2 dépôts
Awesome GitHub RepositoriesLocal Cache Deployments
Deploys inference services that load models from a local cache on node storage.
Distinct from Model Serving: Distinct from Model Serving: focuses on deploying services that use a pre-populated local cache rather than general model serving infrastructure.
Explore 2 awesome GitHub repositories matching devops & infrastructure · Local Cache Deployments. Refine with filters or upvote what's useful.
Awesome Local Cache Deployments GitHub Repositories
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- kubeflow/kfserving
kubeflow/kfserving
5,576Voir sur GitHubKServe is an open platform for deploying and serving generative and predictive AI models on Kubernetes. It defines inference services as custom resources with declarative YAML specifications, enabling a Kubernetes-native approach to model deployment and lifecycle management. The platform leverages Knative-based serverless scaling for automatic scale-to-zero and revision management, and supports a pluggable serving runtime architecture that maps model formats to containerized execution environments. KServe distinguishes itself through model-aware autoscaling that scales replicas based on token
Deploys inference services that load models from a local cache on node storage to reduce startup time.
Go - kserve/kserve
kserve/kserve
5,576Voir sur GitHubKServe is a Kubernetes-native platform for deploying and serving machine learning models as scalable inference services. It supports both generative AI models, including large language models, and traditional predictive models from frameworks such as TensorFlow, PyTorch, Scikit-Learn, XGBoost, and ONNX. The platform manages the full lifecycle of model deployments, including revision tracking, canary rollouts, A/B testing, and automatic rollbacks, and provides serverless scale-to-zero capabilities for cost-efficient resource management. KServe distinguishes itself through a standardized infere
Installs a controller that caches model data on local nodes to reduce download latency for repeated deployments.
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