Using the TFX Command-line Interface

The TFX command-line interface (CLI) performs a full range of pipeline actions using pipeline orchestrators, such as Kubeflow Pipelines, Vertex Pipelines. Local orchestrator can be also used for faster development or debugging. Apache Beam and Apache airflow is supported as experimental features. 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.

!!! Note The TFX CLI doesn't currently provide compatibility guarantees. The CLI interface might change as new versions are released.

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.

!!! Warning Currently not all commands are supported in every orchestrator. Such commands explicitly mention the engines supported.

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 \
--build_image --build_base_image=build-base-image]

--pipeline_path=pipeline-path{.variable} : The path to the pipeline configuration file.

--endpoint=endpoint{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **local**: sets engine to local orchestrator
-   **vertex**: sets engine to Vertex Pipelines
-   **airflow**: (experimental) sets engine to Apache Airflow
-   **beam**: (experimental) sets engine to Apache Beam

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

!!! note "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 the local orchestrator is used by default.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint when using Kubeflow Pipelines.

--namespace=namespace{.variable} : (Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

--build_image

: (Optional.) When the engine{.variable} is kubeflow or vertex, TFX creates a container image for your pipeline if specified. Dockerfile in the current directory will be used, and TFX will automatically generate one if not exists.

The built image will be pushed to the remote registry which is specified in `KubeflowDagRunnerConfig` or `KubeflowV2DagRunnerConfig`.

--build_base_image=build-base-image{.variable}

: (Optional.) When the engine{.variable} 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.

Examples

Kubeflow:

tfx pipeline create --engine=kubeflow --pipeline_path=pipeline-path \
--iap_client_id=iap-client-id --namespace=namespace --endpoint=endpoint \
--build_image

Local:

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

Vertex:

tfx pipeline create --engine=vertex --pipeline_path=pipeline-path \
--build_image

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

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 --build_image]

--pipeline_path=pipeline-path{.variable} : The path to the pipeline configuration file.

--endpoint=endpoint{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **local**: sets engine to local orchestrator
-   **vertex**: sets engine to Vertex Pipelines
-   **airflow**: (experimental) sets engine to Apache Airflow
-   **beam**: (experimental) sets engine to Apache Beam

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

!!! note "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 the local orchestrator is used by default.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : (Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

--build_image

: (Optional.) When the engine{.variable} is kubeflow or vertex, TFX creates a container image for your pipeline if specified. Dockerfile in the current directory will be used.

The built image will be pushed to the remote registry which is specified in `KubeflowDagRunnerConfig` or `KubeflowV2DagRunnerConfig`.

Examples

Kubeflow:

tfx pipeline update --engine=kubeflow --pipeline_path=pipeline-path \
--iap_client_id=iap-client-id --namespace=namespace --endpoint=endpoint \
--build_image

Local:

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

Vertex:

tfx pipeline update --engine=vertex --pipeline_path=pipeline-path \
--build_image

compile

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

Checks if the pipeline path is valid.
Checks if the pipeline details are extracted successfully from the pipeline config file.
Checks if the DagRunner in the pipeline config matches the engine.
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 [--engine=engine]

--pipeline_path=pipeline-path{.variable} : The path to the pipeline configuration file.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **local**: sets engine to local orchestrator
-   **vertex**: sets engine to Vertex Pipelines
-   **airflow**: (experimental) sets engine to Apache Airflow
-   **beam**: (experimental) sets engine to Apache Beam

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

!!! note "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 the local orchestrator is used by default.

Examples

Kubeflow:

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

Local:

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

Vertex:

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

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{.variable} : The path to the pipeline configuration file.

--endpoint=endpoint{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **local**: sets engine to local orchestrator
-   **vertex**: sets engine to Vertex Pipelines
-   **airflow**: (experimental) sets engine to Apache Airflow
-   **beam**: (experimental) sets engine to Apache Beam

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

!!! note "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 the local orchestrator is used by default.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : (Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples

Kubeflow:

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

Local:

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

Vertex:

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

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{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **local**: sets engine to local orchestrator
-   **vertex**: sets engine to Vertex Pipelines
-   **airflow**: (experimental) sets engine to Apache Airflow
-   **beam**: (experimental) sets engine to Apache Beam

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

!!! note "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 the local orchestrator is used by default.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : (Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Examples

Kubeflow:

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

Local:

tfx pipeline list --engine=local

Vertex:

tfx pipeline list --engine=vertex

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. For Kubeflow, the most recent pipeline version of the pipeline in the cluster is used.

Usage:

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

--pipeline_name=pipeline-name{.variable} : The name of the pipeline.

--endpoint=endpoint{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **local**: sets engine to local orchestrator
-   **vertex**: sets engine to Vertex Pipelines
-   **airflow**: (experimental) sets engine to Apache Airflow
-   **beam**: (experimental) sets engine to Apache Beam

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

!!! note "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 the local orchestrator is used by default.

