kfserving
Standardized Serverless ML Inference Platform on Kubernetes [Moved to: https://github.com/kserve/kserve] (by kubeflow)
mosec
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine (by mosecorg)
kfserving | mosec | |
---|---|---|
1 | 11 | |
2,113 | 707 | |
- | 0.9% | |
10.0 | 8.5 | |
about 1 year ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | 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.
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.
kfserving
Posts with mentions or reviews of kfserving.
We have used some of these posts to build our list of alternatives
and similar projects.
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How do we assign pods properly so that KFServing can scale down GPU Instances to zero?
We are using KFServing as well. KFServing allows us to auto-scale our GPU up and down, specifically scaling to zero when its not in use. The components in KFServing also get assigned to GPU nodes when applying them to our cluster.
mosec
Posts with mentions or reviews of mosec.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-06.
-
20x Faster as the Beginning: Introducing pgvecto.rs extension written in Rust
Mosec - A high-performance serving framework for ML models, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine. Simple and faster alternative to NVIDIA Triton.
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[D] Handling Concurrent Request for ML Model API
- Yes C++ would be better, but you can try mosec. It has a Python interface and helps you handle all the difficult things about Python multiprocessing. The web service part is implemented in Rust thus it's fast enough for machine learning services.
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Launching ModelZ Beta!
Contribute to open source projects: Modelz is built on top of envd, mosec, modelz-llm and many other open source projects. If you're interested in contributing to these projects, you can check out their GitHub repositories and start contributing.
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Deploying a model with an API in docker
You could first create the image with the framework you like (e.g. bentoml or https://github.com/mosecorg/mosec for light weight).
- PostgresML is 8-40x faster than Python HTTP microservices
- Python Machine Learning Service Can Run Way More Faster
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[D] Open Source ML Organisations to contribute to?
If you're interested in machine learning model serving, can check mosec: https://github.com/mosecorg/mosec
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Why not multiprocessing
During the development of a machine learning serving project Mosec, I used a lot of multiprocessing to make it more efficient. I want to share some experiences and some researches related to Python multiprocessing.
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[P] Mosec: deploy your machine learning model in an easy and efficient way
That's a good example. I have met the same situation before. I have created a discussion in GitHub to track the DAG progress.
- Mosec: deploy your machine learning model in an easy and efficient way
What are some alternatives?
When comparing kfserving and mosec you can also consider the following projects:
soopervisor - ☁️ Export Ploomber pipelines to Kubernetes (Argo), Airflow, AWS Batch, SLURM, and Kubeflow.
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!