gRPC
awesome-machine-learning
gRPC | awesome-machine-learning | |
---|---|---|
11 | 12 | |
11,180 | 63,555 | |
0.6% | - | |
9.6 | 7.6 | |
3 days ago | 10 days ago | |
Java | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
gRPC
- FLaNK Stack Weekly 12 February 2024
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Reference Count, Don't Garbage Collect
That's not true at all. Case in point In general, this is not a problem that AGC can solve. The language can help (something Java is admittedly particularly bad at) but even so, there'll always be avenues for leaks. That's just the nature of shared things. Interestingly, in the linked grpc case, the leaked memory is only half the problem -- AGC doesn't help at all with the leaked HTTP2 connection.
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Distroless Alpine
I've trialled my new image with an existing project via JLink that's heavy on Netty and gRPC the image works great (with a small tweak to exclude grpc-netty-shaded due to grpc-java#9083).
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What are the user agents?
When developing an application, the vast majority of code is written by other people. We import that code and make use of it to get whatever we need done. In this case, the developer of an various android applications are using grpc-java.
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Buf raises $93M to deprecate REST/JSON
`proto_library` for building the `.bin` file from protos works great. Generating stubs/messages for "all" languages does not. Each language does not want to implement gRPC rules, the gRPC team does not want to implement rules for each language. Sort of a deadlock situation. For example:
- C++: https://github.com/grpc/grpc/blob/master/bazel/cc_grpc_libra...
- Python: https://github.com/grpc/grpc/blob/master/bazel/python_rules....
- ObjC: https://github.com/grpc/grpc/blob/master/bazel/objc_grpc_lib...
- Java: https://github.com/grpc/grpc-java/blob/master/java_grpc_libr...
- Go (different semantics than all of the other): https://github.com/bazelbuild/rules_go/blob/master/proto/def...
But there's also no real cohesion within the community. The biggest effort to date has been in https://github.com/stackb/rules_proto which integrates with gazelle.
tl;dr: Low alignment results in diverging implementations that are complicated to understand for newcomers. Buff's approach is much more appealing as it's a "this is the one way to do the right thing" and having it just work by detecting `proto_library` and doing all of the linting/registry stuff automagically in CI would be fantastic.
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grpc_bench: open-source, objective gRPC benchmark
Small clarification (to my understanding, I'm not a Java Guru) on why Java got on top - those Java implementations use something called Direct Executor. It's super performant when there's no chance of a blocking operation. But if you are to do anything more than echo service, you might be in trouble. Other implementations probably don't suffer from the same constraint. The related discussion can be found in this PR.
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Android Java GRPC Tutorial
clone https://github.com/grpc/grpc-java
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GRPC
If you do streaming then the best option would be to use a so called manual flow control. You can find an example here.
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High performing APIs with gRPC
Another interesting link is their official grpc-java benchmarks project, which is also used in the benchmark I've posted you.
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Java 16 EA Alpine & JLink vs Graal
Both JLink (gRPC#3522) and Graal have some issues; I'm especially concerned about the Serial GC in Graal so will be putting that under some stress soon to see if that confirms my suspicions. I'll also be good when some Java 16 JRE Alpine images appear as the JDK is too bloaty.
awesome-machine-learning
- FLaNK Stack Weekly 12 February 2024
- Good coding groups for black women?
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Recent Google layoffs and Go
Nope! Here’s a link for more info —-> https://github.com/josephmisiti/awesome-machine-learning
- A python program that adds ~700 Results to Watch Later or YouTube Playlist; or, if on Google, bookmarks or Reading List
- I want to learn more about AI and Machine Learning
- Complete beginner looking for a proper starting point
- GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.
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✨ 5 Best GitHub Repositories to Learn Machine Learning in 2022 for Free 💯
2️⃣ Awesome Machine Learning
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How to Use the Maven Shade Plugin if Your Project Uses Java Platform Module System
Chips-n-Salsa
- How can I get into AI development?
What are some alternatives?
Dubbo - The java implementation of Apache Dubbo. An RPC and microservice framework.
tails - This is the Tails composer package for Laravel. Easily fetch designs in your Laravel application that you design inside of the Tails Site/Page Builder.
Netty - Netty project - an event-driven asynchronous network application framework
DALLE-mtf - Open-AI's DALL-E for large scale training in mesh-tensorflow.
Finagle - A fault tolerant, protocol-agnostic RPC system
100-Days-Of-ML-Code - 100 Days of ML Coding
OkHttp - Square’s meticulous HTTP client for the JVM, Android, and GraalVM.
Machine-Learning-Algorithms - All Machine Learning Algorithms
Undertow - High performance non-blocking webserver
500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code - 500 AI Machine learning Deep learning Computer vision NLP Projects with code
KryoNet - TCP/UDP client/server library for Java, based on Kryo
Face-Recognition-One-Shot-Learning- - This project is based on Siamese Neural Networks & Triplet Loss Functions