apt update && apt upgrade -y
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
cat <<EOF > /etc/apt/sources.list.d/kubernetes.list
deb http://apt.kubernetes.io/ kubernetes-xenial main
EOF
#!/bin/sh | |
log() { | |
format="$1"; shift | |
# shellcheck disable=SC2059 | |
>&2 printf "$format\n" "$@" | |
} | |
usage() { | |
>&2 cat <<EOF |
Just documenting docs, articles, and discussion related to gRPC and load balancing.
https://github.com/grpc/grpc/blob/master/doc/load-balancing.md
Seems gRPC prefers thin client-side load balancing where a client gets a list of connected clients and a load balancing policy from a "load balancer" and then performs client-side load balancing based on the information. However, this could be useful for traditional load banaling approaches in clound deployments.
https://groups.google.com/forum/#!topic/grpc-io/8s7UHY_Q1po
gRPC "works" in AWS. That is, you can run gRPC services on EC2 nodes and have them connect to other nodes, and everything is fine. If you are using AWS for easy access to hardware then all is fine. What doesn't work is ELB (aka CLB), and ALBs. Neither of these support HTTP/2 (h2c) in a way that gRPC needs.
http://mojodna.net/2014/05/14/kvm-libvirt-and-ubuntu-14-04.html
- Get a qcow2 image from here: https://cloud-images.ubuntu.com/releases/16.04/release/
- Convert it with qemu-img
- Resize it (we'll add 50GB). This will be a "thin" resize.
Example: https://cloud-images.ubuntu.com/releases/16.04/release/ubuntu-16.04-server-cloudimg-amd64-disk1.img
This is a collection of basic "recipes", many using twurl (the Swiss Army Knife for the Twitter API!) and jq to query the Twitter API and format the results. Also, some scripts to test or automate common actions.
Let’s see how to use PostgreSQL to import and export CSV files painlessly with the COPY
command.
Import CSV into table t_words
:
COPY t_words FROM '/path/to/file.csv' DELIMITER ',' CSV;
You can tell quote char with QUOTE
and change delimiter with DELIMITER
.
import org.apache.spark.ml.feature.{CountVectorizer, RegexTokenizer, StopWordsRemover} | |
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer} | |
import org.apache.spark.mllib.linalg.Vector | |
import sqlContext.implicits._ | |
val numTopics: Int = 100 | |
val maxIterations: Int = 100 | |
val vocabSize: Int = 10000 |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
/* | |
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 |
import nltk | |
text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital | |
computer or the gears of a cycle transmission as he does at the top of a mountain | |
or in the petals of a flower. To think otherwise is to demean the Buddha...which is | |
to demean oneself.""" | |
# Used when tokenizing words | |
sentence_re = r'''(?x) # set flag to allow verbose regexps | |
([A-Z])(\.[A-Z])+\.? # abbreviations, e.g. U.S.A. |