python/pipelines/automl_tabular_pl_v1.yaml [6827:6979]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        - --dataset_type
        - '{{$.inputs.parameters[''dataset_type'']}}'
        - --pipeline_job_id
        - '{{$.pipeline_job_uuid}}'
        - --pipeline_job_resource_name
        - '{{$.pipeline_job_resource_name}}'
        - --model_name
        - '{{$.inputs.artifacts[''model''].metadata[''resourceName'']}}'
        - --gcp_resources
        - '{{$.outputs.parameters[''gcp_resources''].output_file}}'
        command:
        - python3
        - -u
        - -m
        - google_cloud_pipeline_components.container.experimental.evaluation.import_model_evaluation
        image: gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.32
    exec-model-upload:
      container:
        args:
        - --type
        - UploadModel
        - --payload
        - '{"Concat": ["{", "\"display_name\": \"", "{{$.inputs.parameters[''display_name'']}}",
          "\"", ", \"description\": \"", "{{$.inputs.parameters[''description'']}}",
          "\"", ", \"explanation_spec\": {", "\"parameters\": ", "{{$.inputs.parameters[''explanation_parameters'']}}",
          ", \"metadata\": ", "{{$.inputs.parameters[''explanation_metadata'']}}",
          "}", ", \"explanation_metadata_artifact\": \"", "{{$.inputs.artifacts[''explanation_metadata_artifact''].uri}}",
          "\"", ", \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
          "\"}", ", \"labels\": ", "{{$.inputs.parameters[''labels'']}}", "}"]}'
        - --project
        - '{{$.inputs.parameters[''project'']}}'
        - --location
        - '{{$.inputs.parameters[''location'']}}'
        - --gcp_resources
        - '{{$.outputs.parameters[''gcp_resources''].output_file}}'
        - --executor_input
        - '{{$}}'
        command:
        - python3
        - -u
        - -m
        - launcher
        image: gcr.io/ml-pipeline/automl-tables-private:1.0.13
    exec-model-upload-2:
      container:
        args:
        - --type
        - UploadModel
        - --payload
        - '{"Concat": ["{", "\"display_name\": \"", "{{$.inputs.parameters[''display_name'']}}",
          "\"", ", \"description\": \"", "{{$.inputs.parameters[''description'']}}",
          "\"", ", \"explanation_spec\": {", "\"parameters\": ", "{{$.inputs.parameters[''explanation_parameters'']}}",
          ", \"metadata\": ", "{{$.inputs.parameters[''explanation_metadata'']}}",
          "}", ", \"explanation_metadata_artifact\": \"", "{{$.inputs.artifacts[''explanation_metadata_artifact''].uri}}",
          "\"", ", \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
          "\"}", ", \"labels\": ", "{{$.inputs.parameters[''labels'']}}", "}"]}'
        - --project
        - '{{$.inputs.parameters[''project'']}}'
        - --location
        - '{{$.inputs.parameters[''location'']}}'
        - --gcp_resources
        - '{{$.outputs.parameters[''gcp_resources''].output_file}}'
        - --executor_input
        - '{{$}}'
        command:
        - python3
        - -u
        - -m
        - launcher
        image: gcr.io/ml-pipeline/automl-tables-private:1.0.13
    exec-model-upload-3:
      container:
        args:
        - --type
        - UploadModel
        - --payload
        - '{"Concat": ["{", "\"display_name\": \"", "{{$.inputs.parameters[''display_name'']}}",
          "\"", ", \"description\": \"", "{{$.inputs.parameters[''description'']}}",
          "\"", ", \"explanation_spec\": {", "\"parameters\": ", "{{$.inputs.parameters[''explanation_parameters'']}}",
          ", \"metadata\": ", "{{$.inputs.parameters[''explanation_metadata'']}}",
          "}", ", \"explanation_metadata_artifact\": \"", "{{$.inputs.artifacts[''explanation_metadata_artifact''].uri}}",
          "\"", ", \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
          "\"}", ", \"labels\": ", "{{$.inputs.parameters[''labels'']}}", "}"]}'
        - --project
        - '{{$.inputs.parameters[''project'']}}'
        - --location
        - '{{$.inputs.parameters[''location'']}}'
        - --gcp_resources
        - '{{$.outputs.parameters[''gcp_resources''].output_file}}'
        - --executor_input
        - '{{$}}'
        command:
        - python3
        - -u
        - -m
        - launcher
        image: gcr.io/ml-pipeline/automl-tables-private:1.0.13
    exec-read-input-uri:
      container:
        args:
        - --executor_input
        - '{{$}}'
        - --function_to_execute
        - _read_input_uri
        command:
        - sh
        - -ec
        - 'program_path=$(mktemp -d)

          printf "%s" "$0" > "$program_path/ephemeral_component.py"

          python3 -m kfp.components.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"

