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Thoras Documentation
Documentation for Thoras, the autonomous control plane for compute efficiency. Predictive Kubernetes autoscaling, self-hosted.
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# Thoras Documentation
> Documentation for Thoras, the autonomous control plane for compute efficiency. Predictive Kubernetes autoscaling, self-hosted.
- [What is Thoras?](https://docs.thoras.ai/getting-started/what-is-thoras.md): The autonomous control plane for compute efficiency.
- [Quickstart](https://docs.thoras.ai/getting-started/quickstart.md): Deploy Thoras and see Kubernetes resource recommendations in minutes!
- [System Requirements](https://docs.thoras.ai/installation/system-requirements.md)
- [Advanced Setup](https://docs.thoras.ai/installation/advanced-setup.md)
- [Persistent Storage](https://docs.thoras.ai/installation/persistent-storage.md): Configure the persistent volume Thoras uses to retain historical usage metrics, and optionally point Thoras at an external TimescaleDB.
- [Credentials](https://docs.thoras.ai/installation/credentials.md): How Thoras handles the credentials it needs, and how to supply your own.
- [Private Container Registry](https://docs.thoras.ai/installation/private-registry.md): Pull Thoras container images from your own private registry instead of the public Thoras registry.
- [Upgrading](https://docs.thoras.ai/installation/upgrading.md): How Thoras Helm chart versions map to upgrade effort, and what changed in chart 5.x.
- [Configuring AIScaleTargets](https://docs.thoras.ai/guides/configuring-asts.md)
- [Predictive Vertical Pod Rightsizing](https://docs.thoras.ai/guides/vertical-pod-rightsizing.md): Predictive Vertical Pod Rightsizing is the process of preemptively optimizing the CPU and memory resource requests and limits for Kubernetes pods to ensure they have just enough resources to run efficiently without over or under provisioning. Unlike horizontal scaling, which adjusts the number of po…
- [In-Place Resize Behavior](https://docs.thoras.ai/guides/in-place-resize.md): How Thoras handles in-place pod resizing, what happens when a resize cannot complete, and the safety controls that protect your workloads.
- [Predictive Horizontal Pod Autoscaling](https://docs.thoras.ai/guides/hpa.md)
- [Choosing a Forecast Optimization Strategy](https://docs.thoras.ai/guides/forecast-optimization-strategies.md)
- [OOM Remediation](https://docs.thoras.ai/guides/oom-remediation.md): Automatically recover workloads from out-of-memory kill loops
- [JVM Workload Scaling](https://docs.thoras.ai/guides/jvm-scaling.md): How Thoras optimizes Java-based applications
- [Enabling Autonomous Mode](https://docs.thoras.ai/guides/enabling-autonomous-mode.md): Enable predictive scaling with confidence
- [Pausing Autonomous Scaling](https://docs.thoras.ai/guides/pause-scaling.md): Temporarily halt all autonomous scaling actions cluster-wide
- [Audit Trail](https://docs.thoras.ai/guides/audit-trail.md): See who changed scaling behavior, and what changed, from the api-server logs
- [Stopping Autonomous Scaling](https://docs.thoras.ai/guides/stopping-autoscaling.md): Options for halting Thoras autoscaling, from cluster-wide to per-worklo
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