<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Continuous training pipeline with Kubeflow Pipeline and AI Platform
Learning Objectives
Step6:  The pipeline uses a mix of custom and pre-build components.
Pre-build components. The pipeline uses the following pre-build components that are included with the KFP distribution
Step7:  The custom components execute in a container image defined in base_image/Dockerfile.
Step8:  The training step in the pipeline employes the AI Platform Training component to schedule a  AI Platform Training job in a custom training container. The custom training image is defined in trainer_image/Dockerfile.
Step9:  Building and deploying the pipeline
Before deploying to AI Platform Pipelines, the pipeline DSL has to be compiled into a pipeline runtime format, also refered to as a pipeline package.  The runtime format is based on Argo Workflow, which is expressed in YAML. 
Configure environment settings
Update  the below constants  with the settings reflecting your lab environment. 
REGION - the compute region for AI Platform Training and Prediction
ARTIFACT_STORE - the GCS bucket created during installation of AI Platform Pipelines. The bucket name will be similar to qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default.
ENDPOINT - set the ENDPOINT constant to the endpoint to your AI Platform Pipelines instance. Then endpoint to the AI Platform Pipelines instance can be found on the AI Platform Pipelines page in the Google Cloud Console.
Open the SETTINGS for your instance
Use the value of the host variable in the Connect to this Kubeflow Pipelines instance from a Python client via Kubeflow Pipelines SKD section of the SETTINGS window.
Run gsutil ls without URLs to list all of the Cloud Storage buckets under your default project ID.
Step10:  HINT
Step11:  Build the trainer image
Step12:  Note
Step13:  Build the base image for custom components
Step14:  Compile the pipeline
You can compile the DSL using an API from the KFP SDK or using the KFP compiler.
To compile the pipeline DSL using the KFP compiler.
Set the pipeline's compile time settings
The pipeline can run using a security context of the GKE default node pool's service account or the service account defined in the user-gcp-sa secret of the Kubernetes namespace hosting KFP. If you want to use the user-gcp-sa service account you change the value of USE_KFP_SA to True.
Note that the default AI Platform Pipelines configuration does not define the user-gcp-sa secret.
Step15:  Use the CLI compiler to compile the pipeline
Step16:  The result is the covertype_training_pipeline.yaml file.
Step17:  Deploy the pipeline package
Step18:  Submitting pipeline runs
You can trigger pipeline runs using an API from the KFP SDK or using KFP CLI. To submit the run using KFP CLI, execute the following commands. Notice how the pipeline's parameters are passed to the pipeline run.
List the pipelines in AI Platform Pipelines
Step19:  Submit a run
Find the ID of the covertype_continuous_training pipeline you uploaded in the previous step and update the value of PIPELINE_ID .
Step20:  Run the pipeline using the kfp command line by retrieving the variables from the environment to pass to the pipeline where
<ASSISTANT_TASK:>
Python Code:
!grep 'BASE_IMAGE =' -A 5 pipeline/covertype_training_pipeline.py
Explanation: Continuous training pipeline with Kubeflow Pipeline and AI Platform
Learning Objectives:
1. Learn how to use Kubeflow Pipeline(KFP) pre-build components (BiqQuery, AI Platform training and predictions)
1. Learn how to use KFP lightweight python components
1. Learn how to build a KFP with these components
1. Learn how to compile, upload, and run a KFP with the command line
In this lab, you will build, deploy, and run a KFP pipeline that orchestrates BigQuery and AI Platform services to train, tune, and deploy a scikit-learn model.
Understanding the pipeline design
The workflow implemented by the pipeline is defined using a Python based Domain Specific Language (DSL). The pipeline's DSL is in the covertype_training_pipeline.py file that we will generate below.
The pipeline's DSL has been designed to avoid hardcoding any environment specific settings like file paths or connection strings. These settings are provided to the pipeline code through a set of environment variables.
