dns.toys
server
dns.toys | server | |
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29 | 24 | |
2,435 | 7,356 | |
- | 2.7% | |
4.7 | 9.5 | |
23 days ago | 6 days ago | |
Go | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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.
dns.toys
- FLaNK Weekly 08 Jan 2024
- DNS Toys
- Useful Utilities and Services over DNS
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Icanhazip: A simple IP address tool survived a deluge of users (2021)
In addition to the others, there is also https://www.dns.toys/
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Show HN: Use DNS TXT to share information
It's always amusing to see DNS "hackery"[1] like this, and always makes me go back to DNS Toys (https://www.dns.toys/), which generated a huge discussion on HN a year ago [2]
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[1] well, it's not really hackery if you're being pedantic, since it's doing what the spec allows it to do
[2] DNS Toys (946 points): https://news.ycombinator.com/item?id=31704789
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YouTube/Google/Gmail unreachable, however all other sites are? (No blocklists, all disabled) Unbound and Google = SERVFAIL, Unbound and everything else = works.
#1: Which of the below DNS Servers track user data & logs #2: Is there any reason to care about DNSSEC in 2022 as regards choice of registrar and DNS host? #3: Useful utilities and toys over DNS | 6 comments
- dns.toys: Useful utilities and services over DNS
- dns.toys
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Hacker News top posts: Jun 12, 2022
DNS Toys\ (83 comments)
- DNS query BOFH excuse generator for ShittySysadmin.com
server
- FLaNK Weekly 08 Jan 2024
- Is there any open source app to load a model and expose API like OpenAI?
- "A matching Triton is not available"
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best way to serve llama V2 (llama.cpp VS triton VS HF text generation inference)
I am wondering what is the best / most cost-efficient way to serve llama V2. - llama.cpp (is it production ready or just for playing around?) ? - Triton inference server ? - HF text generation inference ?
- Triton Inference Server - Backend
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Single RTX 3080 or two RTX 3060s for deep learning inference?
For inference of CNNs, memory should really not be an issue. If it is a software engineering problem, not a hardware issue. FP16 or Int8 for weights is fine and weight size won’t increase due to the high resolution. And during inference memory used for hidden layer tensors can be reused as soon as the last consumer layer has been processed. You likely using something that is designed for training for inference and that blows up the memory requirement, or if you are using TensorRT or something like that, you need to be careful to avoid that every tasks loads their own copy of the library code into the GPU. Maybe look at https://github.com/triton-inference-server/server
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Machine Learning Inference Server in Rust?
I am looking for something like [Triton Inference Server](https://github.com/triton-inference-server/server) or [TFX Serving](https://www.tensorflow.org/tfx/guide/serving), but in Rust. I came across [Orkon](https://github.com/vertexclique/orkhon) which seems to be dormant and a bunch of examples off of the [Awesome-Rust-MachineLearning](https://github.com/vaaaaanquish/Awesome-Rust-MachineLearning)
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Multi-model serving options
You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.
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I mean,.. we COULD just make our own lol
[1] https://docs.nvidia.com/launchpad/ai/chatbot/latest/chatbot-triton-overview.html[2] https://github.com/triton-inference-server/server[3] https://neptune.ai/blog/deploying-ml-models-on-gpu-with-kyle-morris[4] https://thechief.io/c/editorial/comparison-cloud-gpu-providers/[5] https://geekflare.com/best-cloud-gpu-platforms/
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Why TensorFlow for Python is dying a slow death
"TensorFlow has the better deployment infrastructure"
Tensorflow Serving is nice in that it's so tightly integrated with Tensorflow. As usual that goes both ways. It's so tightly coupled to Tensorflow if the mlops side of the solution is using Tensorflow Serving you're going to get "trapped" in the Tensorflow ecosystem (essentially).
For pytorch models (and just about anything else) I've been really enjoying Nvidia Triton Server[0]. Of course it further entrenches Nvidia and CUDA in the space (although you can execute models CPU only) but for a deployment today and the foreseeable future you're almost certainly going to be using a CUDA stack anyway.
Triton Server is very impressive and I'm always surprised to see how relatively niche it is.
[0] - https://github.com/triton-inference-server/server
What are some alternatives?
bunny1 - bunny1 is a tool that lets you write smart bookmarks in python and then share them across all your browsers and with a group of people or the whole world. It was developed at Facebook and is widely used there.
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
iodine - Official git repo for iodine dns tunnel
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
vytal-extension - Browser extension to spoof timezone, geolocation, locale and user agent.
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
android_kernel_oneplus_sm8250
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
kittendns
Triton - Triton is a dynamic binary analysis library. Build your own program analysis tools, automate your reverse engineering, perform software verification or just emulate code.
bofh - BOFH excuse generator
Megatron-LM - Ongoing research training transformer models at scale