dlrover
kfserving
| dlrover | kfserving | |
|---|---|---|
| 3 | 1 | |
| 1,662 | 2,113 | |
| 0.5% | - | |
| 9.3 | 10.0 | |
| 22 days ago | about 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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.
dlrover
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DLRover: A Large-scale Intelligent Distributed Training System
Star our project on GitHub: https://github.com/intelligent-machine-learning/dlrover
- DLRover: Distributed Deep Learning System for LLM Training
- [KDD'23] Weighted Sharpness-Aware Minimization (WSAM) Optimizer open-sourced in DLRover project.
kfserving
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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.
What are some alternatives?
beiboot - Getdeck Beiboot is a Kubernetes-in-Kubernetes solution :rocket: It allows creating multiple logical Kubernetes environments within one :arrow_right: physical host cluster.
quick-deploy - Optimize, convert and deploy machine learning models as fast inference API using Triton and ORT. Currently support Hugging Face transformers, PyToch, Tensorflow, SKLearn and XGBoost models.
microk8s - MicroK8s is a small, fast, single-package Kubernetes for developers, IoT and edge. [Moved to: https://github.com/canonical/microk8s]
examples - 📝 Examples of how to use Neptune for different use cases and with various MLOps tools
microk8s - MicroK8s is a small, fast, single-package Kubernetes for datacenters and the edge.
mosec - A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine