sample pipeline notebook (kubeflow#476)

* sample pipeline notebook * addressing review comments
magdalenakuhn17 · Oct 25, 2019 · 485814b · 485814b
1 parent 5ca79a9
commit 485814b
Showing 1 changed file with 183 additions and 0 deletions.
diff --git a/docs/samples/pipelines/kfs-pipeline.ipynb b/docs/samples/pipelines/kfs-pipeline.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KFServing Pipeline samples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install the necessary kfp library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip3 install kfp --upgrade"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import kfp.dsl as dsl\n",
+    "import kfp\n",
+    "from kfp import components\n",
+    "import json\n",
+    "\n",
+    "# Create kfp client\n",
+    "# Note: Add the KubeFlow Pipeline endpoint below if the client is not running on the same cluster.\n",
+    "client = kfp.Client('kfserving_endpoint')\n",
+    "EXPERIMENT_NAME = 'KFServing Experiments'\n",
+    "experiment = client.create_experiment(name=EXPERIMENT_NAME)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TensorFlow example\n",
+    "\n",
+    "Note: Change the action from `update` to `create` if you are deploying the model for the first time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfserving_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/kfserving/component.yaml')\n",
+    "\n",
+    "@dsl.pipeline(\n",
+    "  name='kfserving pipeline',\n",
+    "  description='A pipeline for kfserving.'\n",
+    ")\n",
+    "def kfservingPipeline(\n",
+    "    action = 'update',\n",
+    "    model_name='tf-sample',\n",
+    "    default_model_uri='gs://kfserving-samples/models/tensorflow/flowers',\n",
+    "    canary_model_uri='gs://kfserving-samples/models/tensorflow/flowers-2',\n",
+    "    canary_model_traffic_percentage='10',\n",
+    "    namespace='your_namespace',\n",
+    "    framework='tensorflow',\n",
+    "    default_custom_model_spec='{}',\n",
+    "    canary_custom_model_spec='{}',\n",
+    "    autoscaling_target='0',\n",
+    "    kfserving_endpoint=''\n",
+    "):\n",
+    "\n",
+    "    # define workflow\n",
+    "    kfserving = kfserving_op(action = action,\n",
+    "                             model_name=model_name,\n",
+    "                             default_model_uri=default_model_uri,\n",
+    "                             canary_model_uri=canary_model_uri,\n",
+    "                             canary_model_traffic_percentage=canary_model_traffic_percentage,\n",
+    "                             namespace=namespace,\n",
+    "                             framework=framework,\n",
+    "                             default_custom_model_spec=default_custom_model_spec,\n",
+    "                             canary_custom_model_spec=canary_custom_model_spec,\n",
+    "                             autoscaling_target=autoscaling_target,\n",
+    "                             kfserving_endpoint=kfserving_endpoint).set_image_pull_policy('Always')\n",
+    "\n",
+    "# Compile pipeline\n",
+    "import kfp.compiler as compiler\n",
+    "compiler.Compiler().compile(kfservingPipeline, 'tf-flower.tar.gz')\n",
+    "\n",
+    "# Execute pipeline\n",
+    "run = client.run_pipeline(experiment.id, 'tf-flower', 'tf-flower.tar.gz')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Custom model example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kfserving_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/kfserving/component.yaml')\n",
+    "\n",
+    "@dsl.pipeline(\n",
+    "  name='kfserving pipeline',\n",
+    "  description='A pipeline for kfserving.'\n",
+    ")\n",
+    "def kfservingPipeline(\n",
+    "    action = 'update',\n",
+    "    model_name='custom-sample',\n",
+    "    default_model_uri='',\n",
+    "    canary_model_uri='',\n",
+    "    canary_model_traffic_percentage='0',\n",
+    "    namespace='kubeflow',\n",
+    "    framework='custom',\n",
+    "    default_custom_model_spec='{\"name\": \"image-segmenter\", \"image\": \"codait/max-image-segmenter:latest\", \"port\": \"5000\"}',\n",
+    "    canary_custom_model_spec='{}',\n",
+    "    autoscaling_target='0',\n",
+    "    kfserving_endpoint=''\n",
+    "):\n",
+    "\n",
+    "    # define workflow\n",
+    "    kfserving = kfserving_op(action = action,\n",
+    "                             model_name=model_name,\n",
+    "                             default_model_uri=default_model_uri,\n",
+    "                             canary_model_uri=canary_model_uri,\n",
+    "                             canary_model_traffic_percentage=canary_model_traffic_percentage,\n",
+    "                             namespace=namespace,\n",
+    "                             framework=framework,\n",
+    "                             default_custom_model_spec=default_custom_model_spec,\n",
+    "                             canary_custom_model_spec=canary_custom_model_spec,\n",
+    "                             autoscaling_target=autoscaling_target,\n",
+    "                             kfserving_endpoint=kfserving_endpoint).set_image_pull_policy('Always')\n",
+    "\n",
+    "# Compile pipeline\n",
+    "import kfp.compiler as compiler\n",
+    "compiler.Compiler().compile(kfservingPipeline, 'custom.tar.gz')\n",
+    "\n",
+    "# Execute pipeline\n",
+    "run = client.run_pipeline(experiment.id, 'custom-model', 'custom.tar.gz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}