1. Advisor
  2. kfp
  3. functions
  4. kfp.dsl.PipelineParam
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How to use the kfp.dsl.PipelineParam 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 amygdala / code-snippets / ml / kubeflow-pipelines / samples / kubeflow-tf / workflow1.py View on Github external
def workflow1(
  input_handle_eval: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-eval', value='gs://aju-dev-demos-codelabs/KF/taxidata/eval/data.csv'),
  input_handle_train: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-train', value='gs://aju-dev-demos-codelabs/KF/taxidata/train/data.csv'),
  outfile_prefix_eval: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-eval', value='eval_transformed'),
  outfile_prefix_train: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-train', value='train_transformed'),
  train_steps: dsl.PipelineParam=dsl.PipelineParam(name='train-steps', value=10000),
  project: dsl.PipelineParam=dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
  working_dir: dsl.PipelineParam=dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
  tft_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tft-setup-file', value='/ml/transform/setup.py'),
  tfma_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tfma-setup-file', value='/ml/analysis/setup.py'),
  workers: dsl.PipelineParam=dsl.PipelineParam(name='workers', value=1),
  pss: dsl.PipelineParam=dsl.PipelineParam(name='pss', value=1),
  max_rows: dsl.PipelineParam=dsl.PipelineParam(name='max-rows', value=10000),
  ts1: dsl.PipelineParam=dsl.PipelineParam(name='ts1', value=''),
  ts2: dsl.PipelineParam=dsl.PipelineParam(name='ts2', value=''),
  preprocessing_module1: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module1', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing.py'),
  preprocessing_module2: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module2', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing2.py'),
  preprocess_mode: dsl.PipelineParam=dsl.PipelineParam(name='preprocess-mode', value='local'),
  tfma_mode: dsl.PipelineParam=dsl.PipelineParam(name='tfma-mode', value='local')):

  tfteval = dsl.ContainerOp(
      name = 'tft-eval',
      image = 'gcr.io/google-samples/ml-pipeline-dataflow-tftbq-taxi',
      arguments = [ "--input_handle", input_handle_eval, "--outfile_prefix", outfile_prefix_eval,
          "--working_dir", '%s/%s/tft-eval' % (working_dir, '{{workflow.name}}'),
          "--project", project,
github amygdala / code-snippets / ml / kubeflow-pipelines / samples / kubeflow-tf / gh_summ.py View on Github external
def gh_summ(  #pylint: disable=unused-argument
  train_steps: dsl.PipelineParam = dsl.PipelineParam(name='train-steps', value=2019300),
  project: dsl.PipelineParam = dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
  github_token: dsl.PipelineParam = dsl.PipelineParam(
      name='github-token', value='YOUR_GITHUB_TOKEN_HERE'),
  working_dir: dsl.PipelineParam = dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
  checkpoint_dir: dsl.PipelineParam = dsl.PipelineParam(
      name='checkpoint-dir',
      value='gs://aju-dev-demos-codelabs/kubecon/model_output_tbase.bak2019000'),
  deploy_webapp: dsl.PipelineParam = dsl.PipelineParam(name='deploy-webapp', value='true'),
  data_dir: dsl.PipelineParam = dsl.PipelineParam(
      name='data-dir', value='gs://aju-dev-demos-codelabs/kubecon/t2t_data_gh_all/')):


