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archlitchi merged 4 commits into from
Aug 4, 2026
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Update document for release 1.1.0 #21

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30 changes: 14 additions & 16 deletions README.md
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Expand Up @@ -4,17 +4,22 @@

For architecture details see the [Design](#design) section.

## Prequisities

@coderabbitai coderabbitai Bot Aug 4, 2026

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📐 Maintainability & Code Quality | 🟡 Minor | ⚡ Quick win

Correct the heading spelling.

Change Prequisities to Prerequisites.

🧰 Tools
🪛 LanguageTool

[grammar] ~7-~7: Ensure spelling is correct
Context: ... see the Design section. ## Prequisities - KAI-scheduler Version: ≥ 0.17.0 ## Qu...

(QB_NEW_EN_ORTHOGRAPHY_ERROR_IDS_1)

🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@README.md` at line 7, Correct the README heading spelling by changing
“Prequisities” to “Prerequisites,” leaving the surrounding content unchanged.

Source: Linters/SAST tools


- **KAI-scheduler Version**: ≥ 0.17.0

## Quick Start

### 1. Deploy KAI-Scheduler with GPU sharing enabled

Follow the [KAI-Scheduler deployment guide](https://github.com/NVIDIA/KAI-Scheduler/blob/main/docs/gpu-sharing/gpu-sharing.md) and enable `gpushare` and `hamicore`:

```bash
helm install kai-scheduler oci://ghcr.io/nvidia/kai-scheduler \
helm install kai-scheduler oci://ghcr.io/kai-scheduler/kai-scheduler/kai-scheduler \
--set global.gpuSharing=true \
--set binder.plugins.hamicore.enabled=true \
--namespace kai-scheduler --create-namespace
--namespace kai-scheduler --create-namespace \
--version v0.17.0
```

### 2. Deploy kai-resource-isolator
Expand All @@ -24,10 +29,14 @@ Install directly from the OCI registry:
```bash
helm install kai-resource-isolator oci://docker.io/projecthami/kai-resource-isolator \
--namespace kai-resource-isolator --create-namespace \
--version 1.0.0-chart
--set monitor.enabled=true \
--set monitor.serviceMonitor.enabled=true \
Comment on lines +32 to +33

@coderabbitai coderabbitai Bot Aug 4, 2026

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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win

Document the ServiceMonitor dependency.

The chart creates ServiceMonitor only when monitoring.coreos.com/v1 is available. Without the Prometheus Operator CRD, Helm succeeds but automatic scraping is not configured.

Add this prerequisite or state that monitor.serviceMonitor.enabled=true is effective only when the CRD exists.

🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@README.md` around lines 32 - 33, Update the README configuration guidance
around monitor.serviceMonitor.enabled to document that automatic scraping
requires the Prometheus Operator monitoring.coreos.com/v1 ServiceMonitor CRD,
and state that the setting has no effect when that CRD is unavailable.

--version 1.1.0-chart
Comment thread
archlitchi marked this conversation as resolved.
```

Note: Chart versions carry a `-chart` suffix (e.g. `1.0.0-chart`). Available versions are listed at [projecthami/kai-resource-isolator](https://hub.docker.com/r/projecthami/kai-resource-isolator/tags) on Docker Hub.
The default `monitor.nodeSelector` is `nvidia.com/gpu.present: "true"` (NVIDIA GPU feature discovery). Set `monitor.runtimeClassName=nvidia` if NVML is only available through the NVIDIA runtime handler in your cluster.

Note: Chart versions carry a `-chart` suffix (e.g. `1.1.0-chart`). Available versions are listed at [projecthami/kai-resource-isolator](https://hub.docker.com/r/projecthami/kai-resource-isolator/tags) on Docker Hub.

## Build

Expand All @@ -38,25 +47,14 @@ git submodule update --init --recursive
docker build -f docker/Dockerfile -t <registry>/<project>/kai-resource-isolator:<tag> .
```

## Per-container VRAM metrics (optional)
## Per-container VRAM metrics

`kai-vgpu-monitor` is a DaemonSet that reads the shared-memory cache `libvgpu.so` writes for each GPU container and exposes HAMi-compatible gauges (`hami_vgpu_memory_used_bytes`, `hami_vgpu_memory_limit_bytes`, `hami_container_device_utilization_ratio`, …) by using

```
curl {pod ip}:9394/metrics
```

It is disabled by default because it runs privileged and needs NVML. Enable it on GPU nodes:

```bash
helm upgrade --install kai-resource-isolator oci://docker.io/projecthami/kai-resource-isolator \
--namespace kai-resource-isolator --create-namespace \
--set monitor.enabled=true \
--set monitor.serviceMonitor.enabled=true
```

The default `monitor.nodeSelector` is `nvidia.com/gpu.present: "true"` (NVIDIA GPU feature discovery). Set `monitor.runtimeClassName=nvidia` if NVML is only available through the NVIDIA runtime handler in your cluster.

## Customization

Tune `paths.containerVgpuMount` and `webhook.gpuShareResources` for your environment and HAMi extended resource names.
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