AI starter kit chart #579
AI starter kit chart #579
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[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: volatilemolotov The full list of commands accepted by this bot can be found here.
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Here is the initial PR, currently in draft state. Think we should be able to send it for reviews |
Removed the example values and ci folder. Hope makefile can stay, it can be useful |
| --mount --mount-string="/tmp/models-cache:/tmp/models-cache" | ||
| ``` | ||
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| 2. **Install the chart:** |
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better to clarify the path this command should be run within
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| 2. **Install the chart:** | ||
| ```bash | ||
| helm install ai-starter-kit . \ |
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Looks like helm dependency build is needed before install
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| 1. **Start minikube with persistent storage:** | ||
| ```bash | ||
| minikube start --cpus 4 --memory 15000 \ |
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Missing a step to create the models-cache folder? mkdir -p /tmp/models-cache
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Please remove all the "outputs" blob in this file
| -f values.yaml | ||
| ``` | ||
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| 3. **Access JupyterHub:** |
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Which notebooks are runnable in the minikube environment? I assume the only one works is chat_bot? Can we add description in each notebook to detail in what environment it's runnable?
| helm install ai-starter-kit . \ | ||
| --set huggingface.token="YOUR_HF_TOKEN" \ | ||
| -f values.yaml \ | ||
| -f values-gke.yaml |
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@janetkuo do you have concern to include the GKE specific setup in the example? Do you think we should remove this all?
| helm install ai-starter-kit . \ | ||
| --set huggingface.token="YOUR_HF_TOKEN" \ | ||
| -f values.yaml \ | ||
| -f values-gke.yaml |
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For all the helm install on GKE autopilot instructions here I got error admission webhook "warden-validating.common-webhooks.networking.gke.io" denied the request: GKE Warden rejected the request because it violates one or more constraints. Violations details: {"[denied by autopilot-persistent-volume-limitation]":["Persistent Volume sources can only be of types [csi nfs gcePersistentDisk] within Autopilot."]}
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This PR adds a AI starter kit helm chart that aims to provide a out of the box development solution for AI workloads. Uses RayServe, Ollama or Ramalama to run the LLMs and JupyterHub for the development.