flyte
Taskflow
flyte | Taskflow | |
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31 | 24 | |
4,853 | 9,608 | |
3.8% | 1.6% | |
9.8 | 7.9 | |
7 days ago | 14 days ago | |
Go | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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.
flyte
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First 15 Open Source Advent projects
9. Flyte by Union AI | Github | tutorial
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Flyte 1.10: Self-hosted solution to build production-grade data and ML pipelines; now ships with monorepo, new agents and sensors, eager workflows and more ๐ (4.1k stars on GitHub)
GitHub: https://github.com/flyteorg/flyte
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Flyte: Open-source orchestrator for building production-grade ML pipelines
This is actually but a link to Flyte, this is a link to the documentation for the Flyte integration in LangChain, a separate product.
Flyte's homepage is https://flyte.org/
- Flyte: Advanced workflow orchestration alternative to Apache Airflow
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Orchestration: Thoughts on Dagster, Airflow and Prefect?
Anyone tried Flyte?
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Flyte 1.6.0: Self-hosted solution to build production-grade data and ML pipelines; now ships with PyTorch elastic training, image specification without dockerfile, enhanced task execution insights and more ๐ (3.4k stars on GitHub)
Website: https://flyte.org/
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Flyte(v1.5.0) - Self-hosted solution to build production-grade data and ML pipelines; now ships with streaming support, pod templates, partial tasks and more ๐ (3.2k stars on GitHub)
Flyte is an open source orchestration tool for managing the workflow of machine learning and AI projects. It runs on top of Kubernetes.
- Flyte: Open-Source Kubernetes-Native ML Orchestrator Implemented in Go
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What is MLOps and how to get started? | MLOps series | Deploying ML in production
I have a question though, what is your opinion on https://flyte.org. My pipeline uses this and itโll be interesting to get your perspectives on itโs capabilities.
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Github alternative for ML?
Have you looked at flyte.org. It aims to bring "versioning", "compute" and "reproducibility" together in one package.
Taskflow
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Improvements of Clojure in his time
For parallel programming nowadays, personally I reach for C++ Taskflow when I really care about performance, or a mix of core.async and running multiple load balanced instances when Iโm doing more traditional web backend stuff in Clojure.
- Taskflow: A General-Purpose Parallel and Heterogeneous Task Programming System
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How to go from intermediate to advance in C++?
Also, you can take a look to good libraries. The problem is that very often libraries are heavily templated, so It could be hard. For example, I like the style of the Taskflow library, I think is very clear, is relatively small, while makes use of more advanced techniques: https://github.com/taskflow/taskflow
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gcl v1.1 released - Graph Concurrent Library for C++
Cool. Thanks! How does it compare to taskflow?
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std::execution from the metal up - Paul Bendixen - Meeting C++ 2022
I've not seen yet, but it's been a bit since I looked last, any evidence of being able to build a computation graph and "save" it to re-run on new inputs. Something like https://github.com/taskflow/taskflow
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Proper abstraction for this?
It seems you're describing something a generic parallel task framework. Check taskflow for a production ready example https://github.com/taskflow/taskflow/blob/master/
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That one technology, question, or skill you never learned, and now you are haunted by during every new job conversation...
- https://github.com/taskflow/taskflow (I recommend to learn it first since its API and documentation are excellent)
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Parallel Computations in C++: Where Do I Begin?
If you want some sort of "job" system, where you submit items to a some sort of queue to be processed in parallel, try searching for a thread pool - there isn't one in the standard library, but there's about a million implementations online. There are more complicated versions of that idea, that describe computation as a directed acyclic graph, such as taskflow.
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High level overview of my custom game engine
The tooling decisions affect engine design though. For example if you want to have visual representation of job graph as it happened in specific frame of interest you need to pass the information around about job relationships and output it to a tool of choice. For example see https://github.com/taskflow/taskflow
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Is there any good reason not to build an open-source C++ project on Intels oneTBB?
I am aware of DAGs of task based threading library like Taskflow and HPX however the benefit they have is not obvious to me, as the following sequential section depends on the parallel part being completed fully. If you want to suggest elaboration on the benefits of this approach would be welcome.
What are some alternatives?
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
tbb - oneAPI Threading Building Blocks (oneTBB) [Moved to: https://github.com/oneapi-src/oneTBB]
argo - Workflow Engine for Kubernetes
tensorflow - An Open Source Machine Learning Framework for Everyone
temporal - Temporal service
HPX - The C++ Standard Library for Parallelism and Concurrency
kubeflow - Machine Learning Toolkit for Kubernetes
C++ Actor Framework - An Open Source Implementation of the Actor Model in C++
Celery-Kubernetes-Operator - An operator to manage celery clusters on Kubernetes (Work in Progress)
entt - Gaming meets modern C++ - a fast and reliable entity component system (ECS) and much more
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.
libunifex - Unified Executors