hamilton
vscode-reactive-jupyter
hamilton | vscode-reactive-jupyter | |
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24 | 2 | |
2,007 | 3 | |
3.1% | - | |
9.7 | 9.0 | |
5 days ago | 11 months ago | |
Jupyter Notebook | TypeScript | |
BSD 3-clause Clear License | GNU General Public License v3.0 or later |
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hamilton
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Show HN: I built an open-source data pipeline tool in Go
I always thought Hamilton [1] does a good job of giving enough visual hooks that draw you in.
I also noticed this pattern where library authors sometimes do a bit extra in terms of discussing and even promoting their competitors, and it makes me trust them more. A “heres why ours is better and everyone else sucks …” section always comes across as the infomercial character who is having quite a hard time peeling an apple to the point you wonder if this the first time they’ve used hands.
One thing wish for is a tool that’s essentially just Celery that doesn’t require a message broker (and can just use a database), and which is supported on Windows. There’s always a handful of edge cases where we’re pulling data from an old 32-bit system on Windows. And basically every system has some not-quite-ergonomic workaround that’s as much work as if you’d just built it yourself.
It seems like it’s just sending a JSON message over a queue or HTTP API and the worker receives it and runs the task. Maybe it’s way harder than I’m envisioning (but I don’t think so because I’ve already written most of it).
I guess that’s one thing I’m not clear on with Bruin, can I run workers if different physical locations and have them carry out the tasks in the right order? Or is this more of a centralized thing (meaning even if its K8s or Dask or Ray, those are all run in a cluster which happens to be distributed, but they’re all machines sitting in the same subnet, which isn’t the definition of a “distributed task” I’m going for.
[1] https://github.com/DAGWorks-Inc/hamilton
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Greppability is an underrated code metric
Yep. When I was designing https://github.com/dagworks-inc/hamilton part of the idea was to make it easy to understand what and where. That is, enable one to grep for function definitions and their downstream use easily, and where people can't screw this up. You'd be surprised how easy it is to make a code base where grep doesn't help you all that much (at least in the python data transform world) ...
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Ask HN: What are you working on (August 2024)?
Graph-based libraries for building ML/AI systems:
- Burr -- build AI applications/agents as state machines https://github.com/dagworks-inc/burr
- Hamilton -- build dataflows as DAGs: https://github.com/dagworks-inc/hamilton
Looking for feedback -- we had some good initial traction on HN, and are looking for OS users/contributors/people who are building complimentary tooling!
- 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
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?
phidata - Agno is a lightweight framework for building multi-modal Agents [Moved to: https://github.com/agno-agi/agno]
gather - Spit shine for Jupyter notebooks 🧽✨
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
TypeCell
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
nodebook - Repeatable analysis plugin for Jupyter notebook
modelfusion - The TypeScript library for building AI applications.
jupyter-cache - A defined interface for working with a cache of executed jupyter notebooks
aipl - Array-Inspired Pipeline Language
Pluto.jl - 🎈 Simple reactive notebooks for Julia
snowpark-python - Snowflake Snowpark Python API
ipyflow - A reactive Python kernel for Jupyter notebooks.