66 lines
1.9 KiB
Markdown
66 lines
1.9 KiB
Markdown
# Prerequisites
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## Below are the prerequisites to be satisfied for running the samples.
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For running the samples you will need a cluster with Kubeflow 1.4.xxx (or later) installed,
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refer https://github.com/kubeflow/manifests for details.
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### Add Minio secret for KServe
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Apply below secret and service account for KServe to access minio server
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minio-secret.yaml
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```yaml
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apiVersion: v1
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kind: Secret
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metadata:
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name: mysecret
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annotations:
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serving.kserve.io/s3-endpoint: minio-service.kubeflow:9000 # replace with your s3 endpoint
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serving.kserve.io/s3-usehttps: "0" # by default 1, for testing with minio you need to set to 0
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serving.kserve.io/s3-region: "minio" # replace with the region the bucket is created in
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serving.kserve.io/s3-useanoncredential: "false" # omitting this is the same as false, if true will ignore credential provided and use anonymous credentials
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type: Opaque
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data:
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AWS_ACCESS_KEY_ID: <base-64-minio-access-key> # replace with your base64 encoded minio credential
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AWS_SECRET_ACCESS_KEY: <base-64-minio-secret-key> # replace with your base64 encoded minio credential
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---
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apiVersion: v1
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kind: ServiceAccount
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metadata:
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name: sa
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secrets:
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- name: mysecret
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```
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Run the following command to set the secrets
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```Kubectl apply -f minio-secret.yaml -n kubeflow-user-example-com```
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### Disable sidecar injection
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Run the following command to disable sidecar injection
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```kubectl label namespace kubeflow-user-example-com istio-injection=disabled --overwrite```
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## Migrate to KServe 0.8.0
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Refer: https://kserve.github.io/website/admin/migration/#migrating-from-kubeflow-based-kfserving
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Note: Install KServe 0.8.0
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### Modify KServe predictor image
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Edit inferenceservice-config configmap
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```kubectl edit cm inferenceservice-config -n kubeflow```
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Update the following keys under `predictors -> pytorch` block
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```yaml
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"image": "pytorch/torchserve-kfs",
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"defaultImageVersion": "0.5.1",
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"defaultGpuImageVersion": "0.5.1-gpu",
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```
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