780 lines
25 KiB
Plaintext
780 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Copyright (c) Facebook, Inc. and its affiliates.\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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"# You may obtain a copy of the License at\n",
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"#\n",
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"# http://www.apache.org/licenses/LICENSE-2.0\n",
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"#\n",
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"# Unless required by applicable law or agreed to in writing, software\n",
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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Bert Pipeline : PyTorch BERT News Classfication\n",
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"\n",
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"This notebook shows PyTorch BERT end-to-end news classification example using Kubeflow Pipelines.\n",
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"\n",
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"\n",
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"An example notebook that demonstrates how to:\n",
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"\n",
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"* Get different tasks needed for the pipeline\n",
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"* Create a Kubeflow pipeline\n",
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"* Include Pytorch KFP components to preprocess, train, visualize and deploy the model in the pipeline\n",
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"* Submit a job for execution\n",
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"* Query(prediction and explain) the final deployed model\n",
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"* Interpretation of the model using the Captum Insights\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"! pip uninstall -y kfp\n",
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"! pip install --no-cache-dir kfp captum"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'1.8.12'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import kfp\n",
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"import json\n",
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"import os\n",
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"from kfp.onprem import use_k8s_secret\n",
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"from kfp import components\n",
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"from kfp.components import load_component_from_file, load_component_from_url\n",
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"from kfp import dsl\n",
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"from kfp import compiler\n",
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"\n",
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"kfp.__version__"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Enter your gateway and the cookie\n",
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"[Use this extension on chrome to get token]( https://chrome.google.com/webstore/detail/editthiscookie/fngmhnnpilhplaeedifhccceomclgfbg?hl=en)\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Update values for the ingress gateway and auth session"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"INGRESS_GATEWAY='http://istio-ingressgateway.istio-system.svc.cluster.local'\n",
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"AUTH=\"<enter your token here>\"\n",
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"NAMESPACE=\"kubeflow-user-example-com\"\n",
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"COOKIE=\"authservice_session=\"+AUTH\n",
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"EXPERIMENT=\"Default\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set Log bucket and Tensorboard Image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"MINIO_ENDPOINT=\"http://minio-service.kubeflow:9000\"\n",
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"LOG_BUCKET=\"mlpipeline\"\n",
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"TENSORBOARD_IMAGE=\"public.ecr.aws/pytorch-samples/tboard:latest\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"client = kfp.Client(host=INGRESS_GATEWAY+\"/pipeline\", cookies=COOKIE)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<a href=\"http://istio-ingressgateway.istio-system.svc.cluster.local/pipeline/#/experiments/details/b4bee8c3-381b-42a0-9494-bc81eb9aa359\" target=\"_blank\" >Experiment details</a>."
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"{'created_at': datetime.datetime(2022, 4, 21, 9, 45, 22, tzinfo=tzlocal()),\n",
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" 'description': None,\n",
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" 'id': 'b4bee8c3-381b-42a0-9494-bc81eb9aa359',\n",
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" 'name': 'Default',\n",
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" 'resource_references': [{'key': {'id': 'kubeflow-user-example-com',\n",
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" 'type': 'NAMESPACE'},\n",
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" 'name': None,\n",
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" 'relationship': 'OWNER'}],\n",
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" 'storage_state': 'STORAGESTATE_AVAILABLE'}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"client.create_experiment(EXPERIMENT)\n",
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"experiments = client.list_experiments(namespace=NAMESPACE)\n",
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"my_experiment = experiments.experiments[0]\n",
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"my_experiment"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set Inference parameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"DEPLOY_NAME=\"bertserve\"\n",
