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140 lines
7.5 KiB
Markdown
140 lines
7.5 KiB
Markdown
# Helm
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A Helm chart to deploy vLLM for Kubernetes
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Helm is a package manager for Kubernetes. It helps automate the deployment of vLLM applications on Kubernetes. With Helm, you can deploy the same framework architecture with different configurations to multiple namespaces by overriding variable values.
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This guide will walk you through the process of deploying vLLM with Helm, including the necessary prerequisites, steps for Helm installation and documentation on architecture and values file.
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## Prerequisites
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Before you begin, ensure that you have the following:
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- A running Kubernetes cluster
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- NVIDIA Kubernetes Device Plugin (`k8s-device-plugin`): This can be found at [https://github.com/NVIDIA/k8s-device-plugin](https://github.com/NVIDIA/k8s-device-plugin)
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- Available GPU resources in your cluster
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- (Optional) An S3 bucket or other storage with the model weights, if using automatic model download
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## Installing the chart
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To install the chart with the release name `test-vllm`:
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```bash
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helm upgrade --install --create-namespace \
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--namespace=ns-vllm test-vllm . \
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-f values.yaml \
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--set secrets.s3endpoint=$ACCESS_POINT \
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--set secrets.s3bucketname=$BUCKET \
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--set secrets.s3accesskeyid=$ACCESS_KEY \
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--set secrets.s3accesskey=$SECRET_KEY
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```
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## Uninstalling the chart
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To uninstall the `test-vllm` deployment:
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```bash
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helm uninstall test-vllm --namespace=ns-vllm
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```
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The command removes all the Kubernetes components associated with the
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chart **including persistent volumes** and deletes the release.
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## Architecture
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## Values
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The following table describes configurable parameters of the chart in `values.yaml`:
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| Key | Type | Default | Description |
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|-----|------|---------|-------------|
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| autoscaling | object | {"enabled":false,"maxReplicas":100,"minReplicas":1,"targetCPUUtilizationPercentage":80} | Autoscaling configuration |
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| autoscaling.enabled | bool | false | Enable autoscaling |
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| autoscaling.maxReplicas | int | 100 | Maximum replicas |
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| autoscaling.minReplicas | int | 1 | Minimum replicas |
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| autoscaling.targetCPUUtilizationPercentage | int | 80 | Target CPU utilization for autoscaling |
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| configs | object | {} | Configmap |
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| containerPort | int | 8000 | Container port |
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| customObjects | list | [] | Custom Objects configuration |
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| deploymentStrategy | object | {} | Deployment strategy configuration |
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| externalConfigs | list | [] | External configuration |
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| extraContainers | list | [] | Additional containers configuration |
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| extraInit | object | {"modelDownload":{"enabled":true},"initContainers":[],"pvcStorage":"1Gi"} | Additional configuration for init containers |
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| extraInit.modelDownload | object | {"enabled":true} | Model download functionality configuration |
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| extraInit.modelDownload.enabled | bool | true | Enable automatic model download job and wait container |
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| extraInit.modelDownload.image | object | {"repository":"amazon/aws-cli","tag":"2.6.4","pullPolicy":"IfNotPresent"} | Image for model download operations |
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| extraInit.modelDownload.waitContainer | object | {} | Wait container configuration (command, args, env) |
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| extraInit.modelDownload.downloadJob | object | {} | Download job configuration (command, args, env) |
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| extraInit.initContainers | list | [] | Custom init containers (appended after model download if enabled) |
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| extraInit.pvcStorage | string | "1Gi" | Storage size for the PVC |
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| extraInit.s3modelpath | string | "relative_s3_model_path/opt-125m" | (Optional) Path of the model on S3 |
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| extraInit.awsEc2MetadataDisabled | bool | true | (Optional) Disable AWS EC2 metadata service |
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| extraPorts | list | [] | Additional ports configuration |
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| gpuModels | list | ["TYPE_GPU_USED"] | Type of gpu used |
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| image | object | {"command":["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"],"repository":"vllm/vllm-openai","tag":"latest"} | Image configuration |
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| image.command | list | ["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"] | Container launch command |
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| image.repository | string | "vllm/vllm-openai" | Image repository |
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| image.tag | string | "latest" | Image tag |
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| livenessProbe | object | {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":15,"periodSeconds":10} | Liveness probe configuration |
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| livenessProbe.failureThreshold | int | 3 | Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not alive |
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| livenessProbe.httpGet | object | {"path":"/health","port":8000} | Configuration of the kubelet http request on the server |
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| livenessProbe.httpGet.path | string | "/health" | Path to access on the HTTP server |
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| livenessProbe.httpGet.port | int | 8000 | Name or number of the port to access on the container, on which the server is listening |
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| livenessProbe.initialDelaySeconds | int | 15 | Number of seconds after the container has started before liveness probe is initiated |
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| livenessProbe.periodSeconds | int | 10 | How often (in seconds) to perform the liveness probe |
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| maxUnavailablePodDisruptionBudget | string | "" | Disruption Budget Configuration |
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| readinessProbe | object | {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":5,"periodSeconds":5} | Readiness probe configuration |
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| readinessProbe.failureThreshold | int | 3 | Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not ready |
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| readinessProbe.httpGet | object | {"path":"/health","port":8000} | Configuration of the kubelet http request on the server |
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| readinessProbe.httpGet.path | string | "/health" | Path to access on the HTTP server |
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| readinessProbe.httpGet.port | int | 8000 | Name or number of the port to access on the container, on which the server is listening |
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| readinessProbe.initialDelaySeconds | int | 5 | Number of seconds after the container has started before readiness probe is initiated |
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| readinessProbe.periodSeconds | int | 5 | How often (in seconds) to perform the readiness probe |
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| replicaCount | int | 1 | Number of replicas |
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| resources | object | {"limits":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1},"requests":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1}} | Resource configuration |
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| resources.limits."nvidia.com/gpu" | int | 1 | Number of GPUs used |
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| resources.limits.cpu | int | 4 | Number of CPUs |
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| resources.limits.memory | string | "16Gi" | CPU memory configuration |
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| resources.requests."nvidia.com/gpu" | int | 1 | Number of GPUs used |
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| resources.requests.cpu | int | 4 | Number of CPUs |
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| resources.requests.memory | string | "16Gi" | CPU memory configuration |
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| secrets | object | {} | Secrets configuration |
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| serviceName | string | "" | Service name |
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| servicePort | int | 80 | Service port |
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| labels.environment | string | test | Environment name |
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## Configuration Examples
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### Using S3 Model Download (Default)
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```yaml
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extraInit:
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modelDownload:
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enabled: true
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pvcStorage: "10Gi"
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s3modelpath: "models/llama-7b"
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```
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### Using Custom Init Containers Only
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For use cases like llm-d where you need custom sidecars without model download:
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```yaml
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extraInit:
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modelDownload:
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enabled: false
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initContainers:
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- name: llm-d-routing-proxy
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image: ghcr.io/llm-d/llm-d-routing-sidecar:v0.2.0
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imagePullPolicy: IfNotPresent
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ports:
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- containerPort: 8080
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name: proxy
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securityContext:
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runAsUser: 1000
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restartPolicy: Always
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pvcStorage: "10Gi"
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```
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