hamilton
vscode-reactive-jupyter
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hamilton | vscode-reactive-jupyter | |
---|---|---|
20 | 2 | |
1,312 | 0 | |
8.2% | - | |
9.8 | 9.0 | |
6 days ago | about 1 month ago | |
Jupyter Notebook | TypeScript | |
BSD 3-clause Clear License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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hamilton
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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.)
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Data lineage
Most people don't track lineage because it's difficult (though if you use something like https://github.com/DAGWorks-Inc/hamilton to write your pipeline - author here - it can come almost for free).
vscode-reactive-jupyter
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Show HN: Marimo – an open-source reactive notebook for Python
Wow.. Really great work, finally someone is doing it!
Since I've thought about this for a long time (I've actually even made a very simplified version last year [1]), I want to contribute a few thoughts:
- cool that you have a Vscode extension, but I was a little disappointed that it opens a full browser view instead of using the existing, good Notebook interface of Vscode. (I get you want to show the whole Frontend- But I'd love to be able to run the Reactive Kernel within the full Vscode ecosystem.. Included Github Copilot is cool, but that's not all)
- As other comments said, if you want to go for reproducibility, the part about Package Management is very important. And it's also mostly solved, with Poetry etc...
- If you want to go for easy deployment of the NB code to Production, another very cool feature would be to extract (as a script) all the code needed to produce a given cell of output! This should be very easy since you already have the DAG.. It actually even existed at some point in VSCode Python extension, then they removed it
Again, great job
[1] https://github.com/micoloth/vscode-reactive-jupyter
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IPyflow: Reactive Python Notebooks in Jupyter(Lab)
Crazy seeing this here!
I searched for this last week, as I'm playing with building the same thing but as a VSCode extension.. See here [1]
I found another similar project on Github, but it was from many years ago. Yours did not turn up..
Very interested in finding out how you implemented it
[1] https://github.com/micoloth/vscode-reactive-jupyter#readme
What are some alternatives?
dagster - An orchestration platform for the development, production, and observation of data assets.
Pluto.jl - 🎈 Simple reactive notebooks for Julia
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
nodebook - Repeatable analysis plugin for Jupyter notebook
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.
gather - Spit shine for Jupyter notebooks 🧽✨
snowpark-python - Snowflake Snowpark Python API
ipyflow - A reactive Python kernel for Jupyter notebooks.
aipl - Array-Inspired Pipeline Language
phidata - Build AI Assistants with memory, knowledge and tools.
modelfusion - The TypeScript library for building AI applications.
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin