wave
metaflow
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wave | metaflow | |
---|---|---|
21 | 24 | |
3,860 | 7,586 | |
1.2% | 2.5% | |
9.2 | 9.2 | |
3 days ago | about 20 hours ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
wave
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Streamlit alternatives but for Rust?
https://streamlit.io/ https://wave.h2o.ai/ https://reflex.dev/
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Launch HN: Pynecone (YC W23) – Web Apps in Pure Python
Looks similar to Nitro https://nitro.h2o.ai/ and Wave https://wave.h2o.ai/ - both open source. Nitro already works with WebAssembly via Pyodide. (Author here)
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Nice GUI
To write web gui in Python, there are some other open source alternatives.
If just want to port simple shell interactive interface to web gui, can check https://github.com/pywebio/PyWebIO
If want to get a production level dashboard by using Python, https://wave.h2o.ai/ <>
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Show HN: Hstream – quick Python web apps (Streamlit alternative using Htmx)
I think the demo site may have been hugged to death. It's not rendering anything for me.
I think it's also worth giving a shoutout to wave in this space; they have quite a few components you can use out-of-the-box https://github.com/h2oai/wave
- PyScript
- Realtime Web Apps and Dashboards for Python
- Realtime Web Apps and Dashboards for Python and R
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[D] What do you use to build UIs for your projects?
H2O Wave : https://wave.h2o.ai
- Creating a web app in Python without knowledge of HTML/CSS/JavaScript
metaflow
- FLaNK Stack 05 Feb 2024
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
- In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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Needs advice for choosing tools for my team. We use AWS.
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.
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Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
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[OC] Gender diversity in Tech companies
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).
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Model artifacts mess and how to deal with it?
Check out Metaflow by Netflix
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Going to Production with Github Actions, Metaflow and AWS SageMaker
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.
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Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
What are some alternatives?
streamlit - Streamlit — A faster way to build and share data apps.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
reactpy - It's React, but in Python
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
nicegui - Create web-based user interfaces with Python. The nice way.
kedro-great - The easiest way to integrate Kedro and Great Expectations
pglet - Pglet - build internal web apps quickly in the language you already know!
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
dephell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump version.
dvc - 🦉 ML Experiments and Data Management with Git