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"MODEL_NAME=\"bert\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing prediction_component.yaml\n",
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"Processing ax_complete_trials_component.yaml\n",
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"Processing preprocess_component.yaml\n",
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"Processing train_component.yaml\n",
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"Processing tensorboard_component.yaml\n",
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"Processing ax_generate_trials_component.yaml\n",
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"Processing minio_component.yaml\n",
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"Processing copy_component.yaml\n",
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"Processing ax_train_component.yaml\n"
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]
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}
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],
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"source": [
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"! python utils/generate_templates.py bert/template_mapping.json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"prepare_tensorboard_op = load_component_from_file(\"yaml/tensorboard_component.yaml\")\n",
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"prep_op = components.load_component_from_file(\n",
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" \"yaml/preprocess_component.yaml\"\n",
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")\n",
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"train_op = components.load_component_from_file(\n",
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" \"yaml/train_component.yaml\"\n",
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")\n",
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"deploy_op = load_component_from_file(\n",
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" \"../../../components/kserve/component.yaml\"\n",
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")\n",
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"minio_op = components.load_component_from_file(\n",
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" \"yaml/minio_component.yaml\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define pipeline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"@dsl.pipeline(name=\"Training pipeline\", description=\"Sample training job test\")\n",
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"def pytorch_bert( # pylint: disable=too-many-arguments\n",
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" minio_endpoint=MINIO_ENDPOINT,\n",
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" log_bucket=LOG_BUCKET,\n",
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" log_dir=f\"tensorboard/logs/{dsl.RUN_ID_PLACEHOLDER}\",\n",
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" mar_path=f\"mar/{dsl.RUN_ID_PLACEHOLDER}/model-store\",\n",
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" config_prop_path=f\"mar/{dsl.RUN_ID_PLACEHOLDER}/config\",\n",
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" model_uri=f\"s3://mlpipeline/mar/{dsl.RUN_ID_PLACEHOLDER}\",\n",
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" tf_image=TENSORBOARD_IMAGE,\n",
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" deploy=DEPLOY_NAME,\n",
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" namespace=NAMESPACE,\n",
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" confusion_matrix_log_dir=f\"confusion_matrix/{dsl.RUN_ID_PLACEHOLDER}/\",\n",
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" num_samples=1000,\n",
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" max_epochs=1\n",
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"):\n",
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" \"\"\"Thid method defines the pipeline tasks and operations\"\"\"\n",
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" prepare_tb_task = prepare_tensorboard_op(\n",
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" log_dir_uri=f\"s3://{log_bucket}/{log_dir}\",\n",
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" image=tf_image,\n",
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" pod_template_spec=json.dumps({\n",
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" \"spec\": {\n",
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" \"containers\": [{\n",
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" \"env\": [\n",
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" {\n",
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" \"name\": \"AWS_ACCESS_KEY_ID\",\n",
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" \"valueFrom\": {\n",
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" \"secretKeyRef\": {\n",
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" \"name\": \"mlpipeline-minio-artifact\",\n",
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" \"key\": \"accesskey\",\n",
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" }\n",
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" },\n",
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" },\n",
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" {\n",
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" \"name\": \"AWS_SECRET_ACCESS_KEY\",\n",
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" \"valueFrom\": {\n",
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" \"secretKeyRef\": {\n",
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" \"name\": \"mlpipeline-minio-artifact\",\n",
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" \"key\": \"secretkey\",\n",
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" }\n",
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" },\n",
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" },\n",
