hamilton
py-shiny
hamilton | py-shiny | |
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21 | 29 | |
1,321 | 979 | |
3.7% | 6.8% | |
9.8 | 9.7 | |
6 days ago | 1 day ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | MIT License |
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
- Show HN: Hamilton's UI – observability, lineage, and catalog for data pipelines
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Building an Email Assistant Application with Burr
Note that this uses simple OpenAI calls — you can replace this with Langchain, LlamaIndex, Hamilton (or something else) if you prefer more abstraction, and delegate to whatever LLM you like to use. And, you should probably use something a little more concrete (E.G. instructor) to guarantee output shape.
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Using IPython Jupyter Magic commands to improve the notebook experience
In this post, we’ll show how your team can turn any utility function(s) into reusable IPython Jupyter magics for a better notebook experience. As an example, we’ll use Hamilton, my open source library, to motivate the creation of a magic that facilitates better development ergonomics for using it. You needn’t know what Hamilton is to understand this post.
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FastUI: Build Better UIs Faster
We built an app with it -- https://blog.dagworks.io/p/building-a-lightweight-experiment. You can see the code here https://github.com/DAGWorks-Inc/hamilton/blob/main/hamilton/....
Usually we've been prototyping with streamlit, but found that at times to be clunky. FastUI still has rough edges, but we made it work for our lightweight app.
- Show HN: On Garbage Collection and Memory Optimization in Hamilton
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Facebook Prophet: library for generating forecasts from any time series data
This library is old news? Is there anything new that they've added that's noteworthy to take it for another spin?
[disclaimer I'm a maintainer of Hamilton] Otherwise FYI Prophet gels well with https://github.com/DAGWorks-Inc/hamilton for setting up your features and dataset for fitting & prediction[/disclaimer].
- Show HN: Declarative Spark Transformations with Hamilton
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Langchain Is Pointless
I had been hearing these pains from Langchain users for quite a while. Suffice to say I think:
1. too many layers of OO abstractions are a liability in production contexts. I'm biased, but a more functional approach is a better way to model what's going on. It's easier to test, wrap a function with concerns, and therefore reason about.
2. as fast as the field is moving, the layers of abstractions actually hurt your ability to customize without really diving into the details of the framework, or requiring you to step outside it -- in which case, why use it?
Otherwise I definitely love the small amount of code you need to write to get an LLM application up with Langchain. However you read code more often than you write it, in which case this brevity is a trade-off. Would you prefer to reduce your time debugging a production outage? or building the application? There's no right answer, other than "it depends".
To that end - we've come up with a post showing how one might use Hamilton (https://github.com/dagWorks-Inc/hamilton) to easily create a workflow to ingest data into a vector database that I think has a great production story. https://open.substack.com/pub/dagworks/p/building-a-maintain...
Note: Hamilton can cover your MLOps as well as LLMOps needs; you'll invariably be connecting LLM applications with traditional data/ML pipelines because LLMs don't solve everything -- but that's a post for another day.
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Free access to beta product I'm building that I'd love feedback on
This is me. I drive an open source library Hamilton that people doing time-series/ML work love to use. I'm building a paid product around it at DAGWorks, and I'm after feedback on our current version. Can I entice anyone to:
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IPyflow: Reactive Python Notebooks in Jupyter(Lab)
From a nuts and bolts perspective, I've been thinking of building some reactivity on top of https://github.com/dagworks-inc/hamilton (author here) that could get at this. (If you have a use case that could be documented, I'd appreciate it.)
py-shiny
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Designing a Pure Python Web Framework
I really like this idea of using Python to create both the frontend and backend. Another lib doing this is https://solara.dev/ . Something I particularly like about Solara is that you can interactively build your app in a Jupyter Notebook, since behind the scenes it's using ipywidgets.
Has anyone compared Solara and Reflex and can comment on pros/cons? Are there other options in this space? Maybe https://shiny.posit.co/py/ ?
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FastUI: Build Better UIs Faster
Would you consider giving Shiny (for Python) a try? https://shiny.posit.co/py/ It's (I hope) pretty close to Streamlit in ease of use for getting started, but reactive programming runs all the way through it. The kind of app you're talking about are extremely natural to write in Shiny, you don't have to keep track of state yourself at all.
If you decide to give it a try and have trouble, please email me (email in profile) or drop by the Discord (https://discord.gg/yMGCamUMnS).
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py-shiny VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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Duckdb + Shiny for Python example
Code is here: https://github.com/rstudio/py-shiny/tree/duckdb-example/examples/duckdb
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Transitioning from R to Python - any tips?
The equivalent of shiny in python is shiny for python: https://shiny.posit.co/py/
- Show HN: Mercury – convert Jupyter Notebooks to Web Apps without code rewriting
- Shiny for Python – building interactive web apps from Python
- Shiny – Web Pages in Python
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Tidyverse 2.0.0
I'm not sure how usable it is, but Shiny for Python exists: https://shiny.rstudio.com/py/
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Graphs in Python web app
There's Shiny for Python - originally for R - but it's only Alpha status: https://shiny.rstudio.com/py/
What are some alternatives?
dagster - An orchestration platform for the development, production, and observation of data assets.
Solara - A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
pyvibe - Generate styled HTML pages from Python
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Genie.jl - 🧞The highly productive Julia web framework
snowpark-python - Snowflake Snowpark Python API
Tidier.jl - Meta-package for data analysis in Julia, modeled after the R tidyverse.
aipl - Array-Inspired Pipeline Language
React - The library for web and native user interfaces.
vscode-reactive-jupyter - A simple Reactive Python Extension for Visual Studio Code
streamlit - Streamlit — A faster way to build and share data apps.