Using the TFX Command-line Interface

The TFX command-line interface (CLI) performs a full range of pipeline actions using pipeline orchestrators, such as Apache Airflow, Apache Beam, and Kubeflow Pipelines. For example, you can use the CLI to:

  • Create, update, and delete pipelines.
  • Run a pipeline and monitor the run on various orchestrators.
  • List pipelines and pipeline runs.

About the TFX CLI

The TFX CLI is installed as a part of the TFX package. All CLI commands follow the structure below:

tfx command-group command flags

The following command-group options are currently supported:

  • tfx pipeline - Create and manage TFX pipelines.
  • tfx run - Create and manage runs of TFX pipelines on various orchestration platforms.
  • tfx template - Experimental commands for listing and copying TFX pipeline templates.

Each command group provides a set of commands. Follow the instructions in the pipeline commands, run commands, and template commands sections to learn more about using these commands.

Flags let you pass arguments into CLI commands. Words in flags are separated with either a hyphen (-) or an underscore (_). For example, the pipeline name flag can be specified as either --pipeline-name or --pipeline_name. This document specifies flags with underscores for brevity. Learn more about flags used in the TFX CLI.

tfx pipeline

The structure for commands in the tfx pipeline command group is as follows:

tfx pipeline command required-flags [optional-flags]

Use the following sections to learn more about the commands in the tfx pipeline command group.

create

Creates a new pipeline in the given orchestrator.

Usage:

tfx pipeline create --pipeline_path=pipeline-path [--endpoint=endpoint --engine=engine \
--iap_client_id=iap-client-id --namespace=namespace --package_path=package-path \
--build_target_image=build-target-image --build_base_image=build-base-image \
--skaffold_cmd=skaffold-command]
--pipeline_path=pipeline-path
The path to the pipeline configuration file.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.
--package_path=package-path

(Optional.) Path to the compiled pipeline as a file. The compiled pipeline should be a compressed file (.tar.gz, .tgz, or .zip) or a YAML file (.yaml or .yml).

If package-path is not specified, TFX uses the following as the default path: current_directory/pipeline_name.tar.gz

--build_target_image=build-target-image

(Optional.) When the engine is kubeflow, TFX creates a container image for your pipeline. The build target image specifies the name, container image repository, and tag to use when creating the pipeline container image. If you do not specify a tag, the container image is tagged as latest.

For your Kubeflow Pipelines cluster to run your pipeline, the cluster must be able to access the specified container image repository.

--build_base_image=build-base-image

(Optional.) When the engine is kubeflow, TFX creates a container image for your pipeline. The build base image specifies the base container image to use when building the pipeline container image.

--skaffold_cmd=skaffold-cmd

(Optional.) The path to Skaffold on your computer.

Examples:

Apache Airflow:

tfx pipeline create --engine=airflow --pipeline_path=pipeline-path

Apache Beam:

tfx pipeline create --engine=beam --pipeline_path=pipeline-path

Kubeflow:

tfx pipeline create --engine=kubeflow --pipeline_path=pipeline-path --package_path=package-path \
--iap_client_id=iap-client-id --namespace=namespace --endpoint=endpoint \
--skaffold_cmd=skaffold-cmd

To autodetect engine from user environment, simply avoid using the engine flag like the example below. For more details, check the flags section.

tfx pipeline create --pipeline_path=pipeline-path --endpoint --iap_client_id --namespace \
--package_path --skaffold_cmd

update

Updates an existing pipeline in the given orchestrator.

Usage:

tfx pipeline update --pipeline_path=pipeline-path [--endpoint=endpoint --engine=engine \
--iap_client_id=iap-client-id --namespace=namespace --package_path=package-path \
--skaffold_cmd=skaffold-command]
--pipeline_path=pipeline-path
The path to the pipeline configuration file.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.
--package_path=package-path

(Optional.) Path to the compiled pipeline as a file. The compiled pipeline should be a compressed file (.tar.gz, .tgz, or .zip) or a YAML file (.yaml or .yml).

If package-path is not specified, TFX uses the following as the default path: current_directory/pipeline_name.tar.gz

--skaffold_cmd=skaffold-cmd

(Optional.) The path to Skaffold on your computer.

