pytorch-forecasting VS pkgx

Source: Conversation with Bing, 4/5/2023 (1) jdb78/pytorch-forecasting: Time series forecasting with PyTorch - GitHub. https://github.com/jdb78/pytorch-forecasting. (2) Time Series Prediction with LSTM Using PyTorch - Colaboratory. https://colab.research.google.com/github/dlmacedo/starter-academic/blob/master/content/courses/deeplearning/notebooks/pytorch/Time_Series_Prediction_with_LSTM_Using_PyTorch.ipynb. (3) time-series-classification · GitHub Topics · GitHub. https://github.com/topics/time-series-classification. (4) PyTorch: Dataloader for time series task - Stack Overflow. https://stackoverflow.com/questions/57893415/pytorch-dataloader-for-time-series-task.

Temporal fusion transformer https://github.com/jdb78/pytorch-forecasting

Pytorch-forecasting

To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast

I'm a data trainee at this organisation. I wrote my master thesis about using an event clustering mechanism to enrich an existing dataset to improve short-term demand predictions, using Pytorch Forecasting using the temporal fusion transformer component, and LightGBM (and compare the models with and w/o the event feature, so 4 runs in total).

Darts is a pretty nice one. I've recently been using pytorch-forecasting for larger models like the Temporal Fusion Transformer. https://github.com/jdb78/pytorch-forecasting

I’m liking pkgx over asdf as it can activate project tooling upon cd’ing into a project folder.

https://pkgx.sh

I saw some alternatives being suggested and wanted to do the same (Also, so that I can look back at this item, through my comments :) ). Started using https://pkgx.sh/ lately. I know it has some baggage with tea.xyz and crypto, but it is also easy to get started with.

Secondly, our development environments must not drift, because then code may behave differently and a change could pass on our machine but fail in production. There are many tools for locking down environments, e.g nix, pkgx, asdf, containers, etc., and they all share the common goal of being able to lock down dependencies for an environment accurately and deterministically. And that needs to be enforced in our local workflow so we don't have to rely on CI environments for correctness. All developers must have environments that are effectively identical to what runs in CI (which itself should be representative of the production environment).

There are many ways this can be done (e.g nix, pkgx, asdf, containers, etc.), and we won’t get into which specific tools to use, because we'll instead cover the essential essence of preventing environment drift:

1. pkgx

Before diving in, it's a good idea to have a package manager handy, like tea. It'll handle your development environment and simplify your life!

Direct fast-track link to repo

checkout https://github.com/teaxyz/cli and https://github.com/teaxyz/pantry

Install is tea: sh <(curl https://tea.xyz) and