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" {\n",
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" \"name\": \"AWS_REGION\",\n",
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" \"value\": \"minio\"\n",
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" },\n",
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" {\n",
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" \"name\": \"S3_ENDPOINT\",\n",
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" \"value\": f\"{minio_endpoint}\",\n",
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" },\n",
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" {\n",
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" \"name\": \"S3_USE_HTTPS\",\n",
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" \"value\": \"0\"\n",
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" },\n",
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" {\n",
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" \"name\": \"S3_VERIFY_SSL\",\n",
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" \"value\": \"0\"\n",
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" },\n",
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" ]\n",
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" }]\n",
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" }\n",
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" }),\n",
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" ).set_display_name(\"Visualization\")\n",
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"\n",
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" prep_task = (\n",
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" prep_op().after(prepare_tb_task\n",
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" ).set_display_name(\"Preprocess & Transform\")\n",
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" )\n",
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" confusion_matrix_url = f\"minio://{log_bucket}/{confusion_matrix_log_dir}\"\n",
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" script_args = f\"model_name=bert.pth,\" \\\n",
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" f\"num_samples={num_samples},\" \\\n",
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" f\"confusion_matrix_url={confusion_matrix_url}\"\n",
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" # For GPU , set device count and strategy type\n",
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" ptl_args = f\"max_epochs={max_epochs},accelerator=gpu,profiler=pytorch,devices=0,strategy=None\"\n",
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" train_task = (\n",
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" train_op(\n",
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" input_data=prep_task.outputs[\"output_data\"],\n",
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" script_args=script_args,\n",
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" ptl_arguments=ptl_args\n",
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" ).after(prep_task).set_display_name(\"Training\")\n",
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" # For allocating resources, uncomment below lines\n",
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" # .set_memory_request('600M')\n",
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" # .set_memory_limit('1200M')\n",
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" # .set_cpu_request('700m')\n",
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" # .set_cpu_limit('1400m')\n",
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" # For GPU uncomment below line and set GPU limit and node selector\n",
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" # .set_gpu_limit(1).add_node_selector_constraint('cloud.google.com/gke-accelerator','nvidia-tesla-p4')\n",
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" )\n",
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"\n",
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" (\n",
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" minio_op(\n",
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" bucket_name=\"mlpipeline\",\n",
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" folder_name=log_dir,\n",
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" input_path=train_task.outputs[\"tensorboard_root\"],\n",
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" filename=\"\",\n",
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" ).after(train_task).set_display_name(\"Tensorboard Events Pusher\")\n",
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" )\n",
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" minio_mar_upload = (\n",
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" minio_op(\n",
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" bucket_name=\"mlpipeline\",\n",
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" folder_name=mar_path,\n",
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" input_path=train_task.outputs[\"checkpoint_dir\"],\n",
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" filename=\"bert_test.mar\",\n",
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" ).after(train_task).set_display_name(\"Mar Pusher\")\n",
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" )\n",
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" (\n",
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" minio_op(\n",
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" bucket_name=\"mlpipeline\",\n",
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" folder_name=config_prop_path,\n",
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" input_path=train_task.outputs[\"checkpoint_dir\"],\n",
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" filename=\"config.properties\",\n",
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" ).after(train_task).set_display_name(\"Conifg Pusher\")\n",
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" )\n",
