server VS DeepSpeed

Compare server vs DeepSpeed and see what are their differences.

server

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

DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. (by microsoft)
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server DeepSpeed
24 51
7,160 32,055
5.1% 3.1%
9.5 9.8
3 days ago 4 days ago
Python Python
BSD 3-clause "New" or "Revised" License Apache License 2.0
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.

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.

DeepSpeed

Posts with mentions or reviews of DeepSpeed. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-06.

What are some alternatives?

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

ColossalAI - Making large AI models cheaper, faster and more accessible

Megatron-LM - Ongoing research training transformer models at scale

fairscale - PyTorch extensions for high performance and large scale training.

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

accelerate - 🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

mesh-transformer-jax - Model parallel transformers in JAX and Haiku

llama - Inference code for Llama models

text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

flash-attention - Fast and memory-efficient exact attention

gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration