arboreal VS ivy

Compare arboreal vs ivy and see what are their differences.

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arboreal ivy
1 17
6 14,016
- 0.5%
1.0 10.0
4 days ago 6 days ago
Go Python
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
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.


Posts with mentions or reviews of arboreal. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-27.
  • Show HN: Carton – Run any ML model from any programming language
    4 projects | | 27 Sep 2023
    We used Triton Inference Server (with a Golang sidecar to translate requests) for model serving and a separate Go app that handled receiving the request, fetching features, sending to Triton, doing other stuff with the response, serving. This scaled to 100k QPS with pretty good performance but does require some hops.

    In general writing pure Go inference libraries sucks. Not easy to do array/vector manipulation, not easy to do SIMD/CUDA acceleration, cgo is not go, etc. I wrote a fast XGBoost library at least ( - it's on par with C implementations, but doing anything more complex is going to be tricky.


Posts with mentions or reviews of ivy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-11-28.

What are some alternatives?

When comparing arboreal and ivy you can also consider the following projects:

carton - Run any ML model from any programming language.

PaddleNLP - πŸ‘‘ Easy-to-use and powerful NLP and LLM library with πŸ€— Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including πŸ—‚Text Classification, πŸ” Neural Search, ❓ Question Answering, ℹ️ Information Extraction, πŸ“„ Document Intelligence, πŸ’Œ Sentiment Analysis etc.

tfgo - Tensorflow + Go, the gopher way

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

DeepFaceLive - Real-time face swap for PC streaming or video calls

PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)

lisp - Toy Lisp 1.5 interpreter

Kornia - Geometric Computer Vision Library for Spatial AI

devops-exercises - Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions

material-design-icons-adt-template - Android Studio / Eclipse ADT template for material-design-icons resources

machine_learning_refined - Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).

upspin - Upspin: A framework for naming everyone's everything.