hamilton VS llrt

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.

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.

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].

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.

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:

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.)

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).

Otherwise, I'm biased here, but check out https://github.com/dagworks-inc/hamilton - it could be your universal layer that expresses how things should flow, that is orchestration system agnostic, which would make it easy to migrate between systems easily.

FROM --platform=arm64 busybox WORKDIR /var/task/ COPY app.mjs ./ ADD https://github.com/awslabs/llrt/releases/latest/download/llrt-container-arm64 /usr/bin/llrt RUN chmod +x /usr/bin/llrt ENV LAMBDA_HANDLER "app.handler" CMD [ "llrt" ]

Let’s get the simplest use case out of the way: cases where the cold starts are so fast that it’s not an issue for you. That’s usually the case for function that use runtimes such as C++, Go, Rust, and LLRT. However, you must follow the best practices and optimizations in every runtime to maintain a low impact cold start.

But compared to other runtimes, LLRT is not so good in terms of performance when it comes to dealing with large data processing, Monte Carlo simulations, or performing tasks with a large number of iterations. The AWS team says that it is best suited for working with smaller Serverless functions dedicated to tasks such as data transformation, real-time processing, AWS service integrations, authorization, validation, etc. Visit the GitHub repository of this project to learn more information.

And lastly, lean into your cloud vendor. Stop trying to build a better mouse trap. Advances in technology are happening all the time. The speed of AWS' Lambda has been rapidly improving over the past couple of years with the launch of things like SnapStart and LLRT

It seems they just added the mention to QuickJS, I assume, based on your feedback:

https://github.com/awslabs/llrt/commit/054aefc4d8486f738ed3a...

Props to them on the quick fix!