onnx-tensorrt VS server

Compare onnx-tensorrt vs server and see what are their differences.

onnx-tensorrt

ONNX-TensorRT: TensorRT backend for ONNX (by onnx)

server

The Triton Inference Server provides an optimized cloud and edge inferencing solution. (by triton-inference-server)
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onnx-tensorrt server
4 24
2,722 7,160
1.5% 5.1%
4.8 9.5
about 2 months ago 3 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.

onnx-tensorrt

Posts with mentions or reviews of onnx-tensorrt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-02.
  • [P] [D]How to get TensorFlow model to run on Jetson Nano?
    4 projects | /r/MachineLearning | 2 Jun 2021
    Conversion was done from Keras Tensorflow using to ONNX https://github.com/onnx/keras-onnx followed by ONNX to TensorRT using https://github.com/onnx/onnx-tensorrt The Python code used for inference using TensorRT can be found at https://github.com/jonnor/modeld/blob/tensorrt/tensorrtutils.py

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
  • 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.

What are some alternatives?

When comparing onnx-tensorrt and server you can also consider the following projects:

onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.

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

jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

deepC - vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers

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

keras-onnx - Convert tf.keras/Keras models to ONNX

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

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.

Megatron-LM - Ongoing research training transformer models at scale

tensorflow - An Open Source Machine Learning Framework for Everyone