  train = dsl.ContainerOp(
      name='train',
      image='gcr.io/google-samples/ml-pipeline-t2ttrain',
      arguments=["--data-dir", data_dir,
          "--checkpoint-dir", checkpoint_dir,
github amygdala / code-snippets / ml / kubeflow-pipelines / samples / kubeflow-tf / workflow1.py View on Github external
def workflow1(
  input_handle_eval: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-eval', value='gs://aju-dev-demos-codelabs/KF/taxidata/eval/data.csv'),
  input_handle_train: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-train', value='gs://aju-dev-demos-codelabs/KF/taxidata/train/data.csv'),
  outfile_prefix_eval: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-eval', value='eval_transformed'),
  outfile_prefix_train: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-train', value='train_transformed'),
  train_steps: dsl.PipelineParam=dsl.PipelineParam(name='train-steps', value=10000),
  project: dsl.PipelineParam=dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
  working_dir: dsl.PipelineParam=dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
  tft_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tft-setup-file', value='/ml/transform/setup.py'),
  tfma_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tfma-setup-file', value='/ml/analysis/setup.py'),
  workers: dsl.PipelineParam=dsl.PipelineParam(name='workers', value=1),
  pss: dsl.PipelineParam=dsl.PipelineParam(name='pss', value=1),
  max_rows: dsl.PipelineParam=dsl.PipelineParam(name='max-rows', value=10000),
  ts1: dsl.PipelineParam=dsl.PipelineParam(name='ts1', value=''),
  ts2: dsl.PipelineParam=dsl.PipelineParam(name='ts2', value=''),
  preprocessing_module1: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module1', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing.py'),
  preprocessing_module2: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module2', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing2.py'),
  preprocess_mode: dsl.PipelineParam=dsl.PipelineParam(name='preprocess-mode', value='local'),
  tfma_mode: dsl.PipelineParam=dsl.PipelineParam(name='tfma-mode', value='local')):

  tfteval = dsl.ContainerOp(
      name = 'tft-eval',
      image = 'gcr.io/google-samples/ml-pipeline-dataflow-tftbq-taxi',
      arguments = [ "--input_handle", input_handle_eval, "--outfile_prefix", outfile_prefix_eval,
github amygdala / code-snippets / ml / kubeflow-pipelines / samples / kubeflow-tf / workflow2.py View on Github external
input_handle_eval: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-eval', value='bigquery-public-data.chicago_taxi_trips.taxi_trips'),
  input_handle_train: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-train', value='bigquery-public-data.chicago_taxi_trips.taxi_trips'),
  outfile_prefix_eval: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-eval', value='eval_transformed'),
  outfile_prefix_train: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-train', value='train_transformed'),
  train_steps: dsl.PipelineParam=dsl.PipelineParam(name='train-steps', value=10000),
  project: dsl.PipelineParam=dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
  working_dir: dsl.PipelineParam=dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
  tft_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tft-setup-file', value='/ml/transform/setup.py'),
  tfma_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tfma-setup-file', value='/ml/analysis/setup.py'),
  workers: dsl.PipelineParam=dsl.PipelineParam(name='workers', value=2),
  pss: dsl.PipelineParam=dsl.PipelineParam(name='pss', value=1),
  max_rows: dsl.PipelineParam=dsl.PipelineParam(name='max-rows', value=10000),
  ts1_1: dsl.PipelineParam=dsl.PipelineParam(name='ts1-1', value='2016-02-01 00:00:00'),
  ts2_1: dsl.PipelineParam=dsl.PipelineParam(name='ts2-1', value='2016-03-01 00:00:00'),
  ts1_2: dsl.PipelineParam=dsl.PipelineParam(name='ts1-2', value='2013-01-01 00:00:00'),
  ts2_2: dsl.PipelineParam=dsl.PipelineParam(name='ts2-2', value='2016-03-01 00:00:00'),
  preprocessing_module: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module1', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing.py'),
  preprocess_mode: dsl.PipelineParam=dsl.PipelineParam(name='preprocess-mode', value='local'),
  tfma_mode: dsl.PipelineParam=dsl.PipelineParam(name='tfma-mode', value='local')):


