paradigm
flecs
paradigm | flecs | |
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9 | 48 | |
36 | 5,530 | |
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7.6 | 9.6 | |
11 months ago | 1 day ago | |
Python | C | |
Apache License 2.0 | MIT License |
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paradigm
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Deploying speech recognition models at scale
I built Paradigm from scratch to deploy any model at scale. It deploys the model on Kubernetes with load balancers. If you run into any issues, I'm happy to guide you on how to use it.
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Which is the best MLOps tool for getting started?
I started with paradigm. I got a deeper understanding about argo workflows through it as well. Helps to get a proper grab of industry standards from the beginning.
- What are some open-source ML pipeline managers that are easy to use?
- I use this OS tool to deploy LLMs on Kubernetes.
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Serving Scikit-Learn model on EC2 instance and Scaling
For scalability, it should be on Kubernetes. This is the best solution I have come across. You can deploy the model as a service with a LoadBalancer. You can refer to Kubernetes services or use a tool such as this one that handles building the service for you.
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Who wants to run ML pipelines on Kubernetes? This might be the simplest tool for the job.
I came across this tool today and checked it out, I feel this can get the job done very quickly without so many complex features. It is also very small in size, so does not take up a lot of space in the cluster as well.
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[P] I found the simplest tool to run ML pipelines on Kubernetes. Github link in comments.
Link - https://github.com/ParadigmAI/paradigm It seems to be a pretty new project. But this has high usability.
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Airflow + Slurm for ML Training Pipelines?
Prefect is a good choice, But I wanted a much simpler tool. Hence, I built a barebone workflow controller here.
flecs
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ECS, Finally
I've also been enjoying building My First Game™ in Bevy using ECS. The community around Bevy really shines, but Flecs (https://github.com/SanderMertens/flecs) is arguably a more mature, open-source ECS implementation. You don't get to write in Rust, though, which makes it less cool in my book :)
I'm not very proud of the code I've written because I've found writing a game to be much more confusing than building websites + backends, but, as the author notes, it certainly feels more elegant than OOP or globals given the context.
I'm building for WASM and Bevy's parallelism isn't supported in that context (yet? https://github.com/bevyengine/bevy/issues/4078), so the performance wins are just so-so. Sharing a thread with UI rendering suuucks.
If anyone wants to browse some code or ask questions, feel free! https://github.com/MeoMix/symbiants
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Databases are the endgame for data-oriented design
Flecs does just that: https://ajmmertens.medium.com/why-it-is-time-to-start-thinking-of-games-as-databases-e7971da33ac3
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What's your way to create an ECS?
I'm trying to optimize my workflow as much as possible, and came across this thing called an ECS. After doing a little bit more digging I found some decent guides on how you would make one, I also found one premade called FLECS. FLECS is nice and all, but I was looking for something more simple that just has the bare bones of what I need and is also configurable. I haven't been able to really find anything like that, so I was wondering if anyone had an example of maybe their way of implementing an ECS. I know how to go about it, but I'm unsure of exactly what the code would look like.
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Introducing Ecsact
Since we wanted a common game simulation that would be on both the server and the client we looked into a few libraries that would fit our ECS needs. It was decided we were going to write this common part of our game in C++, but rust was considered. C++ was a familiar language for us so naturally EnTT and flecs came up right away. I had used EnTT before, writing some small demo projects, so our choice was made based on familiarity. In order to integrate with Unity we created a small C interface to communicate between our simulation code and Unity’s C#. Here’s close to what it looked like. I removed some parts for brevity sake.
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Prolog for future AI
Repository: https://github.com/SanderMertens/flecs
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An in-game query engine heavily inspired by prolog
This is the project: https://github.com/SanderMertens/flecs (query engine implementation lives here: https://github.com/SanderMertens/flecs/tree/master/src/addons/rules)
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What are the limits of blueprints?
There's also a performance question. While we can now use Blueprint nativization to convert Blueprints to C++ the result will be a fairly naive version, fast enough for most purposes but not if you're trying to push every bit of performance. This is where you're looking at making sure you're hitting things such as using the CPU cache as well as possible for an ECS system (Look at ENTT or Flecs if you want to see what they're about and why you'd want one), or a system needing to process massive amounts of data quickly such as the Voxel Plugin.
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What's the hot tech stack these days?
If I knew C++ and I'd heard about it before I started my current project, I would have been tempted to use this https://github.com/SanderMertens/flecs which can be built to WASM. Of course you still need JavaScript in the front end to link to the WASM part. I've recently been using esbuild to bundle my front end code, which does a pretty similar job to webpack, but is a bit faster.
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Bevy and WebGPU
When do think bevy will support entity-entity relationships ? https://github.com/bevyengine/bevy/issues/3742.
Flecs ECS already supports this: https://github.com/SanderMertens/flecs/blob/master/docs/Rela...
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any resources for expanding on ECS?
For a modern engine you’re probably best looking at Unity’s DOTS. You may also want to check out some of the different open source ECS libraries such as flecs and EnTT are two popular ones for C++, but there’s lots of them. Largely you’ll see lots of different approaches taken, all with their own pros and cons. Not all of them will be performant (some focus more on the design benefits) while others will be optimised for certain use cases. What you should prioritise will depend on your specific needs.
What are some alternatives?
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
entt - Gaming meets modern C++ - a fast and reliable entity component system (ECS) and much more
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
JUCE - JUCE is an open-source cross-platform C++ application framework for desktop and mobile applications, including VST, VST3, AU, AUv3, LV2 and AAX audio plug-ins.
aws-sfn-resume-from-any-state - Resume failed state machines midstream and skip all previously succeded steps.
Boost - Super-project for modularized Boost
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
SDL - DEPRECATED: Official development moved to GitHub
dagster - An orchestration platform for the development, production, and observation of data assets.
Folly - An open-source C++ library developed and used at Facebook.
wenet - Production First and Production Ready End-to-End Speech Recognition Toolkit
Seastar - High performance server-side application framework