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bg = new BackgroundLayer backgroundColor:"black"

scroll_panel = new Layer
	height: bg.height
	width: bg.width
	backgroundColor: "transparent"
scroll_panel.scrollVertical = true

scroll_content = new Layer
	width: bg.width

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<!-- Reference: https://developers.facebook.com/docs/sharing/best-practices#tags -->
<!-- CORE og -->
<!-- Title, without branding. -->
<meta property="og:title" content="" />
<!-- Name, not URL (e.g. Apple, not apple.com) -->
<meta property="og:site_name" content="" />
<!-- URL. Should match canonical URL for SEO. Unique identifier for your article. -->
<meta property="og:url" content="" />
<!-- OPTIONAL description. 2-4 sentences. -->
<meta property="og:description" content="" />

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import seaborn as sns
from scipy.optimize import curve_fit

# Function for linear fit
def func(x, a, b):
    return a + b * x

# Seaborn conveniently provides the data for
# Anscombe's quartet.
df = sns.load_dataset("anscombe")

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Adapted from [scikit learn cheat sheet](http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html).

* More than 50 samples?
  * No
    * Get more data
  * Yes
    * Predicting a category?
      * No
        * Predicting a quantity?
          * No

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from quilt.data.robmarkcole import birds_vs_not_birds as birds
from quilt.asa.img import plot

%matplotlib inline
birds.birds(asa=plot())

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def handler(event, context):
    for record in event['Records']:
        try:
            response = S3_CLIENT.get_object(Bucket=bucket, Key=key)
            eventname = record['eventName']
            bucket = unquote(record['s3']['bucket']['name'])
            if eventname == 'ObjectRemoved:Delete':
                event_type = 'Delete'
                post_to_es(event_type, 0, '', key, {})
                continue

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NB_VERSION = 4

def extract_text(notebook_str):
    formatted = nbformat.reads(notebook_str, as_version=NB_VERSION)
    text = []
    for cell in formatted.get('cells', []):
        if 'source' in cell and 'cell_type' in cell:
            if cell['cell_type'] == 'code' or cell['cell_type'] == 'markdown':
                text.append(cell['source'])

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def post_to_es(event_type, size, text, key, meta, version_id=''):

    ES_HOST = os.environ['ES_HOST']
    ES_INDEX = 'drive'

    data = {
        'type': event_type,
        'size': size,
        'text': text,
        'key': key,

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Quilt Package Usecases

Introduction

This document outlines several key use cases and proposes code samples to satisfy those usecases. Both usecases and code samples are open to discussion.

The document is about UX, not implementation. It does not go much into performance needs, but the expectation is that the implementation will be written such that any API we expose is as high-performance as possible.

API names are rough and will need to be improved.

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pip install quilt3
# Note: installing COCO requires at least 21GB of disk
quilt3 install cv/coco2017 \
  --top-hash 3722a4 \
  --dest ./detectron2/datasets/coco/ \
  --registry s3://quilt-ml