examples/minio/minio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Examples for SKlearn Prepackaged Server with MinIO\n",
    "\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    " * A kubernetes cluster with kubectl configured\n",
    " * curl\n",
    "\n",
    "## Setup Seldon Core\n",
    "\n",
    "Use the setup notebook to [Setup Cluster](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html#Setup-Cluster) with [Ambassador Ingress](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html#Ambassador) and [Install Seldon Core](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html#Install-Seldon-Core). Instructions [also online](https://docs.seldon.io/projects/seldon-core/en/latest/examples/seldon_core_setup.html).\n",
    "\n",
    "## Setup MinIO\n",
    "\n",
    "Use the provided [notebook](https://docs.seldon.io/projects/seldon-core/en/latest/examples/minio_setup.html) to install Minio in your cluster and configure `mc` CLI tool. \n",
    "Instructions [also online](https://docs.seldon.io/projects/seldon-core/en/latest/examples/minio_setup.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy iris model into local MinIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added `gcs` successfully.\n",
      "Bucket created successfully `minio-seldon/iris`.\n",
      "`gcs/seldon-models/sklearn/iris/model.joblib` -> `minio-seldon/iris/model.joblib`\n",
      "Total: 0 B, Transferred: 1.06 KiB, Speed: 2.16 KiB/s\n",
      "`gcs/seldon-models/sklearn/iris/metadata.yaml` -> `minio-seldon/iris/metadata.yaml`\n",
      "Total: 0 B, Transferred: 162 B, Speed: 335 B/s\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mc config host add gcs https://storage.googleapis.com \"\" \"\" \n",
    "\n",
    "mc mb minio-seldon/iris -p\n",
    "mc cp gcs/seldon-models/sklearn/iris/model.joblib minio-seldon/iris/\n",
    "mc cp gcs/seldon-models/sklearn/iris/metadata.yaml minio-seldon/iris/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modify model metadata (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "name: iris\n",
      "versions: [iris/v1]\n",
      "platform: sklearn\n",
      "inputs:\n",
      "- datatype: BYTES\n",
      "  name: input\n",
      "  shape: [ 4 ]\n",
      "outputs:\n",
      "- datatype: BYTES\n",
      "  name: output\n",
      "  shape: [ 3 ]\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mc cat minio-seldon/iris/metadata.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting metadata.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile metadata.yaml\n",
    "\n",
    "name: iris\n",
    "versions: [iris/v1-updated]\n",
    "platform: sklearn\n",
    "inputs:\n",
    "- datatype: BYTES\n",
    "  name: input\n",
    "  shape: [ 1, 4 ]\n",
    "outputs:\n",
    "- datatype: BYTES\n",
    "  name: output\n",
    "  shape: [ 3 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`metadata.yaml` -> `minio-seldon/iris/metadata.yaml`\n",
      "Total: 0 B, Transferred: 173 B, Speed: 25.02 KiB/s\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mc cp metadata.yaml minio-seldon/iris/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy sklearn server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting secret.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile secret.yaml\n",
    "\n",
    "apiVersion: v1\n",
    "kind: Secret\n",
    "metadata:\n",
    "  name: seldon-init-container-secret\n",
    "type: Opaque\n",
    "stringData:\n",
    "  RCLONE_CONFIG_S3_TYPE: s3\n",
    "  RCLONE_CONFIG_S3_PROVIDER: minio\n",
    "  RCLONE_CONFIG_S3_ACCESS_KEY_ID: minioadmin\n",
    "  RCLONE_CONFIG_S3_SECRET_ACCESS_KEY: minioadmin\n",
    "  RCLONE_CONFIG_S3_ENDPOINT: http://minio.minio-system.svc.cluster.local:9000\n",
    "  RCLONE_CONFIG_S3_ENV_AUTH: \"false\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "secret/seldon-init-container-secret created\n"
     ]
    }
   ],
   "source": [
