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7_get_data_train_upload.yaml
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7_get_data_train_upload.yaml
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# PIPELINE DEFINITION
# Name: 7-get-data-train-upload
components:
comp-get-data:
executorLabel: exec-get-data
outputDefinitions:
artifacts:
train_data_output_path:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
validate_data_output_path:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
comp-train-model:
executorLabel: exec-train-model
inputDefinitions:
artifacts:
train_data_input_path:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
validate_data_input_path:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
model_output_path:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
comp-upload-model:
executorLabel: exec-upload-model
inputDefinitions:
artifacts:
input_model_path:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
deploymentSpec:
executors:
exec-get-data:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- get_data
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.8.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' && \"\
$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef get_data(train_data_output_path: OutputPath(), validate_data_output_path:\
\ OutputPath()):\n import urllib.request\n print(\"starting download...\"\
)\n print(\"downloading training data\")\n url = \"https://raw.githubusercontent.com/cfchase/fraud-detection/main/data/train.csv\"\
\n urllib.request.urlretrieve(url, train_data_output_path)\n print(\"\
train data downloaded\")\n print(\"downloading validation data\")\n \
\ url = \"https://raw.githubusercontent.com/cfchase/fraud-detection/main/data/validate.csv\"\
\n urllib.request.urlretrieve(url, validate_data_output_path)\n print(\"\
validation data downloaded\")\n\n"
image: quay.io/modh/runtime-images:runtime-cuda-tensorflow-ubi9-python-3.9-2024a-20240523
exec-train-model:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- train_model
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.8.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'onnx' 'onnxruntime'\
\ 'tf2onnx' && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef train_model(train_data_input_path: InputPath(), validate_data_input_path:\
\ InputPath(), model_output_path: OutputPath()):\n import numpy as np\n\
\ import pandas as pd\n from keras.models import Sequential\n from\
\ keras.layers import Dense, Dropout, BatchNormalization, Activation\n \
\ from sklearn.model_selection import train_test_split\n from sklearn.preprocessing\
\ import StandardScaler\n from sklearn.utils import class_weight\n \
\ import tf2onnx\n import onnx\n import pickle\n from pathlib\
\ import Path\n\n # Load the CSV data which we will use to train the\
\ model.\n # It contains the following fields:\n # distancefromhome\
\ - The distance from home where the transaction happened.\n # distancefromlast_transaction\
\ - The distance from last transaction happened.\n # ratiotomedianpurchaseprice\
\ - Ratio of purchased price compared to median purchase price.\n # \
\ repeat_retailer - If it's from a retailer that already has been purchased\
\ from before.\n # used_chip - If the (credit card) chip was used.\n\
\ # usedpinnumber - If the PIN number was used.\n # online_order\
\ - If it was an online order.\n # fraud - If the transaction is fraudulent.\n\
\n\n feature_indexes = [\n 1, # distance_from_last_transaction\n\
\ 2, # ratio_to_median_purchase_price\n 4, # used_chip\n\
\ 5, # used_pin_number\n 6, # online_order\n ]\n\n \
\ label_indexes = [\n 7 # fraud\n ]\n\n X_train = pd.read_csv(train_data_input_path)\n\
\ y_train = X_train.iloc[:, label_indexes]\n X_train = X_train.iloc[:,\
\ feature_indexes]\n\n X_val = pd.read_csv(validate_data_input_path)\n\
\ y_val = X_val.iloc[:, label_indexes]\n X_val = X_val.iloc[:, feature_indexes]\n\
\n # Scale the data to remove mean and have unit variance. The data will\
\ be between -1 and 1, which makes it a lot easier for the model to learn\
\ than random (and potentially large) values.\n # It is important to\
\ only fit the scaler to the training data, otherwise you are leaking information\
\ about the global distribution of variables (which is influenced by the\