End of explanation
%%writefile ./pipeline/covertype_training_pipeline.py
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
KFP orchestrating BigQuery and Cloud AI Platform services.
import os
from helper_components import evaluate_model
from helper_components import retrieve_best_run
from jinja2 import Template
import kfp
from kfp.components import func_to_container_op
from kfp.dsl.types import Dict
from kfp.dsl.types import GCPProjectID
from kfp.dsl.types import GCPRegion
from kfp.dsl.types import GCSPath
from kfp.dsl.types import String
from kfp.gcp import use_gcp_secret
# Defaults and environment settings
BASE_IMAGE = os.getenv('BASE_IMAGE')
TRAINER_IMAGE = os.getenv('TRAINER_IMAGE')
RUNTIME_VERSION = os.getenv('RUNTIME_VERSION')
PYTHON_VERSION = os.getenv('PYTHON_VERSION')
COMPONENT_URL_SEARCH_PREFIX = os.getenv('COMPONENT_URL_SEARCH_PREFIX')
USE_KFP_SA = os.getenv('USE_KFP_SA')
TRAINING_FILE_PATH = 'datasets/training/data.csv'
VALIDATION_FILE_PATH = 'datasets/validation/data.csv'
TESTING_FILE_PATH = 'datasets/testing/data.csv'
# Parameter defaults
SPLITS_DATASET_ID = 'splits'
HYPERTUNE_SETTINGS = 
{
    "hyperparameters":  {
        "goal": "MAXIMIZE",
        "maxTrials": 6,
        "maxParallelTrials": 3,
        "hyperparameterMetricTag": "accuracy",
        "enableTrialEarlyStopping": True,
        "params": [
            {
                "parameterName": "max_iter",
                "type": "DISCRETE",
                "discreteValues": [500, 1000]
            },
            {
                "parameterName": "alpha",
                "type": "DOUBLE",
                "minValue": 0.0001,
                "maxValue": 0.001,
                "scaleType": "UNIT_LINEAR_SCALE"
            }
        ]
    }
}
# Helper functions
def generate_sampling_query(source_table_name, num_lots, lots):
    Prepares the data sampling query.
    sampling_query_template = 
         SELECT *
         FROM 
             `{{ source_table }}` AS cover
         WHERE 
         MOD(ABS(FARM_FINGERPRINT(TO_JSON_STRING(cover))), {{ num_lots }}) IN ({{ lots }})
         
    query = Template(sampling_query_template).render(
        source_table=source_table_name, num_lots=num_lots, lots=str(lots)[1:-1])
    return query
# Create component factories
component_store = kfp.components.ComponentStore(
    local_search_paths=None, url_search_prefixes=[COMPONENT_URL_SEARCH_PREFIX])
bigquery_query_op = component_store.load_component('bigquery/query')
mlengine_train_op = component_store.load_component('ml_engine/train')
mlengine_deploy_op = component_store.load_component('ml_engine/deploy')
retrieve_best_run_op = func_to_container_op(
    retrieve_best_run, base_image=BASE_IMAGE)
evaluate_model_op = func_to_container_op(evaluate_model, base_image=BASE_IMAGE)
@kfp.dsl.pipeline(
    name='Covertype Classifier Training',
    description='The pipeline training and deploying the Covertype classifierpipeline_yaml'
)
def covertype_train(project_id,
                    region,
                    source_table_name,
                    gcs_root,
                    dataset_id,
                    evaluation_metric_name,
                    evaluation_metric_threshold,
                    model_id,
                    version_id,
                    replace_existing_version,
                    hypertune_settings=HYPERTUNE_SETTINGS,
                    dataset_location='US'):
    Orchestrates training and deployment of an sklearn model.
