Jingchen Hu noname01

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

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	from pydoc import locate
	import tensorflow as tf
	import numpy as np
	from seq2seq import tasks, models
	from seq2seq.training import utils as training_utils
	from seq2seq.tasks.inference_task import InferenceTask, unbatch_dict


	class DecodeOnce(InferenceTask):
	'''

	# based on https://github.com/google/seq2seq/blob/master/bin/tools/generate_beam_viz.py

	# extracts probabilities and sequences from .npz file generated during beam search.
	# and pickles a list of the length n_samples that has beam_width most probable tuples
	# (path, logprob, prob)
	# where probs are scaled to 1.

	import numpy as np
	import networkx as nx
	import pickle

	/**
	* STEP 1: Setup
	* - Open Chrome Secure Shell settings
	* - Open JS Console (CTRL+SHIFT+J)
	* - Copy and paste the following:
	*/
	var s7d_colours = {
	'base03': '#002b36',
	'base02': '#073642',
	'base01': '#586e75',

	import os
	import struct
	import numpy as np

	"""
	MNist loading helper for Python 2.7.

	For Python 3.x, see https://gist.github.com/akesling/42393ccb868125071fdea77d98a0d2f0

	Loosely inspired by http://abel.ee.ucla.edu/cvxopt/_downloads/mnist.py