How to use the kfp.components function in kfp

#!/usr/bin/env python3

import kfp
from kfp import components
from kfp import dsl
from kfp.aws import use_aws_secret

sagemaker_workteam_op = components.load_component_from_file('../../../../components/aws/sagemaker/workteam/component.yaml')
sagemaker_gt_op = components.load_component_from_file('../../../../components/aws/sagemaker/ground_truth/component.yaml')
sagemaker_train_op = components.load_component_from_file('../../../../components/aws/sagemaker/train/component.yaml')

@dsl.pipeline(
    name='Ground Truth image classification test pipeline',
    description='SageMaker Ground Truth job test'
)
def ground_truth_test(region='us-west-2',
    team_name='ground-truth-demo-team',
    team_description='Team for mini image classification labeling job',
    user_pool='',
    user_groups='',
    client_id='',
    ground_truth_train_job_name='mini-image-classification-demo-train',
    ground_truth_validation_job_name='mini-image-classification-demo-validation',
    ground_truth_label_attribute_name='category',

# limitations under the License.

# flake8: noqa TODO

import kfp.dsl as dsl
import kfp.gcp as gcp
import kfp.components as comp
import datetime
import json
import os

dataflow_python_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/gcp/dataflow/launch_python/component.yaml')
cloudml_train_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/gcp/ml_engine/train/component.yaml')
cloudml_deploy_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/gcp/ml_engine/deploy/component.yaml')


def resnet_preprocess_op(project_id: 'GcpProject', output: 'GcsUri', staging_dir: 'GcsUri', train_csv: 'GcsUri[text/csv]',
                         validation_csv: 'GcsUri[text/csv]', labels, train_size: 'Integer', validation_size: 'Integer',
                         step_name='preprocess'):
    return dataflow_python_op(
        python_file_path='gs://ml-pipeline-playground/samples/ml_engine/resnet-cmle/preprocess/preprocess.py',
        project_id=project_id,
        requirements_file_path='gs://ml-pipeline-playground/samples/ml_engine/resnet-cmle/preprocess/requirements.txt',
        staging_dir=staging_dir,
        args=json.dumps([
            '--train_csv', str(train_csv),
            '--validation_csv', str(validation_csv),
            '--labels', str(labels),
            '--output_dir', str(output),

# 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.

import kfp.dsl as dsl
from kfp import components
import json

kfserving_op = components.load_component_from_file('component.yaml')

@dsl.pipeline(
  name='kfserving pipeline',
  description='A pipeline for kfserving.'
)
def kfservingPipeline(
    action = 'create',
    model_name='tensorflow-sample',
    default_model_uri='gs://kfserving-samples/models/tensorflow/flowers',
    canary_model_uri='gs://kfserving-samples/models/tensorflow/flowers-2',
    canary_model_traffic_percentage='10',
    namespace='kubeflow',
    framework='tensorflow',
    default_custom_model_spec='{}',
    canary_custom_model_spec='{}',
    autoscaling_target=0,

# limitations under the License.


import kfp
from kfp import components
from kfp import dsl
from kfp import gcp
from kfp import onprem

platform = 'onprem'

dataflow_tf_data_validation_op  = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/74d8e592174ae90175f66c3c00ba76a835cfba6d/components/dataflow/tfdv/component.yaml')
dataflow_tf_transform_op        = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/74d8e592174ae90175f66c3c00ba76a835cfba6d/components/dataflow/tft/component.yaml')
tf_train_op                     = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/74d8e592174ae90175f66c3c00ba76a835cfba6d/components/kubeflow/dnntrainer/component.yaml')
dataflow_tf_model_analyze_op    = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/74d8e592174ae90175f66c3c00ba76a835cfba6d/components/dataflow/tfma/component.yaml')
dataflow_tf_predict_op          = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/74d8e592174ae90175f66c3c00ba76a835cfba6d/components/dataflow/predict/component.yaml')

confusion_matrix_op             = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/74d8e592174ae90175f66c3c00ba76a835cfba6d/components/local/confusion_matrix/component.yaml')
roc_op                          = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/74d8e592174ae90175f66c3c00ba76a835cfba6d/components/local/roc/component.yaml')

kubeflow_deploy_op              = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/74d8e592174ae90175f66c3c00ba76a835cfba6d/components/kubeflow/deployer/component.yaml')