    # Create the training split
    query = generate_sampling_query(
        source_table_name=source_table_name, num_lots=10, lots=[1, 2, 3, 4])
    training_file_path = '{}/{}'.format(gcs_root, TRAINING_FILE_PATH)
    create_training_split = bigquery_query_op(
        query=query,
        project_id=project_id,
        dataset_id=dataset_id,
        table_id='',
        output_gcs_path=training_file_path,
        dataset_location=dataset_location)
    # Create the validation split
    query = generate_sampling_query(
        source_table_name=source_table_name, num_lots=10, lots=[8])
    validation_file_path = '{}/{}'.format(gcs_root, VALIDATION_FILE_PATH)
    create_validation_split = bigquery_query_op(
        query=query,
        project_id=project_id,
        dataset_id=dataset_id,
        table_id='',
        output_gcs_path=validation_file_path,
        dataset_location=dataset_location)
    # Create the testing split
    query = generate_sampling_query(
        source_table_name=source_table_name, num_lots=10, lots=[9])
    testing_file_path = '{}/{}'.format(gcs_root, TESTING_FILE_PATH)
    create_testing_split = bigquery_query_op(
        query=query,
        project_id=project_id,
        dataset_id=dataset_id,
        table_id='',
        output_gcs_path=testing_file_path,
        dataset_location=dataset_location)
    # Tune hyperparameters
    tune_args = [
        '--training_dataset_path',
        create_training_split.outputs['output_gcs_path'],
        '--validation_dataset_path',
        create_validation_split.outputs['output_gcs_path'], '--hptune', 'True'
    ]
    job_dir = '{}/{}/{}'.format(gcs_root, 'jobdir/hypertune',
                                kfp.dsl.RUN_ID_PLACEHOLDER)
    hypertune = mlengine_train_op(
        project_id=project_id,
        region=region,
        master_image_uri=TRAINER_IMAGE,
        job_dir=job_dir,
        args=tune_args,
        training_input=hypertune_settings)
    # Retrieve the best trial
    get_best_trial = retrieve_best_run_op(
            project_id, hypertune.outputs['job_id'])
    # Train the model on a combined training and validation datasets
    job_dir = '{}/{}/{}'.format(gcs_root, 'jobdir', kfp.dsl.RUN_ID_PLACEHOLDER)
    train_args = [
        '--training_dataset_path',
        create_training_split.outputs['output_gcs_path'],
        '--validation_dataset_path',
        create_validation_split.outputs['output_gcs_path'], '--alpha',
        get_best_trial.outputs['alpha'], '--max_iter',
        get_best_trial.outputs['max_iter'], '--hptune', 'False'
    ]
    train_model = mlengine_train_op(
        project_id=project_id,
        region=region,
        master_image_uri=TRAINER_IMAGE,
        job_dir=job_dir,
        args=train_args)
    # Evaluate the model on the testing split
    eval_model = evaluate_model_op(
        dataset_path=str(create_testing_split.outputs['output_gcs_path']),
        model_path=str(train_model.outputs['job_dir']),
        metric_name=evaluation_metric_name)
    # Deploy the model if the primary metric is better than threshold
    with kfp.dsl.Condition(eval_model.outputs['metric_value'] > evaluation_metric_threshold):
        deploy_model = mlengine_deploy_op(
        model_uri=train_model.outputs['job_dir'],
        project_id=project_id,
        model_id=model_id,
        version_id=version_id,
        runtime_version=RUNTIME_VERSION,
        python_version=PYTHON_VERSION,
        replace_existing_version=replace_existing_version)
    # Configure the pipeline to run using the service account defined
    # in the user-gcp-sa k8s secret
    if USE_KFP_SA == 'True':
        kfp.dsl.get_pipeline_conf().add_op_transformer(
              use_gcp_secret('user-gcp-sa'))
Explanation: The pipeline uses a mix of custom and pre-build components.
Pre-build components. The pipeline uses the following pre-build components that are included with the KFP distribution:
BigQuery query component
AI Platform Training component
AI Platform Deploy component
Custom components. The pipeline uses two custom helper components that encapsulate functionality not available in any of the pre-build components. The components are implemented using the KFP SDK's Lightweight Python Components mechanism. The code for the components is in the helper_components.py file:
Retrieve Best Run. This component retrieves a tuning metric and hyperparameter values for the best run of a AI Platform Training hyperparameter tuning job.
