tucan9389 tucan9389

Goal: built mlir-opt through emscripten to run it interactively from documentation websites.

References:

Notes:

Swift Concurrency Manifesto

	# Show all node_modules folder information in current working directory
	alias show-all-node-modules="find . -name 'node_modules' -type d -prune -print \| xargs du -chs"

	# Remove all node_modules folder in current working directory
	alias remove-all-node-modules="show-all-node-modules && find . -name 'node_modules' -type d -prune -print -exec rm -rf '{}' \;"

	# Usage:
	# command
	# `sh gifconverter.sh /Users/canapio/Desktop/turtle_DEMO_004_4.MP4`
	# on mac

	# some configuration
	width="240"
	fps="12"

	# path setup

	mkdir coco
	cd coco
	mkdir images
	cd images

	wget http://images.cocodataset.org/zips/train2017.zip
	wget http://images.cocodataset.org/zips/val2017.zip
	wget http://images.cocodataset.org/zips/test2017.zip
	wget http://images.cocodataset.org/zips/unlabeled2017.zip

	# Create a simple TF Graph
	# By Omid Alemi - Jan 2017
	# Works with TF <r1.0

	import tensorflow as tf

	I = tf.placeholder(tf.float32, shape=[None,3], name='I') # input
	W = tf.Variable(tf.zeros_initializer(shape=[3,2]), dtype=tf.float32, name='W') # weights
	b = tf.Variable(tf.zeros_initializer(shape=[2]), dtype=tf.float32, name='b') # biases
	O = tf.nn.relu(tf.matmul(I, W) + b, name='O') # activation / output

	bash -c 'while [[ "$(curl -s -o /dev/null -w ''%{http_code}'' localhost:9000)" != "200" ]]; do sleep 5; done'

	# also check https://gist.github.com/rgl/c2ba64b7e2a5a04d1eb65983995dce76