msquic
hummingbird
Our great sponsors
msquic | hummingbird | |
---|---|---|
19 | 9 | |
3,833 | 3,302 | |
2.0% | 0.7% | |
9.6 | 7.1 | |
5 days ago | 10 days ago | |
C | Python | |
MIT License | MIT 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.
msquic
- Msquic: Cross-platform C implementation of QUIC protocol for C, C++, C#, Rust
-
Avoiding HTTP/3 (for a while) as a pragmatic default
I referred to sockets as an API design, not to express an opinion on whether you should place your protocol implementations inside or outside the kernel. (Although that’s undeniably an interesting question that by all rights should have been settled by now, but isn’t.)
Even then, I didn’t mean you should reproduce the Berkeley socket API verbatim (ZeroMQ-style); multiple streams per connection does not sound like a particularly good fit to it (although apparently people have managed to fit SCTP into it[1]?). I only meant that with the current mainstream libraries[2,3,4], establishing a QUIC connection and transmitting bytestreams or datagrams over it seems quite a bit more involved than performing the equivalent TCP actions using sockets.
[1] https://datatracker.ietf.org/doc/html/rfc6458
[2] https://quiche.googlesource.com/quiche
[3] https://github.com/microsoft/msquic
[4] https://github.com/litespeedtech/lsquic
-
My plan for making 256bit signed and unsigned integers in C. Please help me understand this concept better.
The documentation of MS QUIC says it is cross-platform, it should work on Linux, it has a CMake preset for Linux and you can download the prebuilt binary releases for Linux.
- Best performing quic implementation?
-
Show HN: Protect Your CI/CD from SolarWinds-Type Attacks with This Agent
Hello HN, my name is Varun, and I am the co-founder of StepSecurity. Here is the backstory about Harden-Runner. We thoroughly researched past software supply chain security incidents. The devastating breaches of SolarWinds, Codecov, and others, have one thing in common – they attacked the CI/ CD pipeline or the build server.
These incidents made it clear that a purpose-built security agent was needed for CI/ CD. While there are numerous agents available for desktops and servers, such as from CrowdStrike and Lacework, none have been tailored specifically to address the unique risks present in CI/CD pipelines.
With the understanding that a specialized solution was needed to secure CI/CD environments, we developed Harden-Runner, an open-source solution tailored specifically for GitHub Actions hosted runners. It can be seamlessly integrated into your workflow by simply adding a step. The agent installation process is also lightning-fast, taking no more than 5 seconds to complete.
Harden-Runner's security agent is designed to closely monitor all aspects of the workflow run, including DNS, network, file, and process events. This allows for real-time identification of any potential security breaches. To prevent incidents like the Codecov breach, where exfiltration of credentials occurred, Harden-Runner allows you to set policies that restrict outbound traffic at both the DNS and network layers. Additionally, we are actively working on implementing further restrictions at the application layer, such as using HTTP verbs and paths, to provide an even more comprehensive security solution.
An excellent example of how Harden-Runner effectively blocks outbound traffic can be found in the following link: https://app.stepsecurity.io/github/microsoft/msquic/actions/.... As you can see, all traffic to unauthorized endpoints is highlighted in red, indicating that it has been blocked; this is because these endpoints are not included in the allowed list defined in the GitHub Actions workflow file, which can be viewed here: https://github.com/microsoft/msquic/blob/aaecb0fac5a3902dd24....
One of the key features of Harden-Runner's monitoring capabilities is its ability to detect any tampering or alteration of files during the build process, similar to the SolarWinds incident. To further enhance security and protect against potential malicious tools or attempts to disable the agent, Harden-Runner includes a disable-sudo mode. This mode effectively disables the use of 'sudo' on the hosted runner, providing an additional layer of protection
Harden-Runner has already been adopted by over 600 open-source repositories: https://github.com/step-security/harden-runner/network/depen.... To fully understand the capabilities of Harden-Runner and how it can protect against past supply chain attacks, please try out our attack simulator GitHub repository at https://github.com/step-security/attack-simulator. I would love to hear your feedback.
-
Least painful path to multiplatform builds?
https://github.com/microsoft/msquic (QUIC / HTTP3)
-
msquic VS MsQuic.Net - a user suggested alternative
2 projects | 15 Jul 2022
- The Illustrated QUIC Connection
- Msquic - Cross-platform, C implementation of the IETF QUIC protocol.
hummingbird
- Treebomination: Convert a scikit-learn decision tree into a Keras model
-
[D] GPU-enabled scikit-learn
If are interested in just predictions you can try Hummingbird. It is part of the PyTorch ecosystem. We get already trained scikit-learn models and translate them into PyTorch models. From them you can run your model on any hardware support by PyTorch, export it into TVM, ONNX, etc. Performance on hardware acceleration is quite good (orders of magnitude better than scikit-learn is some cases)
-
Machine Learning with PyTorch and Scikit-Learn – The *New* Python ML Book
I think Rapids AI's cuML tried to go into this direction (essentially scikit-learn on the GPU): https://docs.rapids.ai/api/cuml/stable/api.html#logistic-reg.... For some reason it never took really off though.
Btw., going on a tangent, you might like Hummingbird (https://github.com/microsoft/hummingbird). It allows you trained scikit-learn tree-based models to PyTorch. I watched the SciPy talk last year, and it's a super smart & elegant idea.
-
Export and run models with ONNX
ONNX opens an avenue for direct inference using a number of languages and platforms. For example, a model could be run directly on Android to limit data sent to a third party service. ONNX is an exciting development with a lot of promise. Microsoft has also released Hummingbird which enables exporting traditional models (sklearn, decision trees, logistical regression..) to ONNX.
-
Supreme Court, in a 6–2 ruling in Google v. Oracle, concludes that Google’s use of Java API was a fair use of that material
And Python.
-
[D] Here are 3 ways to Speed Up Scikit-Learn - Any suggestions?
For inference, you can convert your models to other formats that support GPU acceleration. See Hummingbird https://github.com/microsoft/hummingbird
-
[D] Microsoft library, Hummingbird, compiles trained ML models into tensor computation for faster inference.
The surprising thing is that Hummingbird can be faster than the GPU implementation of LightGBM (and XGBoost) if you use tensor compilers such as TVM. [The paper](https://www.usenix.org/conference/osdi20/presentation/nakandala) describes our findings. We have also open sourced the [benchmark code](https://github.com/microsoft/hummingbird/tree/main/benchmarks) so you try yourself!
-
I learned about Microsoft's Hummingbird library today. 1000x performance??
I took their sample code from Github and tweaked it to spit out times for each model's prediction, as well as increase the number of rows to 5 million. I used Google's Colab and selected GPU for my hardware accelerator. This gives an option to run code on GPU, not that all computations will happen on the GPU.
What are some alternatives?
quiche - 🥧 Savoury implementation of the QUIC transport protocol and HTTP/3
onnx - Open standard for machine learning interoperability
lsquic - LiteSpeed QUIC and HTTP/3 Library
swift - The Swift Programming Language
quinn - Async-friendly QUIC implementation in Rust
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
openmptcprouter - OpenMPTCProuter is an open source solution to aggregate multiple internet connections using Multipath TCP (MPTCP) on OpenWrt
cuml - cuML - RAPIDS Machine Learning Library
shadowsocks-rust - A Rust port of shadowsocks
docker - Docker - the open-source application container engine
mvfst - An implementation of the QUIC transport protocol.
chemprop - Message Passing Neural Networks for Molecule Property Prediction