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"\n",
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" model_uri = str(model_uri)\n",
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" # pylint: disable=unused-variable\n",
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" isvc_yaml = \"\"\"\n",
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" apiVersion: \"serving.kserve.io/v1beta1\"\n",
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" kind: \"InferenceService\"\n",
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" metadata:\n",
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" name: {}\n",
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" namespace: {}\n",
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" spec:\n",
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" predictor:\n",
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" serviceAccountName: sa\n",
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" pytorch:\n",
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" protocolVersion: v2\n",
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" storageUri: {}\n",
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" resources:\n",
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" requests: \n",
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" cpu: 4\n",
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" memory: 8Gi\n",
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" limits:\n",
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" cpu: 4\n",
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" memory: 8Gi\n",
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" \"\"\".format(deploy, namespace, model_uri)\n",
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"\n",
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" # For GPU inference use below yaml with gpu count and accelerator\n",
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" gpu_count = \"1\"\n",
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" accelerator = \"nvidia-tesla-p4\"\n",
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" isvc_gpu_yaml = \"\"\"\n",
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" apiVersion: \"serving.kserve.io/v1beta1\"\n",
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" kind: \"InferenceService\"\n",
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" metadata:\n",
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" name: {}\n",
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" namespace: {}\n",
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" spec:\n",
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" predictor:\n",
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" serviceAccountName: sa\n",
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" pytorch:\n",
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" protocolVersion: v2\n",
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" storageUri: {}\n",
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" resources:\n",
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" requests: \n",
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" cpu: 4\n",
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" memory: 8Gi\n",
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" limits:\n",
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" cpu: 4\n",
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" memory: 8Gi\n",
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" nvidia.com/gpu: {}\n",
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" nodeSelector:\n",
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" cloud.google.com/gke-accelerator: {}\n",
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"\"\"\".format(deploy, namespace, model_uri, gpu_count, accelerator)\n",
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" # Update inferenceservice_yaml for GPU inference\n",
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" deploy_task = (\n",
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" deploy_op(action=\"apply\", inferenceservice_yaml=isvc_yaml\n",
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" ).after(minio_mar_upload).set_display_name(\"Deployer\")\n",
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" )\n",
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"\n",
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" dsl.get_pipeline_conf().add_op_transformer(\n",
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" use_k8s_secret(\n",
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" secret_name=\"mlpipeline-minio-artifact\",\n",
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" k8s_secret_key_to_env={\n",
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" \"secretkey\": \"MINIO_SECRET_KEY\",\n",
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" \"accesskey\": \"MINIO_ACCESS_KEY\",\n",
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" },\n",
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" )\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compile pipeline\n",
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"compiler.Compiler().compile(pytorch_bert, 'pytorch.tar.gz', type_check=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<a href=\"http://istio-ingressgateway.istio-system.svc.cluster.local/pipeline/#/runs/details/4f635288-b8d2-47c7-b05e-d7db4829f5bd\" target=\"_blank\" >Run details</a>."
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Execute pipeline\n",
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"run = client.run_pipeline(my_experiment.id, 'pytorch-bert', 'pytorch.tar.gz')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Wait for inference service below to go to `READY True` state."
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"NAME URL READY PREV LATEST PREVROLLEDOUTREVISION LATESTREADYREVISION AGE\n",
|
|
