time is spent in serialization (if you click in the top right hand corner and search for "serialize", you can see that 78.6% of the sampled CPU time was spent in serialization). We used this insight to spee

ageitgey

Build tensorflow on OSX with NVIDIA CUDA support (GPU acceleration)

These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.

Requirements

OS X 10.10 (Yosemite) or newer

josephwinston

Getting Pow to work in OS X Yosemite

Some parts taken from: https://gist.github.com/kujohn/7209628

ipfw is officially deprecated and removed in OS X Yosemite. Pow requires another program pf to handle the port forwarding.

1. Anchor file

Create file /etc/pf.anchors/pow