Examples:

Apache Airflow:

tfx pipeline update --engine=airflow --pipeline_path=pipeline-path

Apache Beam:

tfx pipeline update --engine=beam --pipeline_path=pipeline-path

Kubeflow:

tfx pipeline update --engine=kubeflow --pipeline_path=pipeline-path --package_path=package-path \
--iap_client_id=iap-client-id --namespace=namespace --endpoint=endpoint \
--skaffold_cmd=skaffold-cmd

compile

Compiles the pipeline config file to create a workflow file in Kubeflow and performs the following checks while compiling:

  1. Checks if the pipeline path is valid.
  2. Checks if the pipeline details are extracted successfully from the pipeline config file.
  3. Checks if the DagRunner in the pipeline config matches the engine.
  4. Checks if the workflow file is created successfully in the package path provided (only for Kubeflow).

Recommended to use before creating or updating a pipeline.

Usage:

tfx pipeline compile --pipeline_path=pipeline-path [--endpoint=endpoint --engine=engine \
--iap_client_id=iap-client-id --namespace=namespace --package_path=package-path]
--pipeline_path=pipeline-path
The path to the pipeline configuration file.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.
--package_path=package-path

(Optional.) Path to the compiled pipeline as a file. The compiled pipeline should be a compressed file (.tar.gz, .tgz, or .zip) or a YAML file (.yaml or .yml).

If package-path is not specified, TFX uses the following as the default path: current_directory/pipeline_name.tar.gz

Examples:

Apache Airflow:

tfx pipeline compile --engine=airflow --pipeline_path=pipeline-path

Apache Beam:

tfx pipeline compile --engine=beam --pipeline_path=pipeline-path

Kubeflow:

tfx pipeline compile --engine=kubeflow --pipeline_path=pipeline-path --package_path=package-path \
--iap_client_id=iap-client-id --namespace=namespace --endpoint=endpoint

delete

Deletes a pipeline from the given orchestrator.

Usage:

tfx pipeline delete --pipeline_path=pipeline-path [--endpoint=endpoint --engine=engine \
--iap_client_id=iap-client-id --namespace=namespace]
--pipeline_path=pipeline-path
The path to the pipeline configuration file.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples:

Apache Airflow:

tfx pipeline delete --engine=airflow --pipeline_name=pipeline-name

Apache Beam:

tfx pipeline delete --engine=beam --pipeline_name=pipeline-name

Kubeflow:

tfx pipeline delete --engine=kubeflow --pipeline_name=pipeline-name \
--iap_client_id=iap-client-id --namespace=namespace --endpoint=endpoint

list

Lists all the pipelines in the given orchestrator.

Usage:

tfx pipeline list [--endpoint=endpoint --engine=engine \
--iap_client_id=iap-client-id --namespace=namespace]
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples:

Apache Airflow:

tfx pipeline list --engine=airflow

Apache Beam:

tfx pipeline list --engine=beam

Kubeflow:

tfx pipeline list --engine=kubeflow --iap_client_id=iap-client-id \
--namespace=namespace --endpoint=endpoint

tfx run

The structure for commands in the tfx run command group is as follows:

tfx run command required-flags [optional-flags]

Use the following sections to learn more about the commands in the tfx run command group.

create

Creates a new run instance for a pipeline in the orchestrator.

Usage:

tfx run create --pipeline_name=pipeline-name [--endpoint=endpoint \
--engine=engine --iap_client_id=iap-client-id --namespace=namespace]
--pipeline_name=pipeline-name
The name of the pipeline.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples:

Apache Airflow:

tfx run create --engine=airflow --pipeline_name=pipeline-name

Apache Beam:

tfx run create --engine=beam --pipeline_name=pipeline-name

Kubeflow:

tfx run create --engine=kubeflow --pipeline_name=pipeline-name --iap_client_id=iap-client-id \
--namespace=namespace --endpoint=endpoint

terminate

Stops a run of a given pipeline.

** Important Note: Currently supported only in Kubeflow.

Usage:

tfx run terminate --run_id=run-id [--endpoint=endpoint --engine=engine \
--iap_client_id=iap-client-id --namespace=namespace]
--run_id=run-id
Unique identifier for a pipeline run.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples:

Kubeflow:

tfx run delete --engine=kubeflow --run_id=run-id --iap_client_id=iap-client-id \
--namespace=namespace --endpoint=endpoint

list

Lists all runs of a pipeline.

