f-stack
F-Stack is an user space network development kit with high performance based on DPDK, FreeBSD TCP/IP stack and coroutine API. (by F-Stack)
onnx
Open standard for machine learning interoperability (by onnx)
f-stack | onnx | |
---|---|---|
3 | 38 | |
3,726 | 16,858 | |
0.6% | 1.0% | |
7.5 | 9.5 | |
14 days ago | 6 days ago | |
C | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
f-stack
Posts with mentions or reviews of f-stack.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-10.
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Coroutine made DPDK dev easy
So, we try to use Photon coroutine lib to simplify the development of DPDK applications with the new concurrency model, and provide more functionalities, such as lock, timer and file I/O. First of all, we need to choose a userspace network protocol stack. After investigation, we have chosen Tencent's open source F-Stack project, which has ported the entire FreeBSD 11.0 network protocol stack on top of DPDK. It also has made some code cuts, providing a set of POSIX APIs, such as socket, epoll, kqueue, etc. Of course, its epoll is also simulated by kqueue, since it is essentially FreeBSD.
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Production Twitter on One Machine: 100Gbps NICs and NVMe Are Fast
I agree most HTTP server benchmarks are highly misleading in that way, and mention in my post how disappointed I am at the lack of good benchmarks. I also agree that typical HTTP servers would fall over at much lower new connection loads.
I'm talking about a hypothetical HTTPS server that used optimized kernel-bypass networking. Here's a kernel-bypass HTTP server benchmarked doing 50k new connections per core second while re-using nginx code: https://github.com/F-Stack/f-stack. But I don't know of anyone who's done something similar with HTTPS support.
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To all C++ professionals, can you state what field you're working in? Is it a niche?
Software for Internet Service Providers. The current project is based on DPDK, on top of it we use modified version of F-stack and then our application logic. There is some application logic "under" the F-stack too.
onnx
Posts with mentions or reviews of onnx.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-01.
- Onyx, a new programming language powered by WebAssembly
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From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.
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Object detection with ONNX, Pipeless and a YOLO model
ONNX is an open format from the Linux Foundation to represent machine learning models. It is becoming extensively adopted by the Machine Learning community and is compatible with most of the machine learning frameworks like PyTorch, TensorFlow, etc. Converting a model between any of those formats and ONNX is really simple and can be done in most cases with a single command.
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38TB of data accidentally exposed by Microsoft AI researchers
ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.
[0] https://github.com/onnx/onnx
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Reddit’s LLM text model for Ads Safety
Running inference for large models on CPU is not a new problem and fortunately there has been great development in many different optimization frameworks for speeding up matrix and tensor computations on CPU. We explored multiple optimization frameworks and methods to improve latency, namely TorchScript, BetterTransformer and ONNX.
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Operationalize TensorFlow Models With ML.NET
ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.
[0] https://onnx.ai
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Does ONNX Runtime not support Double/float64?
It's not clear why you thing this sub is appropriate for some third party system with a Python interface. Why don't you try their discussion group: https://github.com/onnx/onnx/discussions
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Async behaviour in python web frameworks
This kind of indirection through standardisation is pretty common to make compatibility between different kinds of software components easier. Some other good examples are the LSP project from Microsoft and ONNX to represent machine learning models. The first provides a standard so that IDEs don't have to re-invent the weel for every programming language. The latter decouples training frameworks from inference frameworks. Going back to WSGI, you can find a pretty extensive rationale for the WSGI standard here if interested.
- Pickle safety in Python