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  2. kfp
  3. functions
  4. kfp.components.load_component_from_url
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How to use the kfp.components.load_component_from_url function in kfp

To help you get started, we’ve selected a few kfp examples, based on popular ways it is used in public projects.

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github kubeflow / pipelines / samples / core / tfx_cab_classification / tfx_cab_classification.py View on Github external
# 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
from kfp import onprem

platform = 'GCP'

dataflow_tf_data_validation_op  = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/dataflow/tfdv/component.yaml')
dataflow_tf_transform_op        = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/dataflow/tft/component.yaml')
tf_train_op                     = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/kubeflow/dnntrainer/component.yaml')
dataflow_tf_model_analyze_op    = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/b705631e892bd8181cabcd704e6e6385b16daf90/components/dataflow/tfma/component.yaml')
dataflow_tf_predict_op          = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/b705631e892bd8181cabcd704e6e6385b16daf90/components/dataflow/predict/component.yaml')

confusion_matrix_op             = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/local/confusion_matrix/component.yaml')
roc_op                          = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/local/roc/component.yaml')

kubeflow_deploy_op              = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/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,
    project,
    column_names='gs://ml-pipeline-playground/tfx/taxi-cab-classification/column-names.json',
    key_columns='trip_start_timestamp',
github PipelineAI / pipeline / kubeflow / kubeflow-pipelines / taxi / taxi-cab-classification-pipeline.py View on Github external
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',
    column_names='/mnt/kubeflow-pipelines/taxi/column-names.json',
    key_columns='trip_start_timestamp',
#    train='gs://ml-pipeline-playground/tfx/taxi-cab-classification/train.csv',
    train='/mnt/kubeflow-pipelines/taxi/train.csv',
#    evaluation='gs://ml-pipeline-playground/tfx/taxi-cab-classification/eval.csv',
github PipelineAI / pipeline / kubeflow / kubeflow-pipelines / taxi / taxi-cab-classification-pipeline.py View on Github external
# 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
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',
github kubeflow / pipelines / samples / contrib / ibm-samples / ffdl-seldon / ffdl_pipeline.py View on Github external
import kfp
from kfp import components
from kfp import dsl
import ai_pipeline_params as params

# generate default secret name
secret_name = 'kfp-creds'

configuration_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/eb830cd73ca148e5a1a6485a9374c2dc068314bc/components/ibm-components/commons/config/component.yaml')
train_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/eb830cd73ca148e5a1a6485a9374c2dc068314bc/components/ibm-components/ffdl/train/component.yaml')
serve_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/eb830cd73ca148e5a1a6485a9374c2dc068314bc/components/ibm-components/ffdl/serve/component.yaml')
    
# create pipeline
@dsl.pipeline(
  name='FfDL pipeline',
  description='A pipeline for machine learning workflow using Fabric for Deep Learning and Seldon.'
)

def ffdlPipeline(
    GITHUB_TOKEN='',
    CONFIG_FILE_URL='https://raw.githubusercontent.com/user/repository/branch/creds.ini',
    model_def_file_path='gender-classification.zip',
    manifest_file_path='manifest.yml',
    model_deployment_name='gender-classifier',
    model_class_name='ThreeLayerCNN',
    model_class_file='gender_classification.py'
):
github kubeflow / pipelines / samples / core / xgboost_training_cm / xgboost_training_cm.py View on Github external
# 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'
)

_PYSRC_PREFIX = 'gs://ml-pipeline-playground/dataproc-example' # Common path to python src.

_XGBOOST_PKG = 'gs://ml-pipeline-playground/xgboost4j-example-0.8-SNAPSHOT-jar-with-dependencies.jar'
github kubeflow / pipelines / samples / core / tfx_cab_classification / tfx_cab_classification.py View on Github external
# 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
from kfp import onprem

platform = 'GCP'

dataflow_tf_data_validation_op  = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/dataflow/tfdv/component.yaml')
dataflow_tf_transform_op        = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/dataflow/tft/component.yaml')
tf_train_op                     = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/kubeflow/dnntrainer/component.yaml')
dataflow_tf_model_analyze_op    = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/b705631e892bd8181cabcd704e6e6385b16daf90/components/dataflow/tfma/component.yaml')
dataflow_tf_predict_op          = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/b705631e892bd8181cabcd704e6e6385b16daf90/components/dataflow/predict/component.yaml')

confusion_matrix_op             = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/local/confusion_matrix/component.yaml')
roc_op                          = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/local/roc/component.yaml')

kubeflow_deploy_op              = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/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,
    project,
    column_names='gs://ml-pipeline-playground/tfx/taxi-cab-classification/column-names.json',
github kubeflow / pipelines / samples / contrib / ibm-samples / openscale / openscale.py View on Github external
import kfp.dsl as dsl
import kfp.components as components
import ai_pipeline_params as params

secret_name = 'aios-creds'

preprocess_spark_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/spark/data_preprocess_spark/component.yaml')
train_spark_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/spark/train_spark/component.yaml')
store_spark_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/spark/store_spark_model/component.yaml')
deploy_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/watson/deploy/component.yaml')
subscribe_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/watson/manage/subscribe/component.yaml')
fairness_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/watson/manage/monitor_fairness/component.yaml')
quality_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/ibm-components/watson/manage/monitor_quality/component.yaml')


@dsl.pipeline(
  name='Watson OpenScale Pipeline',
  description='A pipeline for end to end Spark machine learning workflow and model monitoring.'
)
def aiosPipeline(
    BUCKET_NAME='',
    TRAINING_DATA_LINK='https://raw.githubusercontent.com/emartensibm/german-credit/master/german_credit_data_biased_training.csv',
    POSTGRES_SCHEMA_NAME='data_mart_credit',
    LABEL_NAME='Risk',
    PROBLEM_TYPE='BINARY_CLASSIFICATION',
    THRESHOLD='0.7',
    AIOS_MANIFEST_PATH='aios.json',
github kubeflow / pipelines / samples / core / tfx_cab_classification / tfx_cab_classification.py View on Github external
# 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
from kfp import onprem

platform = 'GCP'

dataflow_tf_data_validation_op  = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/dataflow/tfdv/component.yaml')
dataflow_tf_transform_op        = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/a97f1d0ad0e7b92203f35c5b0b9af3a314952e05/components/dataflow/tft/component.yaml')
tf_train_op                     = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/kubeflow/dnntrainer/component.yaml')
dataflow_tf_model_analyze_op    = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/b705631e892bd8181cabcd704e6e6385b16daf90/components/dataflow/tfma/component.yaml')
dataflow_tf_predict_op          = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/b705631e892bd8181cabcd704e6e6385b16daf90/components/dataflow/predict/component.yaml')

confusion_matrix_op             = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/local/confusion_matrix/component.yaml')
roc_op                          = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/components/local/roc/component.yaml')

kubeflow_deploy_op              = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e7a021ed1da6b0ff21f7ba30422decbdcdda0c20/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,
    project,
github kubeflow / pipelines / samples / core / xgboost_training_cm / xgboost_training_cm.py View on Github external
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'
)

_PYSRC_PREFIX = 'gs://ml-pipeline-playground/dataproc-example' # Common path to python src.

_XGBOOST_PKG = 'gs://ml-pipeline-playground/xgboost4j-example-0.8-SNAPSHOT-jar-with-dependencies.jar'

_TRAINER_MAIN_CLS = 'ml.dmlc.xgboost4j.scala.example.spark.XGBoostTrainer'

_PREDICTOR_MAIN_CLS = 'ml.dmlc.xgboost4j.scala.example.spark.XGBoostPredictor'

kfp

Kubeflow Pipelines SDK

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