container2wasm VS server

Compare container2wasm vs server and see what are their differences.

server

The Triton Inference Server provides an optimized cloud and edge inferencing solution. (by triton-inference-server)
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container2wasm server
8 24
1,810 7,384
- 3.0%
9.1 9.5
1 day ago 2 days ago
C++ Python
Apache License 2.0 BSD 3-clause "New" or "Revised" License
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.

container2wasm

Posts with mentions or reviews of container2wasm. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-07.
  • Apple Introduces M4 Chip
    7 projects | news.ycombinator.com | 7 May 2024
    The existence of vscode.dev always makes me wonder why Microsoft never released an iOS version of VSCode to get more users into its ecosystem. Sure, it's almost as locked down as the web environment, but there's a lot of space in that "almost" - you could do all sorts of things like let users run their code, or complex extensions, in containers in a web view using https://github.com/ktock/container2wasm or similar.
  • Show HN: dockerc – Docker image to static executable "compiler"
    12 projects | news.ycombinator.com | 6 Mar 2024
    Unfortunately cosmopolitan wouldn't work for dockerc. Cosmopolitan works as long as you only use it but container runtimes require additional features. Also containers contain arbitrary executables so not sure how that would work either...

    As for WASM, this is already possible using container2wasm[0] and wasmer[1]'s ability to generate static binaries.

    [0]: https://github.com/ktock/container2wasm

    [1]: https://wasmer.io/

  • FLaNK Weekly 08 Jan 2024
    41 projects | dev.to | 8 Jan 2024
  • Container2wasm: Convert Containers to WASM Blobs
    16 projects | news.ycombinator.com | 3 Jan 2024
    Really impressed by the depth and breadth of this project, well done!

    A particularly interesting part is the socket layer inside the browser. Other people solving this problem have previously used a proxy to a server that does the real socket implementation. This means you can't have a "browser-only" solution.

    The author has solved this (for HTTP/S only) by proxying HTTP requests and then re-creating them as fetch requests (details here: https://github.com/ktock/container2wasm/tree/main/examples/n...). I'm very interested in using this approach for my own project Runno (https://runno.dev).

  • ktock/container2wasm: Container to WASM converter
    1 project | /r/devopsish | 28 Feb 2023

server

Posts with mentions or reviews of server. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-08.
  • FLaNK Weekly 08 Jan 2024
    41 projects | dev.to | 8 Jan 2024
  • Is there any open source app to load a model and expose API like OpenAI?
    5 projects | /r/LocalLLaMA | 9 Dec 2023
  • "A matching Triton is not available"
    1 project | /r/StableDiffusion | 15 Oct 2023
  • best way to serve llama V2 (llama.cpp VS triton VS HF text generation inference)
    3 projects | /r/LocalLLaMA | 25 Sep 2023
    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
    2 projects | /r/learnmachinelearning | 13 Jun 2023
  • Single RTX 3080 or two RTX 3060s for deep learning inference?
    1 project | /r/computervision | 12 Apr 2023
    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
  • Machine Learning Inference Server in Rust?
    4 projects | /r/rust | 21 Mar 2023
    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)
  • Multi-model serving options
    3 projects | /r/mlops | 12 Feb 2023
    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.
  • I mean,.. we COULD just make our own lol
    4 projects | /r/replika | 12 Feb 2023
    [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/
  • Why TensorFlow for Python is dying a slow death
    4 projects | news.ycombinator.com | 15 Jan 2023
    "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?

When comparing container2wasm and server you can also consider the following projects:

webvm - Virtual Machine for the Web

DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

SSH-Snake - SSH-Snake is a self-propagating, self-replicating, file-less script that automates the post-exploitation task of SSH private key and host discovery.

onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX

leptos - Build fast web applications with Rust.

ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]

dioxus - Fullstack GUI library for web, desktop, mobile, and more.

pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.

dockerc - container image to single executable compiler

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.

cortex - Drop-in, local AI alternative to the OpenAI stack. Multi-engine (llama.cpp, TensorRT-LLM). Powers 👋 Jan

Megatron-LM - Ongoing research training transformer models at scale