This page was generated from examples/minio/minio.ipynb.

Basic Examples for SKlearn Prepackaged Server with MinIO¶

Prerequisites¶

  • A kubernetes cluster with kubectl configured

  • curl

Setup Seldon Core¶

Use the setup notebook to Setup Cluster with Ambassador Ingress and Install Seldon Core. Instructions also online.

Setup MinIO¶

Use the provided notebook to install Minio in your cluster and configure mc CLI tool. Instructions also online.

Copy iris model into local MinIO¶

[1]:

%%bash
mc config host add gcs https://storage.googleapis.com "" ""

mc mb minio-seldon/iris -p
mc cp gcs/seldon-models/sklearn/iris/model.joblib minio-seldon/iris/
mc cp gcs/seldon-models/sklearn/iris/metadata.yaml minio-seldon/iris/

Added `gcs` successfully.
Bucket created successfully `minio-seldon/iris`.
`gcs/seldon-models/sklearn/iris/model.joblib` -> `minio-seldon/iris/model.joblib`
Total: 0 B, Transferred: 1.06 KiB, Speed: 2.16 KiB/s
`gcs/seldon-models/sklearn/iris/metadata.yaml` -> `minio-seldon/iris/metadata.yaml`
Total: 0 B, Transferred: 162 B, Speed: 335 B/s

Modify model metadata (optional)¶

[2]:

%%bash
mc cat minio-seldon/iris/metadata.yaml


name: iris
versions: [iris/v1]
platform: sklearn
inputs:
- datatype: BYTES
  name: input
  shape: [ 4 ]
outputs:
- datatype: BYTES
  name: output
  shape: [ 3 ]

[3]:

%%writefile metadata.yaml

name: iris
versions: [iris/v1-updated]
platform: sklearn
inputs:
- datatype: BYTES
  name: input
  shape: [ 1, 4 ]
outputs:
- datatype: BYTES
  name: output
  shape: [ 3 ]

Overwriting metadata.yaml

[4]:

%%bash
mc cp metadata.yaml minio-seldon/iris/

`metadata.yaml` -> `minio-seldon/iris/metadata.yaml`
Total: 0 B, Transferred: 173 B, Speed: 25.02 KiB/s

Deploy sklearn server¶

[5]:

%%writefile secret.yaml

apiVersion: v1
kind: Secret
metadata:
  name: seldon-init-container-secret
type: Opaque
stringData:
  RCLONE_CONFIG_S3_TYPE: s3
  RCLONE_CONFIG_S3_PROVIDER: minio
  RCLONE_CONFIG_S3_ACCESS_KEY_ID: minioadmin
  RCLONE_CONFIG_S3_SECRET_ACCESS_KEY: minioadmin
  RCLONE_CONFIG_S3_ENDPOINT: http://minio.minio-system.svc.cluster.local:9000
  RCLONE_CONFIG_S3_ENV_AUTH: "false"

Overwriting secret.yaml

[6]:

!kubectl apply -f secret.yaml

secret/seldon-init-container-secret created

[7]:

%%writefile deploy.yaml

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: minio-sklearn
spec:
  name: iris
  predictors:
  - componentSpecs:
    graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: s3://iris
      envSecretRefName: seldon-init-container-secret
      name: classifier
    name: default
    replicas: 1

Overwriting deploy.yaml

[8]:

!kubectl apply -f deploy.yaml

seldondeployment.machinelearning.seldon.io/minio-sklearn created

[9]:

!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=minio-sklearn -o jsonpath='{.items[0].metadata.name}')

Waiting for deployment "minio-sklearn-default-0-classifier" rollout to finish: 0 of 1 updated replicas are available...
deployment "minio-sklearn-default-0-classifier" successfully rolled out

Test deployment¶

Test prediction¶

[10]:

%%bash
curl -s -X POST -H 'Content-Type: application/json' \
    -d '{"data":{"ndarray":[[5.964, 4.006, 2.081, 1.031]]}}' \
    http://localhost:8003/seldon/seldon/minio-sklearn/api/v1.0/predictions  | jq .

{
  "data": {
    "names": [
      "t:0",
      "t:1",
      "t:2"
    ],
    "ndarray": [
      [
        0.9548873249364169,
        0.04505474761561406,
        5.7927447968952436e-05
      ]
    ]
  },
  "meta": {}
}

Test model metadata (optional)¶

[11]:

%%bash
curl -s http://localhost:8003/seldon/seldon/minio-sklearn/api/v1.0/metadata/classifier | jq .

{
  "inputs": [
    {
      "datatype": "BYTES",
      "name": "input",
      "shape": [
        1,
        4
      ]
    }
  ],
  "name": "iris",
  "outputs": [
    {
      "datatype": "BYTES",
      "name": "output",
      "shape": [
        3
      ]
    }
  ],
  "platform": "sklearn",
  "versions": [
    "iris/v1-updated"
  ]
}

Test for CI¶

[12]:

import json

data = !curl -s -X POST -H 'Content-Type: application/json' -d '{"data":{"ndarray":[[5.964, 4.006, 2.081, 1.031]]}}' http://localhost:8003/seldon/seldon/minio-sklearn/api/v1.0/predictions
data = json.loads(data[0])

assert data == {
    "data": {
        "names": ["t:0", "t:1", "t:2"],
        "ndarray": [[0.9548873249364169, 0.04505474761561406, 5.7927447968952436e-05]],
    },
    "meta": {},
}

[13]:

import json

meta = !curl -s http://localhost:8003/seldon/seldon/minio-sklearn/api/v1.0/metadata/classifier
meta = json.loads(meta[0])

assert data == {
    "data": {
        "names": ["t:0", "t:1", "t:2"],
        "ndarray": [[0.9548873249364169, 0.04505474761561406, 5.7927447968952436e-05]],
    },
    "meta": {},
}

Cleanup¶

[14]:

!kubectl delete -f deploy.yaml

seldondeployment.machinelearning.seldon.io "minio-sklearn" deleted