c09d7b252a74c0fb4ca6d21516867cb7942b7cf5,ml/kubeflow-pipelines/samples/kubeflow-tf/workflow1.py,,workflow1,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,22

Before Change

  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,
          "--ts1", ts1,
          "--ts2", ts2,
          "--stage", "eval",
          "--preprocessing-module", preprocessing_module1]
      // file_outputs = {"transformed": "/output.txt"}
      )
  tfttrain = dsl.ContainerOp(
      name = "tft-train",
      image = "gcr.io/google-samples/ml-pipeline-dataflow-tftbq-taxi",
      arguments = [ "--input_handle", input_handle_train, "--outfile_prefix", outfile_prefix_train,

After Change

  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,
          "--ts1", ts1,
          "--ts2", ts2,
          "--stage", "eval",
          "--preprocessing-module", preprocessing_module1]
      // file_outputs = {"transformed": "/output.txt"}
      ).apply(gcp.use_gcp_secret("user-gcp-sa"))
  tfttrain = dsl.ContainerOp(
      name = "tft-train",
      image = "gcr.io/google-samples/ml-pipeline-dataflow-tftbq-taxi",
      arguments = [ "--input_handle", input_handle_train, "--outfile_prefix", outfile_prefix_train,

Instances