It has been impossible in the past to get all three of the largest neural network architectures running in the same Python environment in such a way that they don’t conflict and so that they will also train on GPU. The reason I want all the libraries running in the same environment is that AutoTS […]
PyTorch, Tensorflow, and MXNet on GPU in the same environment and GPU vs CPU performance Read More »
I was so excited to see the announcement of the M6 forecasting competition. For those who don’t know the M series of competitions (not to be confused with BMW cars) is a long running series of competitions for forecasting time series. It comes from the 1980’s, well predating the modern data science explosion. And this
All the Problems with the M6 Competition (and a few good things) Read More »
Note that a variation on this article may also be posted on the Nousot blog. Time series are a string of numbers varying across time. It could be the weather, sales of tissue paper, electricity demand of a region, or countless other things. There are plenty of useful things to do with this type of
Preprocessing for Time Series Read More »
Ranked Probability Score is an unusual error metric that was designed for meteorology but which has since more often been utilized for assessing models predicting football (soccer) matches. I was forced upon it because of it’s use in the M6 forecasting competition. The purpose of the ranked probability score is to give an accuracy metric
Ranked Probability Score in Python Read More »
Setting up a Python environment is often a pain. Even worse is that sometimes the environment, once it is finally built, is surprisingly slow because the underlying numeric libraries (BLAS, OneAPI, and so on) are improperly configured. This aims to be a fairly definitive guide to fixing those problems as of early 2022. How much
While doing some benchmarking of my AutoTS package I quickly discovered a troubling trend – my newest, fanciest, most expensive Intel i7 CPUs – an Intel 10700 and 1165G7, were performing vastly slower than their older counterparts. I wasn’t terribly surprised to see the 1165G7 having issues: Tiger Lake – a brand new architecture CPU
For loops are very convenient. With a combination of a for loop and some if-else statements you can do just about anything. There’s just one problem though, for loops are kinda slow. There are many ways to speed up the code, the best usually, in the data science world, to use vectorized NumPy or Pandas
Multiprocessing in Python for Data Science Read More »
AutoTS development has been on hold for the last three months or so. Partly that is because it finally works more or less as I originally intended. In fact, it works better than I originally intended. Still, the current development feels like a failure as I failed to use it to submit any decent models
AutoTS: Looking Forward Read More »
Choosing a place to travel for a week or two is not usually a big challenge. With tens of thousands of awesome places to go to, it is likely that many will be equally satisfying, give or take whatever fate may throw at you. I won’t say it is easy to choose, but rather that
Digital Nomad Index – Rank Your Best Countries Read More »
This release represents several months of fairly hard work for me. It even has a pretty, if simple documentation website that you can check out here. It features quite a number of bug fixes, including several rather critical ones that fixed cross validation not actually cross validating. As always, my biggest focus was on data
AutoTS 0.2.0 Release Read More »