@dsl.pipeline(
  name='TFX Taxi Cab Classification Pipeline Example',
  description='Example pipeline that does classification with model analysis based on a public BigQuery dataset.'
)
def taxi_cab_classification(
#    output='minio://minio-service:9000/blah/',
#    output='gs://pipelineai-kubeflow/blah',
    output='/mnt',
    project='taxi-cab-classification-pipeline',
#    column_names='gs://ml-pipeline-playground/tfx/taxi-cab-classification/column-names.json',

# 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.


# generate default secret name
import os
import kfp
from kfp import components
from kfp import dsl
import ai_pipeline_params as params

secret_name = 'kfp-creds'
configuration_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/commons/config/component.yaml')
train_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/watson/train/component.yaml')
store_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/watson/store/component.yaml')
deploy_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/watson/deploy/component.yaml')

# create pipelines

@dsl.pipeline(
    name='KFP on WML training',
    description='Kubeflow pipelines running on WML performing tensorflow image recognition.'
)
def kfp_wml_pipeline(
    GITHUB_TOKEN='',
    CONFIG_FILE_URL='https://raw.githubusercontent.com/user/repository/branch/creds.ini',
    train_code='tf-model.zip',
    execution_command='\'python3 convolutional_network.py --trainImagesFile ${DATA_DIR}/train-images-idx3-ubyte.gz --trainLabelsFile ${DATA_DIR}/train-labels-idx1-ubyte.gz --testImagesFile ${DATA_DIR}/t10k-images-idx3-ubyte.gz --testLabelsFile ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --learningRate 0.001 --trainingIters 20000\'',
    framework= 'tensorflow',

#
#      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.


import kfp
from kfp import components
from kfp import dsl
from kfp import gcp

dataflow_tf_transform_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/48dd338c8ab328084633c51704cda77db79ac8c2/components/dataflow/tft/component.yaml')
kubeflow_tf_training_op  = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/48dd338c8ab328084633c51704cda77db79ac8c2/components/kubeflow/dnntrainer/component.yaml')
dataflow_tf_predict_op   = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/48dd338c8ab328084633c51704cda77db79ac8c2/components/dataflow/predict/component.yaml')
confusion_matrix_op      = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/48dd338c8ab328084633c51704cda77db79ac8c2/components/local/confusion_matrix/component.yaml')

@dsl.pipeline(
    name='TF training and prediction pipeline',
    description=''
)
def kubeflow_training(output, project,
    evaluation='gs://ml-pipeline-playground/flower/eval100.csv',
    train='gs://ml-pipeline-playground/flower/train200.csv',
    schema='gs://ml-pipeline-playground/flower/schema.json',
    learning_rate=0.1,
    hidden_layer_size='100,50',
    steps=2000,
    target='label',

#
# 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.


import json
import kfp
from kfp import components
from kfp import dsl
import os
import subprocess

confusion_matrix_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e4d9e2b67cf39c5f12b9c1477cae11feb1a74dc7/components/local/confusion_matrix/component.yaml')

roc_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e4d9e2b67cf39c5f12b9c1477cae11feb1a74dc7/components/local/roc/component.yaml')

dataproc_create_cluster_op = components.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/e4d9e2b67cf39c5f12b9c1477cae11feb1a74dc7/components/gcp/dataproc/create_cluster/component.yaml')

dataproc_delete_cluster_op = components.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/e4d9e2b67cf39c5f12b9c1477cae11feb1a74dc7/components/gcp/dataproc/delete_cluster/component.yaml')

dataproc_submit_pyspark_op = components.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/e4d9e2b67cf39c5f12b9c1477cae11feb1a74dc7/components/gcp/dataproc/submit_pyspark_job/component.yaml'
)

dataproc_submit_spark_op = components.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/e4d9e2b67cf39c5f12b9c1477cae11feb1a74dc7/components/gcp/dataproc/submit_spark_job/component.yaml'
)