server

The Triton Inference Server provides an optimized cloud and edge inferencing solution. (by triton-inference-server)

Server Alternatives

Similar projects and alternatives to server

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better server alternative or higher similarity.

server reviews and mentions

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
  • 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
  • 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

  • Show HN: Software for Remote GPU-over-IP
    6 projects | news.ycombinator.com | 14 Dec 2022
    Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.

    Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.

    So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.

    Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.

    Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.

    [0] - https://github.com/kserve/kserve

    [1] - https://github.com/triton-inference-server/server

    [2] - https://github.com/triton-inference-server/client

    [3] - https://github.com/triton-inference-server/model_analyzer

  • Exploring Ghostwriter, a GitHub Copilot alternative
    3 projects | dev.to | 8 Nov 2022
    Replit built Ghostwriter on the open source scene based on Salesforce’s Codegen, using Nvidia’s FasterTransformer and Triton server for highly optimized decoders, and the knowledge distillation process of the CodeGen model from two billion parameters to a faster model of one billion parameters.
  • A note from our sponsor - SaaSHub
    www.saashub.com | 12 Apr 2024
    SaaSHub helps you find the best software and product alternatives Learn more →

Stats

Basic server repo stats
24
7,232
9.5
4 days ago

triton-inference-server/server is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.

The primary programming language of server is Python.

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