    "!kubectl apply -f secret.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting deploy.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile deploy.yaml\n",
    "\n",
    "apiVersion: machinelearning.seldon.io/v1\n",
    "kind: SeldonDeployment\n",
    "metadata:\n",
    "  name: minio-sklearn\n",
    "spec:\n",
    "  name: iris\n",
    "  predictors:\n",
    "  - componentSpecs:\n",
    "    graph:\n",
    "      children: []\n",
    "      implementation: SKLEARN_SERVER\n",
    "      modelUri: s3://iris\n",
    "      envSecretRefName: seldon-init-container-secret\n",
    "      name: classifier\n",
    "    name: default\n",
    "    replicas: 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seldondeployment.machinelearning.seldon.io/minio-sklearn created\n"
     ]
    }
   ],
   "source": [
    "!kubectl apply -f deploy.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for deployment \"minio-sklearn-default-0-classifier\" rollout to finish: 0 of 1 updated replicas are available...\n",
      "deployment \"minio-sklearn-default-0-classifier\" successfully rolled out\n"
     ]
    }
   ],
   "source": [
    "!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=minio-sklearn -o jsonpath='{.items[0].metadata.name}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"data\": {\n",
      "    \"names\": [\n",
      "      \"t:0\",\n",
      "      \"t:1\",\n",
      "      \"t:2\"\n",
      "    ],\n",
      "    \"ndarray\": [\n",
      "      [\n",
      "        0.9548873249364169,\n",
      "        0.04505474761561406,\n",
      "        5.7927447968952436e-05\n",
      "      ]\n",
      "    ]\n",
      "  },\n",
      "  \"meta\": {}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -s -X POST -H 'Content-Type: application/json' \\\n",
    "    -d '{\"data\":{\"ndarray\":[[5.964, 4.006, 2.081, 1.031]]}}' \\\n",
    "    http://localhost:8003/seldon/seldon/minio-sklearn/api/v1.0/predictions  | jq ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test model metadata (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"inputs\": [\n",
      "    {\n",
      "      \"datatype\": \"BYTES\",\n",
      "      \"name\": \"input\",\n",
      "      \"shape\": [\n",
      "        1,\n",
      "        4\n",
      "      ]\n",
      "    }\n",
      "  ],\n",
      "  \"name\": \"iris\",\n",
      "  \"outputs\": [\n",
      "    {\n",
      "      \"datatype\": \"BYTES\",\n",
      "      \"name\": \"output\",\n",
      "      \"shape\": [\n",
      "        3\n",
      "      ]\n",
      "    }\n",
      "  ],\n",
      "  \"platform\": \"sklearn\",\n",
      "  \"versions\": [\n",
      "    \"iris/v1-updated\"\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "curl -s http://localhost:8003/seldon/seldon/minio-sklearn/api/v1.0/metadata/classifier | jq ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test for CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "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\n",
    "data = json.loads(data[0])\n",
    "\n",
    "assert data == {\n",
    "    \"data\": {\n",
    "        \"names\": [\"t:0\", \"t:1\", \"t:2\"],\n",
    "        \"ndarray\": [[0.9548873249364169, 0.04505474761561406, 5.7927447968952436e-05]],\n",
    "    },\n",
    "    \"meta\": {},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "meta = !curl -s http://localhost:8003/seldon/seldon/minio-sklearn/api/v1.0/metadata/classifier\n",
    "meta = json.loads(meta[0])\n",
    "\n",
    "assert data == {\n",
    "    \"data\": {\n",
    "        \"names\": [\"t:0\", \"t:1\", \"t:2\"],\n",
    "        \"ndarray\": [[0.9548873249364169, 0.04505474761561406, 5.7927447968952436e-05]],\n",
    "    },\n",
    "    \"meta\": {},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seldondeployment.machinelearning.seldon.io \"minio-sklearn\" deleted\n"
     ]
    }
   ],
   "source": [
    "!kubectl delete -f deploy.yaml"
   ]
  }
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