  tfteval = dsl.ContainerOp(
      name = 'tft-eval',
      image = 'gcr.io/google-samples/ml-pipeline-dataflow-tftbq-taxi',
      arguments = [ "--input_handle", input_handle_eval, "--outfile_prefix", outfile_prefix_eval,
          "--working_dir", '%s/%s/tft-eval' % (working_dir, '{{workflow.name}}'),
          "--project", project,
          "--mode", preprocess_mode,
          "--setup_file", tft_setup_file,
          "--max_rows", max_rows,
          "--ts1", ts1_1,
          "--ts2", ts2_1,
github amygdala / code-snippets / ml / kubeflow-pipelines / samples / kubeflow-tf / workflow1.py View on Github external
def workflow1(
  input_handle_eval: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-eval', value='gs://aju-dev-demos-codelabs/KF/taxidata/eval/data.csv'),
  input_handle_train: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-train', value='gs://aju-dev-demos-codelabs/KF/taxidata/train/data.csv'),
  outfile_prefix_eval: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-eval', value='eval_transformed'),
  outfile_prefix_train: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-train', value='train_transformed'),
  train_steps: dsl.PipelineParam=dsl.PipelineParam(name='train-steps', value=10000),
  project: dsl.PipelineParam=dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
  working_dir: dsl.PipelineParam=dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
  tft_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tft-setup-file', value='/ml/transform/setup.py'),
  tfma_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tfma-setup-file', value='/ml/analysis/setup.py'),
  workers: dsl.PipelineParam=dsl.PipelineParam(name='workers', value=1),
  pss: dsl.PipelineParam=dsl.PipelineParam(name='pss', value=1),
  max_rows: dsl.PipelineParam=dsl.PipelineParam(name='max-rows', value=10000),
  ts1: dsl.PipelineParam=dsl.PipelineParam(name='ts1', value=''),
  ts2: dsl.PipelineParam=dsl.PipelineParam(name='ts2', value=''),
  preprocessing_module1: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module1', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing.py'),
  preprocessing_module2: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module2', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing2.py'),
  preprocess_mode: dsl.PipelineParam=dsl.PipelineParam(name='preprocess-mode', value='local'),
  tfma_mode: dsl.PipelineParam=dsl.PipelineParam(name='tfma-mode', value='local')):

  tfteval = dsl.ContainerOp(
      name = 'tft-eval',
      image = 'gcr.io/google-samples/ml-pipeline-dataflow-tftbq-taxi',
      arguments = [ "--input_handle", input_handle_eval, "--outfile_prefix", outfile_prefix_eval,
          "--working_dir", '%s/%s/tft-eval' % (working_dir, '{{workflow.name}}'),
          "--project", project,
          "--mode", preprocess_mode,
          "--setup_file", tft_setup_file,
          "--max-rows", '5000',
github amygdala / code-snippets / ml / kubeflow-pipelines / samples / kubeflow-tf / workflow2.py View on Github external
def workflow2(
  input_handle_eval: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-eval', value='bigquery-public-data.chicago_taxi_trips.taxi_trips'),
  input_handle_train: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-train', value='bigquery-public-data.chicago_taxi_trips.taxi_trips'),
  outfile_prefix_eval: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-eval', value='eval_transformed'),
  outfile_prefix_train: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-train', value='train_transformed'),
  train_steps: dsl.PipelineParam=dsl.PipelineParam(name='train-steps', value=10000),
  project: dsl.PipelineParam=dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
  working_dir: dsl.PipelineParam=dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
  tft_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tft-setup-file', value='/ml/transform/setup.py'),
  tfma_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tfma-setup-file', value='/ml/analysis/setup.py'),
  workers: dsl.PipelineParam=dsl.PipelineParam(name='workers', value=2),
  pss: dsl.PipelineParam=dsl.PipelineParam(name='pss', value=1),
  max_rows: dsl.PipelineParam=dsl.PipelineParam(name='max-rows', value=10000),
  ts1_1: dsl.PipelineParam=dsl.PipelineParam(name='ts1-1', value='2016-02-01 00:00:00'),
  ts2_1: dsl.PipelineParam=dsl.PipelineParam(name='ts2-1', value='2016-03-01 00:00:00'),
  ts1_2: dsl.PipelineParam=dsl.PipelineParam(name='ts1-2', value='2013-01-01 00:00:00'),
  ts2_2: dsl.PipelineParam=dsl.PipelineParam(name='ts2-2', value='2016-03-01 00:00:00'),
  preprocessing_module: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module1', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing.py'),
  preprocess_mode: dsl.PipelineParam=dsl.PipelineParam(name='preprocess-mode', value='local'),
  tfma_mode: dsl.PipelineParam=dsl.PipelineParam(name='tfma-mode', value='local')):


