wasmer-java
server
wasmer-java | server | |
---|---|---|
8 | 24 | |
557 | 7,356 | |
0.0% | 2.7% | |
0.0 | 9.5 | |
almost 2 years ago | 3 days ago | |
Java | 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.
wasmer-java
- FLaNK Weekly 08 Jan 2024
- FLaNK Stack Weekly for 12 September 2023
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Announcing CheerpJ 3.0: a JVM replacement in HTML5 and WebAssembly to run Java applications (and applets) on modern browsers
Great idea! Luckily, there's https://github.com/wasmerio/wasmer-java, so you can easily run that wasm binary in your JVM running in your browser!
- Do goroutines typically run ontop of operating system threads?
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CC:Tweaked meets WASM
The biggest integration library is wasmer. It's intended as a server runtime for wasm. The official port wasmer-java has the problem of not supporting imports and that's also not to be implemented in the near future (they are restructuring the wasmer library according to this issue), the other is wasmer-jni. An inofficial jni-binding for the wasmer base implementation. Less nice to use, but feature complete.
- Since Clojure runs on the JVM and interops with Java, am I able to use wasmer to run web assembly modules from Clojure?
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Looking for Java Based RandomX Miner
Check how the rust compiler can target Web Assembly and then have a look at https://github.com/wasmerio/wasmer-java
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IOTA 1.5 Java client library
If your savvy with WASM I think you could compile to wasm and make java bindings with https://github.com/wasmerio/wasmer-java
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?
webassembly-wasi-experiments - Discover WebAssembly System Interface (WASI) with C/Rust use cases
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
GoJavaWasm - A Java project for running Go(lang)'s WebAssembly code
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
wasmer-jni - wasmer java binding
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
JavaCPP - The missing bridge between Java and native C++
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
Aparapi - The New Official Aparapi: a framework for executing native Java and Scala code on the GPU.
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.
iota.rs - Official IOTA Rust library.
Megatron-LM - Ongoing research training transformer models at scale