\ test set) into the training set.\n\n scaler = StandardScaler()\n\n\
\ X_train = scaler.fit_transform(X_train.values)\n\n Path(\"artifact\"\
).mkdir(parents=True, exist_ok=True)\n with open(\"artifact/scaler.pkl\"\
, \"wb\") as handle:\n pickle.dump(scaler, handle)\n\n # Since\
\ the dataset is unbalanced (it has many more non-fraud transactions than\
\ fraudulent ones), set a class weight to weight the few fraudulent transactions\
\ higher than the many non-fraud transactions.\n class_weights = class_weight.compute_class_weight('balanced',\
\ classes=np.unique(y_train), y=y_train.values.ravel())\n class_weights\
\ = {i: class_weights[i] for i in range(len(class_weights))}\n\n # Build\
\ the model, the model we build here is a simple fully connected deep neural\
\ network, containing 3 hidden layers and one output layer.\n\n model\
\ = Sequential()\n model.add(Dense(32, activation='relu', input_dim=len(feature_indexes)))\n\
\ model.add(Dropout(0.2))\n model.add(Dense(32))\n model.add(BatchNormalization())\n\
\ model.add(Activation('relu'))\n model.add(Dropout(0.2))\n model.add(Dense(32))\n\
\ model.add(BatchNormalization())\n model.add(Activation('relu'))\n\
\ model.add(Dropout(0.2))\n model.add(Dense(1, activation='sigmoid'))\n\
\ model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n\
\ model.summary()\n\n # Train the model and get performance\n\n \
\ epochs = 2\n history = model.fit(X_train, y_train, epochs=epochs,\n\
\ validation_data=(scaler.transform(X_val.values),\
\ y_val),\n verbose=True, class_weight=class_weights)\n\
\n # Save the model as ONNX for easy use of ModelMesh\n model_proto,\
\ _ = tf2onnx.convert.from_keras(model)\n print(model_output_path)\n\
\ onnx.save(model_proto, model_output_path)\n\n"
image: quay.io/modh/runtime-images:runtime-cuda-tensorflow-ubi9-python-3.9-2024a-20240523
exec-upload-model:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- upload_model
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.8.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'boto3' 'botocore'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef upload_model(input_model_path: InputPath()):\n import os\n\
\ import boto3\n import botocore\n\n aws_access_key_id = os.environ.get('AWS_ACCESS_KEY_ID')\n\
\ aws_secret_access_key = os.environ.get('AWS_SECRET_ACCESS_KEY')\n \
\ endpoint_url = os.environ.get('AWS_S3_ENDPOINT')\n region_name =\
\ os.environ.get('AWS_DEFAULT_REGION')\n bucket_name = os.environ.get('AWS_S3_BUCKET')\n\
\n s3_key = os.environ.get(\"S3_KEY\")\n\n session = boto3.session.Session(aws_access_key_id=aws_access_key_id,\n\
\ aws_secret_access_key=aws_secret_access_key)\n\
\n s3_resource = session.resource(\n 's3',\n config=botocore.client.Config(signature_version='s3v4'),\n\
\ endpoint_url=endpoint_url,\n region_name=region_name)\n\n\
\ bucket = s3_resource.Bucket(bucket_name)\n\n print(f\"Uploading\
\ {s3_key}\")\n bucket.upload_file(input_model_path, s3_key)\n\n"
env:
- name: S3_KEY
value: models/fraud/1/model.onnx
image: quay.io/modh/runtime-images:runtime-cuda-tensorflow-ubi9-python-3.9-2024a-20240523
pipelineInfo:
name: 7-get-data-train-upload
root:
dag:
tasks:
get-data:
cachingOptions:
enableCache: true
componentRef:
name: comp-get-data
taskInfo:
name: get-data
train-model:
cachingOptions:
enableCache: true
componentRef:
name: comp-train-model
dependentTasks:
- get-data
inputs:
artifacts:
train_data_input_path:
taskOutputArtifact:
outputArtifactKey: train_data_output_path
producerTask: get-data
validate_data_input_path:
taskOutputArtifact:
outputArtifactKey: validate_data_output_path
producerTask: get-data
taskInfo:
name: train-model
upload-model:
cachingOptions:
enableCache: true
componentRef:
name: comp-upload-model
dependentTasks:
- train-model
inputs:
artifacts:
input_model_path:
taskOutputArtifact:
outputArtifactKey: model_output_path
producerTask: train-model
taskInfo:
name: upload-model
schemaVersion: 2.1.0
sdkVersion: kfp-2.8.0
---
platforms:
kubernetes:
deploymentSpec:
executors:
exec-upload-model:
secretAsEnv:
- keyToEnv:
- envVar: AWS_ACCESS_KEY_ID
secretKey: AWS_ACCESS_KEY_ID
- envVar: AWS_SECRET_ACCESS_KEY
secretKey: AWS_SECRET_ACCESS_KEY
- envVar: AWS_DEFAULT_REGION
secretKey: AWS_DEFAULT_REGION
- envVar: AWS_S3_BUCKET
secretKey: AWS_S3_BUCKET
- envVar: AWS_S3_ENDPOINT
secretKey: AWS_S3_ENDPOINT
secretName: aws-connection-my-storage