  tfteval = dsl.ContainerOp(
      name = 'tft-eval',
      image = 'gcr.io/google-samples/ml-pipeline-dataflow-tftbq-taxi',
      arguments = [ "--input_handle", input_handle_eval, "--outfile_prefix", outfile_prefix_eval,
github amygdala / code-snippets / ml / kubeflow-pipelines / samples / kubeflow-tf / workflow2.py View on Github external
def workflow2(
  input_handle_eval: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-eval', value='bigquery-public-data.chicago_taxi_trips.taxi_trips'),
  input_handle_train: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-train', value='bigquery-public-data.chicago_taxi_trips.taxi_trips'),
  outfile_prefix_eval: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-eval', value='eval_transformed'),
  outfile_prefix_train: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-train', value='train_transformed'),
  train_steps: dsl.PipelineParam=dsl.PipelineParam(name='train-steps', value=10000),
  project: dsl.PipelineParam=dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
  working_dir: dsl.PipelineParam=dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
  tft_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tft-setup-file', value='/ml/transform/setup.py'),
  tfma_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tfma-setup-file', value='/ml/analysis/setup.py'),
  workers: dsl.PipelineParam=dsl.PipelineParam(name='workers', value=2),
  pss: dsl.PipelineParam=dsl.PipelineParam(name='pss', value=1),
  max_rows: dsl.PipelineParam=dsl.PipelineParam(name='max-rows', value=10000),
  ts1_1: dsl.PipelineParam=dsl.PipelineParam(name='ts1-1', value='2016-02-01 00:00:00'),
  ts2_1: dsl.PipelineParam=dsl.PipelineParam(name='ts2-1', value='2016-03-01 00:00:00'),
  ts1_2: dsl.PipelineParam=dsl.PipelineParam(name='ts1-2', value='2013-01-01 00:00:00'),
  ts2_2: dsl.PipelineParam=dsl.PipelineParam(name='ts2-2', value='2016-03-01 00:00:00'),
  preprocessing_module: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module1', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing.py'),
github kubeflow / pipelines / samples / core / parameterized_tfx_oss / parameterized_tfx_oss.py View on Github external
specs=[
              evaluator_pb2.SingleSlicingSpec(
                  column_for_slicing=['trip_start_hour']
              )
          ]
      ),
  )
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model']
  )

  # Hack: ensuring push_destination can be correctly parameterized and interpreted.
  # pipeline root will be specified as a dsl.PipelineParam with the name
  # pipeline-root, see:
  # https://github.com/tensorflow/tfx/blob/1c670e92143c7856f67a866f721b8a9368ede385/tfx/orchestration/kubeflow/kubeflow_dag_runner.py#L226
  _pipeline_root_param = dsl.PipelineParam(name='pipeline-root')
  pusher = Pusher(
      model_export=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=os.path.
              join(str(_pipeline_root_param), 'model_serving')
          )
      ),
  )