** Important Note: Currently not supported in Apache Beam.

Usage:

tfx run list --pipeline_name=pipeline-name [--endpoint=endpoint \
--engine=engine --iap_client_id=iap-client-id --namespace=namespace]
--pipeline_name=pipeline-name
The name of the pipeline.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples:

Apache Airflow:

tfx run list --engine=airflow --pipeline_name=pipeline-name

Kubeflow:

tfx run list --engine=kubeflow --pipeline_name=pipeline-name --iap_client_id=iap-client-id \
--namespace=namespace --endpoint=endpoint

status

Returns the current status of a run.

** Important Note: Currently not supported in Apache Beam.

Usage:

tfx run status --pipeline_name=pipeline-name --run_id=run-id [--endpoint=endpoint \
--engine=engine --iap_client_id=iap-client-id --namespace=namespace]
--pipeline_name=pipeline-name
The name of the pipeline.
--run_id=run-id
Unique identifier for a pipeline run.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples:

Apache Airflow:

tfx run status --engine=airflow --run_id=run-id --pipeline_name=pipeline-name

Kubeflow:

tfx run status --engine=kubeflow --run_id=run-id --pipeline_name=pipeline-name \
--iap_client_id=iap-client-id --namespace=namespace --endpoint=endpoint

delete

Deletes a run of a given pipeline.

** Important Note: Currently supported only in Kubeflow

Usage:

tfx run delete --run_id=run-id [--engine=engine --iap_client_id=iap-client-id \
--namespace=namespace --endpoint=endpoint]
--run_id=run-id
Unique identifier for a pipeline run.
--endpoint=endpoint

(Optional.) Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--engine=engine

(Optional.) The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--iap_client_id=iap-client-id
(Optional.) Client ID for IAP protected endpoint.
--namespace=namespace
(Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples:

Kubeflow:

tfx run delete --engine=kubeflow --run_id=run-id --iap_client_id=iap-client-id \
--namespace=namespace --endpoint=endpoint

tfx template [Experimental]

The structure for commands in the tfx template command group is as follows:

tfx template command required-flags [optional-flags]

Use the following sections to learn more about the commands in the tfx template command group. Template is an experimental feature and subject to change at any time.

list

List available TFX pipeline templates.

Usage:

tfx template list

copy

Copy a template to the destination directory.

Usage:

tfx template copy --model=model --pipeline_name=pipeline-name \
--destination_path=destination-path
--model=model
The name of the model built by the pipeline template.
--pipeline_name=pipeline-name
The name of the pipeline.
--destination_path=destination-path
The path to copy the template to.

Understanding TFX CLI Flags

Common flags

--engine=engine

The orchestrator to be used for the pipeline. The value of engine must match on of the following values:

  • airflow: sets engine to Apache Airflow
  • beam: sets engine to Apache Beam
  • kubeflow: sets engine to Kubeflow

If the engine is not set, the engine is auto-detected based on the environment.

** Important note: The orchestrator required by the DagRunner in the pipeline config file must match the selected or autodetected engine. Engine auto-detection is based on user environment. If Apache Airflow and Kubeflow Pipelines are not installed, then Apache Beam is used by default.

--pipeline_name=pipeline-name
The name of the pipeline.
--pipeline_path=pipeline-path
The path to the pipeline configuration file.
--run_id=run-id
Unique identifier for a pipeline run.

Kubeflow specific flags

--endpoint=endpoint

Endpoint of the Kubeflow Pipelines API service. The endpoint of your Kubeflow Pipelines API service is the same as URL of the Kubeflow Pipelines dashboard. Your endpoint value should be something like:

https://host-name/pipeline

If you do not know the endpoint for your Kubeflow Pipelines cluster, contact you cluster administrator.

If the --endpoint is not specified, the in-cluster service DNS name is used as the default value. This name works only if the CLI command executes in a pod on the Kubeflow Pipelines cluster, such as a Kubeflow Jupyter notebooks instance.

--iap_client_id=iap-client-id
Client ID for IAP protected endpoint.
--namespace=namespace
Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.
--package_path=package-path

Path to the compiled pipeline as a file. The compiled pipeline should be a compressed file (.tar.gz, .tgz, or .zip) or a YAML file (.yaml or .yml).

If package-path is not specified, TFX uses the following as the default path: current_directory/pipeline_name.tar.gz