Evaluate Model. This component evaluates a sklearn trained model using a provided metric and a testing dataset.
End of explanation
!cat base_image/Dockerfile
Explanation: The custom components execute in a container image defined in base_image/Dockerfile.
End of explanation
!cat trainer_image/Dockerfile
Explanation: The training step in the pipeline employes the AI Platform Training component to schedule a  AI Platform Training job in a custom training container. The custom training image is defined in trainer_image/Dockerfile.
End of explanation
!gsutil ls
Explanation: Building and deploying the pipeline
Before deploying to AI Platform Pipelines, the pipeline DSL has to be compiled into a pipeline runtime format, also refered to as a pipeline package.  The runtime format is based on Argo Workflow, which is expressed in YAML. 
Configure environment settings
Update  the below constants  with the settings reflecting your lab environment. 
REGION - the compute region for AI Platform Training and Prediction
ARTIFACT_STORE - the GCS bucket created during installation of AI Platform Pipelines. The bucket name will be similar to qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default.
ENDPOINT - set the ENDPOINT constant to the endpoint to your AI Platform Pipelines instance. Then endpoint to the AI Platform Pipelines instance can be found on the AI Platform Pipelines page in the Google Cloud Console.
Open the SETTINGS for your instance
Use the value of the host variable in the Connect to this Kubeflow Pipelines instance from a Python client via Kubeflow Pipelines SKD section of the SETTINGS window.
Run gsutil ls without URLs to list all of the Cloud Storage buckets under your default project ID.
End of explanation
REGION = 'us-central1'
ENDPOINT = '337dd39580cbcbd2-dot-us-central2.pipelines.googleusercontent.com' # TO DO: REPLACE WITH YOUR ENDPOINT
ARTIFACT_STORE_URI = 'gs://qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default'  # TO DO: REPLACE WITH YOUR ARTIFACT_STORE NAME 
PROJECT_ID = !(gcloud config get-value core/project)
PROJECT_ID = PROJECT_ID[0]
Explanation: HINT: 
For ENDPOINT, use the value of the host variable in the Connect to this Kubeflow Pipelines instance from a Python client via Kubeflow Pipelines SDK section of the SETTINGS window.
For ARTIFACT_STORE_URI, copy the bucket name which starts with the qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default prefix from the previous cell output. Your copied value should look like 'gs://qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default'
End of explanation
IMAGE_NAME='trainer_image'
TAG='latest'
TRAINER_IMAGE='gcr.io/{}/{}:{}'.format(PROJECT_ID, IMAGE_NAME, TAG)
Explanation: Build the trainer image
End of explanation
!gcloud builds submit --timeout 15m --tag $TRAINER_IMAGE trainer_image
Explanation: Note: Please ignore any incompatibility ERROR that may appear for the packages visions as it will not affect the lab's functionality.
End of explanation
IMAGE_NAME='base_image'
TAG='latest'
BASE_IMAGE='gcr.io/{}/{}:{}'.format(PROJECT_ID, IMAGE_NAME, TAG)
!gcloud builds submit --timeout 15m --tag $BASE_IMAGE base_image
Explanation: Build the base image for custom components
End of explanation
USE_KFP_SA = False
COMPONENT_URL_SEARCH_PREFIX = 'https://raw.githubusercontent.com/kubeflow/pipelines/0.2.5/components/gcp/'
RUNTIME_VERSION = '1.15'
PYTHON_VERSION = '3.7'
%env USE_KFP_SA={USE_KFP_SA}
%env BASE_IMAGE={BASE_IMAGE}
%env TRAINER_IMAGE={TRAINER_IMAGE}
%env COMPONENT_URL_SEARCH_PREFIX={COMPONENT_URL_SEARCH_PREFIX}
%env RUNTIME_VERSION={RUNTIME_VERSION}
%env PYTHON_VERSION={PYTHON_VERSION}
Explanation: Compile the pipeline
You can compile the DSL using an API from the KFP SDK or using the KFP compiler.