  return pipeline.Pipeline(
      pipeline_name='parameterized_tfx_oss',
      pipeline_root=pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema, validate_stats, transform,
github amygdala / code-snippets / ml / kubeflow-pipelines / samples / kubeflow-tf / workflow2.py View on Github external
def workflow2(
  input_handle_eval: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-eval', value='bigquery-public-data.chicago_taxi_trips.taxi_trips'),
  input_handle_train: dsl.PipelineParam=dsl.PipelineParam(name='input-handle-train', value='bigquery-public-data.chicago_taxi_trips.taxi_trips'),
  outfile_prefix_eval: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-eval', value='eval_transformed'),
  outfile_prefix_train: dsl.PipelineParam=dsl.PipelineParam(name='outfile-prefix-train', value='train_transformed'),
  train_steps: dsl.PipelineParam=dsl.PipelineParam(name='train-steps', value=10000),
  project: dsl.PipelineParam=dsl.PipelineParam(name='project', value='YOUR_PROJECT_HERE'),
  working_dir: dsl.PipelineParam=dsl.PipelineParam(name='working-dir', value='YOUR_GCS_DIR_HERE'),
  tft_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tft-setup-file', value='/ml/transform/setup.py'),
  tfma_setup_file: dsl.PipelineParam=dsl.PipelineParam(name='tfma-setup-file', value='/ml/analysis/setup.py'),
  workers: dsl.PipelineParam=dsl.PipelineParam(name='workers', value=2),
  pss: dsl.PipelineParam=dsl.PipelineParam(name='pss', value=1),
  max_rows: dsl.PipelineParam=dsl.PipelineParam(name='max-rows', value=10000),
  ts1_1: dsl.PipelineParam=dsl.PipelineParam(name='ts1-1', value='2016-02-01 00:00:00'),
  ts2_1: dsl.PipelineParam=dsl.PipelineParam(name='ts2-1', value='2016-03-01 00:00:00'),
  ts1_2: dsl.PipelineParam=dsl.PipelineParam(name='ts1-2', value='2013-01-01 00:00:00'),
  ts2_2: dsl.PipelineParam=dsl.PipelineParam(name='ts2-2', value='2016-03-01 00:00:00'),
  preprocessing_module: dsl.PipelineParam=dsl.PipelineParam(name='preprocessing-module1', value='gs://aju-dev-demos-codelabs/KF/taxi-preproc/preprocessing.py'),
  preprocess_mode: dsl.PipelineParam=dsl.PipelineParam(name='preprocess-mode', value='local'),
  tfma_mode: dsl.PipelineParam=dsl.PipelineParam(name='tfma-mode', value='local')):


  tfteval = dsl.ContainerOp(
      name = 'tft-eval',
      image = 'gcr.io/google-samples/ml-pipeline-dataflow-tftbq-taxi',
      arguments = [ "--input_handle", input_handle_eval, "--outfile_prefix", outfile_prefix_eval,
          "--working_dir", '%s/%s/tft-eval' % (working_dir, '{{workflow.name}}'),
          "--project", project,
          "--mode", preprocess_mode,
github kubeflow / pipelines / samples / core / parameterized_tfx_oss / parameterized_tfx_oss.py View on Github external
from tfx.components.model_validator.component import ModelValidator
from tfx.components.pusher.component import Pusher
from tfx.components.schema_gen.component import SchemaGen
from tfx.components.statistics_gen.component import StatisticsGen
from tfx.components.trainer.component import Trainer
from tfx.components.transform.component import Transform
from tfx.orchestration import pipeline
from tfx.orchestration.kubeflow import kubeflow_dag_runner
from tfx.proto import evaluator_pb2
from tfx.utils.dsl_utils import external_input
from tfx.proto import pusher_pb2
from tfx.proto import trainer_pb2

# Define pipeline params used for pipeline execution.
# Path to the module file, should be a GCS path.
_taxi_module_file_param = dsl.PipelineParam(
    name='module-file',
    value='gs://ml-pipeline-playground/tfx_taxi_simple/modules/taxi_utils.py'
)

# Path to the CSV data file, under which their should be a data.csv file.
_data_root_param = dsl.PipelineParam(
    name='data-root', value='gs://ml-pipeline-playground/tfx_taxi_simple/data'
)

# Path of pipeline root, should be a GCS path.
pipeline_root = os.path.join(
    'gs://your-bucket', 'tfx_taxi_simple', kfp.dsl.RUN_ID_PLACEHOLDER
)


def _create_test_pipeline(

kfp

Kubeflow Pipelines SDK

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Apache-2.0
Latest version published 12 days ago

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