          '
        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
          \ *\n\ndef _read_input_uri(split_uri: dsl.InputPath('Dataset')) -> list:\
          \  # Required by KFP; pylint:disable=g-bare-generic\n  \"\"\"Construct Dataset\
          \ based on the batch prediction job.\n\n  Args:\n    split_uri: Tbe path\
          \ to the file that contains Dataset data.\n\n  Returns:\n    The list of\
          \ string that represents the batch prediction input files.\n  \"\"\"\n \
          \ # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\
          \  import json\n  # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\
          \  with open(split_uri, 'r') as f:\n    data_source = json.loads(f.read())\n\
          \    return data_source['tf_record_data_source']['file_patterns']\n\n"
        image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:dev
    exec-read-input-uri-2:
      container:
        args:
        - --executor_input
        - '{{$}}'
        - --function_to_execute
        - _read_input_uri
        command:
        - sh
        - -ec
        - 'program_path=$(mktemp -d)

          printf "%s" "$0" > "$program_path/ephemeral_component.py"

          python3 -m kfp.components.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"

          '
        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
          \ *\n\ndef _read_input_uri(split_uri: dsl.InputPath('Dataset')) -> list:\
          \  # Required by KFP; pylint:disable=g-bare-generic\n  \"\"\"Construct Dataset\
          \ based on the batch prediction job.\n\n  Args:\n    split_uri: Tbe path\
          \ to the file that contains Dataset data.\n\n  Returns:\n    The list of\
          \ string that represents the batch prediction input files.\n  \"\"\"\n \
          \ # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\
          \  import json\n  # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\
          \  with open(split_uri, 'r') as f:\n    data_source = json.loads(f.read())\n\
          \    return data_source['tf_record_data_source']['file_patterns']\n\n"
        image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:dev
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python/pipelines/automl_tabular_pl_v2.yaml [7432:7584]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        - --dataset_type
        - '{{$.inputs.parameters[''dataset_type'']}}'
        - --pipeline_job_id
        - '{{$.pipeline_job_uuid}}'
        - --pipeline_job_resource_name
        - '{{$.pipeline_job_resource_name}}'
        - --model_name
        - '{{$.inputs.artifacts[''model''].metadata[''resourceName'']}}'
        - --gcp_resources
        - '{{$.outputs.parameters[''gcp_resources''].output_file}}'
        command:
        - python3
        - -u
        - -m
        - google_cloud_pipeline_components.container.experimental.evaluation.import_model_evaluation
        image: gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.32
    exec-model-upload:
      container:
        args:
        - --type
        - UploadModel
        - --payload
        - '{"Concat": ["{", "\"display_name\": \"", "{{$.inputs.parameters[''display_name'']}}",
          "\"", ", \"description\": \"", "{{$.inputs.parameters[''description'']}}",
          "\"", ", \"explanation_spec\": {", "\"parameters\": ", "{{$.inputs.parameters[''explanation_parameters'']}}",
          ", \"metadata\": ", "{{$.inputs.parameters[''explanation_metadata'']}}",
          "}", ", \"explanation_metadata_artifact\": \"", "{{$.inputs.artifacts[''explanation_metadata_artifact''].uri}}",
          "\"", ", \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
          "\"}", ", \"labels\": ", "{{$.inputs.parameters[''labels'']}}", "}"]}'
        - --project
        - '{{$.inputs.parameters[''project'']}}'
        - --location
        - '{{$.inputs.parameters[''location'']}}'
        - --gcp_resources
        - '{{$.outputs.parameters[''gcp_resources''].output_file}}'
        - --executor_input
        - '{{$}}'
        command:
        - python3
        - -u
        - -m
        - launcher
        image: gcr.io/ml-pipeline/automl-tables-private:1.0.13
    exec-model-upload-2:
      container:
        args:
        - --type
        - UploadModel
        - --payload
        - '{"Concat": ["{", "\"display_name\": \"", "{{$.inputs.parameters[''display_name'']}}",
          "\"", ", \"description\": \"", "{{$.inputs.parameters[''description'']}}",
          "\"", ", \"explanation_spec\": {", "\"parameters\": ", "{{$.inputs.parameters[''explanation_parameters'']}}",
          ", \"metadata\": ", "{{$.inputs.parameters[''explanation_metadata'']}}",
          "}", ", \"explanation_metadata_artifact\": \"", "{{$.inputs.artifacts[''explanation_metadata_artifact''].uri}}",
          "\"", ", \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
          "\"}", ", \"labels\": ", "{{$.inputs.parameters[''labels'']}}", "}"]}'
        - --project
        - '{{$.inputs.parameters[''project'']}}'
        - --location
        - '{{$.inputs.parameters[''location'']}}'
        - --gcp_resources
        - '{{$.outputs.parameters[''gcp_resources''].output_file}}'
        - --executor_input
        - '{{$}}'
        command:
        - python3
        - -u
        - -m
        - launcher
        image: gcr.io/ml-pipeline/automl-tables-private:1.0.13
    exec-model-upload-3:
      container:
        args:
        - --type
        - UploadModel
        - --payload
        - '{"Concat": ["{", "\"display_name\": \"", "{{$.inputs.parameters[''display_name'']}}",
          "\"", ", \"description\": \"", "{{$.inputs.parameters[''description'']}}",
          "\"", ", \"explanation_spec\": {", "\"parameters\": ", "{{$.inputs.parameters[''explanation_parameters'']}}",
          ", \"metadata\": ", "{{$.inputs.parameters[''explanation_metadata'']}}",
          "}", ", \"explanation_metadata_artifact\": \"", "{{$.inputs.artifacts[''explanation_metadata_artifact''].uri}}",
          "\"", ", \"encryption_spec\": {\"kms_key_name\":\"", "{{$.inputs.parameters[''encryption_spec_key_name'']}}",
          "\"}", ", \"labels\": ", "{{$.inputs.parameters[''labels'']}}", "}"]}'
        - --project
        - '{{$.inputs.parameters[''project'']}}'
        - --location
        - '{{$.inputs.parameters[''location'']}}'
        - --gcp_resources
        - '{{$.outputs.parameters[''gcp_resources''].output_file}}'
        - --executor_input
        - '{{$}}'
        command:
        - python3
        - -u
        - -m
        - launcher
        image: gcr.io/ml-pipeline/automl-tables-private:1.0.13
    exec-read-input-uri:
      container:
        args:
        - --executor_input
        - '{{$}}'
        - --function_to_execute
        - _read_input_uri
        command:
        - sh
        - -ec
        - 'program_path=$(mktemp -d)

