kevashcraft

# if dataset is not batched
# this will take 1 example
with (ambient, target), label in dataset.take(1):
  print("ambient shape", ambient.shape)
  print("target shape", target.shape)
  print("label shape", label.shape)
  ambient_array = ambient.numpy()
  target_array = target.numpy()
  label_array = label.numpy()
  

kevashcraft

# Read a TFRecord file into a TF Dataset
def map_fn(serialized_example):
  feature = {
      'label': tf.io.FixedLenFeature([1], tf.int64),
      'ambient': tf.io.FixedLenFeature([16000], tf.float32),
      'target': tf.io.FixedLenFeature([4000], tf.float32)
  }
  example = tf.io.parse_single_example(serialized_example, feature)
  ambient = tf.expand_dims(example['ambient'], 1)
  target = tf.expand_dims(example['target'], 1)

kevashcraft

# Write a ambient, target, and label data to a TFRecords file
with tf.io.TFRecordWriter('examples.tfrecord') as training_file:
  for ambient, target, label in batch: # batch is a list of (ambient, target, label) tuples
    features = {
        'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
        'ambient': tf.train.Feature(float_list=tf.train.FloatList(value=ambient.tolist())), # ambient is a 1-D np array
        'target': tf.train.Feature(float_list=tf.train.FloatList(value=target.tolist())) # target is a 1-D np array
    }
    example_proto = tf.train.Example(features=tf.train.Features(feature=features))
    training_file.write(example_proto.SerializeToString())

kevashcraft

label_size = 1 # the length of the previously written label
feature_size = 256 # the length of the previously written feature lists
def map_fn(serialized_example):
    feature = {
        'label': tf.io.FixedLenFeature([label_)size], tf.int64),
        'features': tf.io.FixedLenFeature([feature_size], tf.float32)
    }
    example = tf.io.parse_single_example(serialized_example, feature)
    features = example['features']
    label = tf.cast(example['label'], tf.int32)

kevashcraft

with tf.io.TFRecordWriter('examples.tfrecord') as training_file:
    for features, label in batch:
        features = {
            'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
            'features': tf.train.Feature(float_list=tf.train.FloatList(value=features)) # expects list, so if numpy use .tolist() and ensure it's 1-D
        }
        example_proto = tf.train.Example(features=tf.train.Features(feature=features))
        training_file.write(example_proto.SerializeToString())

kevashcraft

convert logo.png -define icon:auto-resize=64,48,32,16 logo.ico

kevashcraft

clearInterval(aa)
function mv() {
    var found_one = false
    var primary = 0

    let el = document.querySelectorAll('.mlbtv-media-player')[primary]
    let has_ads = !!el.querySelector('.interruption-link')
    if (!has_ads) {
        el.querySelector('video').muted = false

kevashcraft

from urllib.request import urlopen
import yaml

def retrieve(symbol=None):
	with open('config.yml') as f:
		config = yaml.load(f)

	params = {
		'function': 'TIME_SERIES_DAILY',
		'symbol': symbol,

kevashcraft

Kubernetes on DigitalOcean with CoreOS

Let's look at an example of how to launch a Kubernetes cluster from scratch on DigitalOcean, including kubeadm, an Nginx Ingress controller, and Letsencrypt certificates.

Overview

Environment

We'll be creating a four-node cluster (k8s-master, k8s-000...k8s-002), load balancer, and ssl certificates.

Table of Contents

1. Install Kubernetes

kevashcraft

Setup WordPress with Docker-Compose on CoreOS

Setup Steps

1. Create docker-compose.yml in /home/core/, replacing the $variables with your values
2. Create nginx.tmpl in /var/lib/docker/volumes/wpserver_nginx_templates/_data/
3. Create wp-server.service in /etc/systemd/system/
4. Run sudo systemctl daemon-reload to load the new service file
5. Run sudo systemctl start wp-server to start the WordPress Server
6. Run sudo systemctl status wp-server to make sure everything is running