Nos
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Serve | 3,768 |  | 15 | 2 days ago | 22 | October 12, 2023 | 339 | apache-2.0 | Java | |
| Serve, optimize and scale PyTorch models in production | ||||||||||
| Nos | 527 | 8 days ago | 18 | apache-2.0 | Go | |||||
| Module to Automatically maximize the utilization of GPU resources in a Kubernetes cluster through real-time dynamic partitioning and elastic quotas - Effortless optimization at its finest! | ||||||||||
| Kube Reqsizer | 182 | 3 months ago | 13 | Go | ||||||
| A Kubernetes controller for automatically optimizing pod requests based on their continuous usage. VPA alternative that can work with HPA. | ||||||||||
| Elearning | 73 | 10 months ago | other | HTML | ||||||
| elearning linux/mac/db/cache/server/tools/人工智能 | ||||||||||
| Gke Pod Usage | 36 | 4 years ago | other | Python | ||||||
| pod_usage queries a k8s cluster and compares pod usage versus the pod requests and limits. It provides output that can then be analyzed to determine if optimizations can be made, and helps you find out if your oversubscribed or undersubcribed. | ||||||||||
| Experiments | 27 | a year ago | 1 | May 15, 2018 | 10 | apache-2.0 | Python | |||
| Experiments API for Experiment Tracking on Kubernetes | ||||||||||
| Container Optimization Data Forwarder | 5 | 2 months ago | 1 | apache-2.0 | Go | |||||
| Openflow_analysis | 2 | 2 years ago | mit | Python | ||||||
| utils for analysis and optimization of config files/rule-sets from the cloud/OF environment | ||||||||||
Nebuly Operating System (nos)
Documentation: docs.nebuly.com/nos/overview
If you like the project please support it by leaving a star
nos is the open-source module to efficiently run AI workloads on Kubernetes,
increasing GPU utilization, cutting down infrastructure costs and improving workloads performance.
Currently, the available features are:
-
Dynamic GPU partitioning: allow to schedule Pods requesting fractions of GPU. GPU partitioning is performed automatically in real-time based on the Pods pending and running in the cluster, so that Pods can request only the resources that are strictly necessary and GPUs are always fully utilized.
-
Elastic Resource Quota management: increase the number of Pods running on the cluster by allowing namespaces to borrow quotas of reserved resources from other namespaces as long as they are not using them.
Getting started
Prerequisites
- Kubernetes v1.23 or newer
- GPU Support must be enabled
- Nebuly k8s-device-plugin (optional, required only if you want to enable MPS partitioning)
- cert-manager (optional, but recommended)
Installation
You can install nos using Helm 3 (recommended).
You can find all the available configuration values in the Chart documentation.
helm install oci://ghcr.io/nebuly-ai/helm-charts/nos \
--version 0.1.2 \
--namespace nebuly-nos \
--generate-name \
--create-namespace
Alternatively, you can use Kustomize by cloning the repository and running make deploy.
