hamilton
llrt
hamilton | llrt | |
---|---|---|
26 | 5 | |
878 | 0 | |
- | - | |
8.1 | 0.0 | |
about 1 year ago | almost 2 years ago | |
Python | C++ | |
BSD 3-clause Clear License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hamilton
-
Write production grade pandas (and other libraries!) with Hamilton
And find the repository here: https://github.com/dagworks-inc/hamilton/
-
Useful libraries for data engineering in various programming languages
Python - https://github.com/stitchfix/hamilton (author here). It's great if you want your code to be always unit testable and documentation friendly, and you want to be able to visualize execution. Blog post on using it with Pandas https://link.medium.com/XhyYD9BAntb.
-
Cognitive Loads in Programming
Yes! As one of the creators of https://github.com/stitchfix/hamilton this was one of the aims. Simplifying the cognitive burden for those developing and managing data transforms over the course of years, and in particular for ones they didn't write!
For example in Hamilton -- we force people to write "declarative functions" which then are stitched together to create a dataflow.
E.g. example function -- my guess is that you can read and understand/guess what it does very easily.
-
Prefect vs other things question
For (1) there are quite a few options - prefect is one, metaflow is another, airflow, dagster, even https://github.com/stitchfix/hamilton (core contributor here), etc.
-
Field Lineage
If you're want to do more python https://github.com/stitchfix/hamilton allows you to model dependencies at a columnar (field) level.
- Show HN
-
[D] Is anyone working on interesting ML libraries and looking for contributors?
Take a look at https://github.com/stitchfix/hamilton - we're after contributors who can help us grow the project, e.g. make documentation great, dog fooding features and suggesting/contributing usability improvements.
-
Useful Python decorators for Data Scientists
For a real world example of their power, we built an entire framework (https://github.com/stitchfix/hamilton) at Stitch Fix, where a lot of cool magic is provide via decorators - see https://hamilton-docs.gitbook.io/docs/reference/api-reference/available-decorators and these two source files (https://github.com/stitchfix/hamilton/blob/main/hamilton/function_modifiers_base.py, https://github.com/stitchfix/hamilton/blob/main/hamilton/function_modifiers.py ). Note we do some non-trivial stuff via them.
-
unit tests
For data processing/transform code, I would recommend looking at https://github.com/stitchfix/hamilton, especially if you're trying to test pandas code. Short getting started here - https://towardsdatascience.com/how-to-use-hamilton-with-pandas-in-5-minutes-89f63e5af8f5 (disclaimer: I'm one of the authors).
-
Dealing with hundreds of customer/computed columns
The python package, hamilton, from Stitch Fix (https://hamilton-docs.gitbook.io/docs/) can help manage transformations on pandas dataframes. This DAG of transformations is managed separately in a file - so it can be versioned, in case the transformations change. The memory required is reduced, because only the API call tables and mapping parameter table have to be in memory. The calculated columns can be produced as needed. Just like dbt, transformations are separate from the source tables - but hamilton can be used on any python object - not just dataframes. dbt is SQL based.
llrt
-
Connectionist simulation tool
Local learning rule tensors: https://github.com/bparkis/llrt
-
Framework for investigating local neuron learning rules
Local learning rule tensors: https://github.com/bparkis/llrt It's a framework to allow you to flexibly specify how an individual neuron works, such as its learning rule, and then connect many of these neurons together in connectivity patterns such as Dense or Local2D. Unlike other ML frameworks, the connectivity pattern can be applied independently from the neuron behavior. So, you can easily test a proposed local learning rule across a variety of architectures.
- Framework for investigating neuron local learning rules
-
[D] Is anyone working on interesting ML libraries and looking for contributors?
See if https://github.com/bparkis/llrt interests you.
-
[Project] Framework for testing out different local learning rules
I made this project: https://github.com/bparkis/llrt It is a framework to allow you to flexibly specify how an individual neuron works, such as its learning rule, and then connect many of these neurons together in connectivity patterns such as Dense or Local2D. Unlike other ML frameworks, the connectivity pattern can be applied independently from the neuron behavior.
What are some alternatives?
prosto - Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby
composer - Supercharge Your Model Training
versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.
grouphug - Multi-task modelling extensions for huggingface transformers
plumbing - Prismatic's Clojure(Script) utility belt
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
OpenLineage - An Open Standard for lineage metadata collection
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
codetour - VS Code extension that allows you to record and play back guided tours of codebases, directly within the editor.
datahub - The Metadata Platform for your Data Stack
fn_graph - Lightweight function pipelines for Python