Motoki Wu tokestermw

See the official Differentiable Programming Manifesto instead.

Feature Store

Uber Michelangelo

https://eng.uber.com/michelangelo/

Finding good features is often the hardest part of machine learning and we have found that building and managing data pipelines is typically one of the most costly pieces of a complete machine learning solution.

A platform should provide standard tools for building data pipelines to generate feature and label data sets for training (and re-training) and feature-only data sets for predicting. These tools should have deep integration with the company’s data lake or warehouses and with the company’s online data serving systems. The pipelines need to be scalable and performant, incorporate integrated monitoring for data flow and data quality, and support both online and offline training and predicting. Ideally, they should also generate the features in a way that is shareable across teams to reduce duplicate work and increase data quality. They should also provide strong guard rails and controls to encourage and empower users to adop

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
How does PyTorch cwrap work to generate code for Tensor methods?
How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare [email protected]:usi-systems/easytrace.git

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

Effective Engineer - Notes

By Edmond Lau
Highly Recommended 👍
http://www.theeffectiveengineer.com/

	# Based on the example from the TensorFlow repository: https://github.com/tensorflow/tensorflow/
	# https://github.com/tensorflow/tensorflow/blob/671baf080238025da9698ea980cd9504005f727c/tensorflow/examples/learn/text_classification_character_rnn.py


	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import argparse
	import sys

	'''
	Copyright (C) 2018 by Daniel Foose

	Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted.

	THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE
	'''

	import re
	import docker

	"""
	Author: Awni Hannun

	This is an example CTC decoder written in Python. The code is
	intended to be a simple example and is not designed to be
	especially efficient.

	The algorithm is a prefix beam search for a model trained
	with the CTC loss function.