          printf "%s" "$0" > "$program_path/ephemeral_component.py"

          python3 -m kfp.components.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"

          '
        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
          \ *\n\ndef _read_input_uri(split_uri: dsl.InputPath('Dataset')) -> list:\
          \  # Required by KFP; pylint:disable=g-bare-generic\n  \"\"\"Construct Dataset\
          \ based on the batch prediction job.\n\n  Args:\n    split_uri: Tbe path\
          \ to the file that contains Dataset data.\n\n  Returns:\n    The list of\
          \ string that represents the batch prediction input files.\n  \"\"\"\n \
          \ # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\
          \  import json\n  # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\
          \  with open(split_uri, 'r') as f:\n    data_source = json.loads(f.read())\n\
          \    return data_source['tf_record_data_source']['file_patterns']\n\n"
        image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:dev
    exec-read-input-uri-2:
      container:
        args:
        - --executor_input
        - '{{$}}'
        - --function_to_execute
        - _read_input_uri
        command:
        - sh
        - -ec
        - 'program_path=$(mktemp -d)

          printf "%s" "$0" > "$program_path/ephemeral_component.py"

          python3 -m kfp.components.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"

          '
        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
          \ *\n\ndef _read_input_uri(split_uri: dsl.InputPath('Dataset')) -> list:\
          \  # Required by KFP; pylint:disable=g-bare-generic\n  \"\"\"Construct Dataset\
          \ based on the batch prediction job.\n\n  Args:\n    split_uri: Tbe path\
          \ to the file that contains Dataset data.\n\n  Returns:\n    The list of\
          \ string that represents the batch prediction input files.\n  \"\"\"\n \
          \ # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\
          \  import json\n  # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported\n\
          \  with open(split_uri, 'r') as f:\n    data_source = json.loads(f.read())\n\
          \    return data_source['tf_record_data_source']['file_patterns']\n\n"
        image: us-docker.pkg.dev/vertex-ai/automl-tabular/kfp-v2-base:dev
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