"bertserve http://bertserve.kubeflow-user-example-com.example.com True 100 bertserve-predictor-default-00003 160m\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"!kubectl get isvc $DEPLOY"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Get Inferenceservice name"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'bertserve.kubeflow-user-example-com.example.com'"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"INFERENCE_SERVICE_LIST = ! kubectl get isvc {DEPLOY_NAME} -n {NAMESPACE} -o json | python3 -c \"import sys, json; print(json.load(sys.stdin)['status']['url'])\"| tr -d '\"' | cut -d \"/\" -f 3\n",
|
|
"INFERENCE_SERVICE_NAME = INFERENCE_SERVICE_LIST[0]\n",
|
|
"INFERENCE_SERVICE_NAME"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Prediction Request"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{\n",
|
|
" \"id\": \"d3b15cad-50a2-4eaf-80ce-8b0a428bd298\",\n",
|
|
" \"inputs\": [{\n",
|
|
" \"name\": \"4b7c7d4a-51e4-43c8-af61-04639f6ef4bc\",\n",
|
|
" \"shape\": -1,\n",
|
|
" \"datatype\": \"BYTES\",\n",
|
|
" \"data\": \"Bloomberg has reported on the economy\"\n",
|
|
" }\n",
|
|
" ]\n",
|
|
"}"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"! cat ./bert/sample.txt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"!curl -v -H \"Host: $INFERENCE_SERVICE_NAME\" -H \"Cookie: $COOKIE\" \"$INGRESS_GATEWAY/v2/models/$MODEL_NAME/infer\" -d @./bert/sample.txt > bert_prediction_output.json"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{\"id\": \"d3b15cad-50a2-4eaf-80ce-8b0a428bd298\", \"model_name\": \"bert_test\", \"model_version\": \"1\", \"outputs\": [{\"name\": \"predict\", \"shape\": [], \"datatype\": \"BYTES\", \"data\": [\"\\\"Business\\\"\"]}]}"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"! cat bert_prediction_output.json"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Explanation Request"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"!curl -v -H \"Host: $INFERENCE_SERVICE_NAME\" -H \"Cookie: $COOKIE\" \"$INGRESS_GATEWAY/v2/models/$MODEL_NAME/explain\" -d @./bert/sample.txt > bert_explaination_output.json"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{\"id\": \"d3b15cad-50a2-4eaf-80ce-8b0a428bd298\", \"model_name\": \"bert_test\", \"model_version\": \"1\", \"outputs\": [{\"name\": \"explain\", \"shape\": [], \"datatype\": \"BYTES\", \"data\": [{\"words\": [\"bloomberg\", \"has\", \"reported\", \"on\", \"the\", \"economy\"], \"importances\": [0.2124089759942075, 0.3070123112652129, -0.3175794877732026, -0.4493290921520886, -0.23262562691072097, 0.7097589881393321], \"delta\": 0.01156902069987975}]}]}"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"! cat bert_explaination_output.json"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {
|
|
"scrolled": true,
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"explanations_json = json.loads(open(\"./bert_explaination_output.json\", \"r\").read())\n",
|
|
"explanations_json"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"prediction_json = json.loads(open(\"./bert_prediction_output.json\", \"r\").read())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import torch\n",
|
|
"attributions = explanations_json[\"outputs\"][0][\"data\"][0]['importances']\n",
|
|
"tokens = explanations_json[\"outputs\"][0][\"data\"][0]['words']\n",
|
|
"delta = explanations_json[\"outputs\"][0][\"data\"][0]['delta']\n",
|
|
"\n",
|
|
"attributions = torch.tensor(attributions)\n",
|
|
"pred_prob = 0.75\n",
|
|
"pred_class = str(prediction_json[\"outputs\"][0][\"data\"][0]).strip('\"\"')\n",
|
|
"true_class = \"Business\"\n",
|
|
"attr_class =\"world\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Visualization of Predictions"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from captum.attr import visualization\n",
|
|
"vis_data_records =[]\n",
|
|
"vis_data_records.append(visualization.VisualizationDataRecord(\n",
|
|
" attributions,\n",
|
|
" pred_prob,\n",
|
|
" pred_class,\n",
|
|
" true_class,\n",
|
|
" attr_class,\n",
|
|
" attributions.sum(), \n",
|
|
" tokens,\n",
|
|
" delta))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px; padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(0, 75%, 60%)\"></span> Negative <span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(0, 75%, 100%)\"></span> Neutral <span style=\"display: inline-block; width: 10px; height: 10px; border: 1px solid; background-color: hsl(120, 75%, 50%)\"></span> Positive </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>Business</b></text></td><td><text style=\"padding-right:2em\"><b>Business (0.75)</b></text></td><td><text style=\"padding-right:2em\"><b>world</b></text></td><td><text style=\"padding-right:2em\"><b>0.23</b></text></td><td><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0; line-height:1.75\"><font color=\"black\"> bloomberg </font></mark><mark style=\"background-color: hsl(120, 75%, 85%); opacity:1.0; line-height:1.75\"><font color=\"black\"> has </font></mark><mark style=\"background-color: hsl(0, 75%, 88%); opacity:1.0; line-height:1.75\"><font color=\"black\"> reported </font></mark><mark style=\"background-color: hsl(0, 75%, 83%); opacity:1.0; line-height:1.75\"><font color=\"black\"> on </font></mark><mark style=\"background-color: hsl(0, 75%, 91%); opacity:1.0; line-height:1.75\"><font color=\"black\"> the </font></mark><mark style=\"background-color: hsl(120, 75%, 65%); opacity:1.0; line-height:1.75\"><font color=\"black\"> economy </font></mark></td><tr></table>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"vis = visualization.visualize_text(vis_data_records)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### visualization appreas as below\n",
|
|
""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Cleanup Script"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"! kubectl delete --all isvc -n $NAMESPACE"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"! kubectl delete pod --field-selector=status.phase==Succeeded -n $NAMESPACE"
|
|
]
|
|
},
|
|
{
|
|
"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": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|