To compile the pipeline DSL using the KFP compiler.
Set the pipeline's compile time settings
The pipeline can run using a security context of the GKE default node pool's service account or the service account defined in the user-gcp-sa secret of the Kubernetes namespace hosting KFP. If you want to use the user-gcp-sa service account you change the value of USE_KFP_SA to True.
Note that the default AI Platform Pipelines configuration does not define the user-gcp-sa secret.
End of explanation
!dsl-compile --py pipeline/covertype_training_pipeline.py --output covertype_training_pipeline.yaml
Explanation: Use the CLI compiler to compile the pipeline
End of explanation
!head covertype_training_pipeline.yaml
Explanation: The result is the covertype_training_pipeline.yaml file.
End of explanation
PIPELINE_NAME='covertype_continuous_training'
!kfp --endpoint $ENDPOINT pipeline upload \
-p $PIPELINE_NAME \
covertype_training_pipeline.yaml
Explanation: Deploy the pipeline package
End of explanation
!kfp --endpoint $ENDPOINT pipeline list
Explanation: Submitting pipeline runs
You can trigger pipeline runs using an API from the KFP SDK or using KFP CLI. To submit the run using KFP CLI, execute the following commands. Notice how the pipeline's parameters are passed to the pipeline run.
List the pipelines in AI Platform Pipelines
End of explanation
PIPELINE_ID='0918568d-758c-46cf-9752-e04a4403cd84' # TO DO: REPLACE WITH YOUR PIPELINE ID 
EXPERIMENT_NAME = 'Covertype_Classifier_Training'
RUN_ID = 'Run_001'
SOURCE_TABLE = 'covertype_dataset.covertype'
DATASET_ID = 'splits'
EVALUATION_METRIC = 'accuracy'
EVALUATION_METRIC_THRESHOLD = '0.69'
MODEL_ID = 'covertype_classifier'
VERSION_ID = 'v01'
REPLACE_EXISTING_VERSION = 'True'
GCS_STAGING_PATH = '{}/staging'.format(ARTIFACT_STORE_URI)
Explanation: Submit a run
Find the ID of the covertype_continuous_training pipeline you uploaded in the previous step and update the value of PIPELINE_ID .
End of explanation
!kfp --endpoint $ENDPOINT run submit \
-e $EXPERIMENT_NAME \
-r $RUN_ID \
-p $PIPELINE_ID \
project_id=$PROJECT_ID \
gcs_root=$GCS_STAGING_PATH \
region=$REGION \
source_table_name=$SOURCE_TABLE \
dataset_id=$DATASET_ID \
evaluation_metric_name=$EVALUATION_METRIC \
evaluation_metric_threshold=$EVALUATION_METRIC_THRESHOLD \
model_id=$MODEL_ID \
version_id=$VERSION_ID \
replace_existing_version=$REPLACE_EXISTING_VERSION
Explanation: Run the pipeline using the kfp command line by retrieving the variables from the environment to pass to the pipeline where:
EXPERIMENT_NAME is set to the experiment used to run the pipeline. You can choose any name you want. If the experiment does not exist it will be created by the command
RUN_ID is the name of the run. You can use an arbitrary name
PIPELINE_ID is the id of your pipeline. Use the value retrieved by the   kfp pipeline list command
GCS_STAGING_PATH is the URI to the Cloud Storage location used by the pipeline to store intermediate files. By default, it is set to the staging folder in your artifact store.
REGION is a compute region for AI Platform Training and Prediction. 
You should be already familiar with these and other parameters passed to the command. If not go back and review the pipeline code.
End of explanation
<END_TASK>