srcecde

cd /usr/local/bin
sudo mkdir ffmpeg && cd ffmpeg

# check https://johnvansickle.com/ffmpeg/ for latest build
sudo wget https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz
sudo tar -xf ffmpeg-release-amd64-static.tar.xz

sudo ln -s /usr/local/bin/ffmpeg/ffmpeg-4.4.1-amd64-static/ffmpeg /usr/bin/ffmpeg
sudo ln -s /usr/local/bin/ffmpeg/ffmpeg-4.4.1-amd64-static/ffprobe /usr/bin/ffprobe

dasgoll

{
    "Conditions": {
        "WithSpotPrice": {
            "Fn::Not": [
                {
                    "Fn::Equals": [
                        {
                            "Ref": "SpotPrice"
                        },
                        "0"

jcbf

# Run get info from Elasticsearch  from AWS Lambda.
from __future__ import print_function

import boto3
import certifi
import yaml
from aws_requests_auth.aws_auth import AWSRequestsAuth
from elasticsearch import Elasticsearch, RequestsHttpConnection

def handler(event, context):

nmarley

import boto3
import base64

if __name__ == '__main__':
    session = boto3.session.Session()

    kms = session.client('kms')

    encrypted_password = 'AQECAHjgTiiE7TYRGp5Irf8jQ3HzlaQaHGYgsUJDaavnHcFm0gAAAGswaQYJKoZIhvcNAQcGoFwwWgIBADBVBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDDwxVQuG0oVwpkU7nQIBEIAoVGk1/wpserb+GVUOzE7PiL/Nr9fTDFKZfpKpF0ip2ct4B2q0Wn6ZZw=='
    binary_data = base64.b64decode(encrypted_password)

leonardofed

A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

---

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

yefim

{
  "AWSEBDockerrunVersion": "1",
  "Image": {
    "Name": "<AWS_ACCOUNT_ID>.dkr.ecr.us-east-1.amazonaws.com/<NAME>:<TAG>",
    "Update": "true"
  },
  "Ports": [
    {
      "ContainerPort": "443"
    }

fancellu

import java.util

import org.apache.kafka.clients.consumer.KafkaConsumer

import scala.collection.JavaConverters._

object ConsumerExample extends App {

  import java.util.Properties

jkbradley

/*
This example uses Scala.  Please see the MLlib documentation for a Java example.

Try running this code in the Spark shell.  It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.

This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
Spark: http://spark.apache.org/
*/

import scala.collection.mutable

arnaldorusso

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
import seaborn as sns
# This line is a patch to Seaborn deal with mpl last version.
# sns.set_context(rc={'lines.markeredgewidth': 0.1}) # deal with mpl markers


xs = np.arange(200)