--runtime_parameter=parameter-name{.variable}=parameter-value{.variable} : (Optional.) Sets a runtime parameter value. Can be set multiple times to set values of multiple variables. Only applicable to airflow, kubeflow and vertex engine.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : (Optional.) Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

--project=GCP-project-id{.variable} : (Required for Vertex.) GCP project id for the vertex pipeline.

--region=GCP-region{.variable} : (Required for Vertex.) GCP region name like us-central1. See [Vertex documentation](https://cloud.google.com/vertex-ai/docs/general/locations) for available regions.

Examples

Kubeflow:

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

Local:

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

Vertex:

tfx run create --engine=vertex --pipeline_name=pipeline-name \
  --runtime_parameter=var_name=var_value \
  --project=gcp-project-id --region=gcp-region

terminate

Stops a run of a given pipeline.

!!! note "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{.variable} : Unique identifier for a pipeline run.

--endpoint=endpoint{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow

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

!!! note "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 the local orchestrator is used by default.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : (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.

!!! note "Important Note" Currently not supported in Local and 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{.variable} : The name of the pipeline.

--endpoint=endpoint{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **airflow**: (experimental) sets engine to Apache Airflow

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

!!! note "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 the local orchestrator is used by default.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : (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 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.

!!! note "Important Note" Currently not supported in Local and 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{.variable} : The name of the pipeline.

--run_id=run-id{.variable} : Unique identifier for a pipeline run.

--endpoint=endpoint{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **airflow**: (experimental) sets engine to Apache Airflow

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

!!! note "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 the local orchestrator is used by default.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : (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 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.

!!! note 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{.variable} : Unique identifier for a pipeline run.

--endpoint=endpoint{.variable}

: (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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow

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

!!! note "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 the local orchestrator is used by default.

--iap_client_id=iap-client-id{.variable} : (Optional.) Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : (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{.variable} : The name of the model built by the pipeline template.

--pipeline_name=pipeline-name{.variable} : The name of the pipeline.

--destination_path=destination-path{.variable} : The path to copy the template to.

Understanding TFX CLI Flags

Common flags

--engine=engine{.variable}

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

-   **kubeflow**: sets engine to Kubeflow
-   **local**: sets engine to local orchestrator
-   **vertex**: sets engine to Vertex Pipelines
-   **airflow**: (experimental) sets engine to Apache Airflow
-   **beam**: (experimental) sets engine to Apache Beam

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

!!! note "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 the local orchestrator is used by default.

--pipeline_name=pipeline-name{.variable} : The name of the pipeline.

--pipeline_path=pipeline-path{.variable} : The path to the pipeline configuration file.

--run_id=run-id{.variable} : Unique identifier for a pipeline run.

Kubeflow specific flags

--endpoint=endpoint{.variable}

: 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](https://www.kubeflow.org/docs/components/notebooks/jupyter-tensorflow-examples/){.external} instance.

--iap_client_id=iap-client-id{.variable} : Client ID for IAP protected endpoint.

--namespace=namespace{.variable} : Kubernetes namespace to connect to the Kubeflow Pipelines API. If the namespace is not specified, the value defaults to kubeflow.

Generated files by TFX CLI

When pipelines are created and run, several files are generated for pipeline management.

${HOME}/tfx/local, beam, airflow, vertex
- Pipeline metadata read from the configuration is stored under ${HOME}/tfx/${ORCHESTRATION_ENGINE}/${PIPELINE_NAME}. This location can be customized by setting environment varaible like AIRFLOW_HOME or KUBEFLOW_HOME. This behavior might be changed in future releases. This directory is used to store pipeline information including pipeline ids in the Kubeflow Pipelines cluster which is needed to create runs or update pipelines.
- Before TFX 0.25, these files were located under ${HOME}/${ORCHESTRATION_ENGINE}. In TFX 0.25, files in the old location will be moved to the new location automatically for smooth migration.
- From TFX 0.27, kubeflow doesn't create these metadata files in local filesystem. However, see below for other files that kubeflow creates.
(Kubeflow only) Dockerfile and a container image
- Kubeflow Pipelines requires two kinds of input for a pipeline. These files are generated by TFX in the current directory.
- One is a container image which will be used to run components in the pipeline. This container image is built when a pipeline for Kubeflow Pipelines is created or updated with --build-image flag. TFX CLI will generate Dockerfile if not exists, and will build and push a container image to the registry specified in KubeflowDagRunnerConfig.

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cli.md

cli.md

Using the TFX Command-line Interface

About the TFX CLI

tfx pipeline

create

Examples

update

Examples

compile

Examples

delete

Examples

list

Examples

tfx run

create

Examples

terminate

Examples

list

Examples

status

Examples

delete

Examples

tfx template [Experimental]

list

copy

Understanding TFX CLI Flags

Common flags

Kubeflow specific flags

Generated files by TFX CLI

Files

cli.md

Latest commit

History

cli.md

File metadata and controls

Using the TFX Command-line Interface

About the TFX CLI

tfx pipeline

create

Examples

update

Examples

compile

Examples

delete

Examples

list

Examples

tfx run

create

Examples

terminate

Examples

list

Examples

status

Examples

delete

Examples

tfx template [Experimental]

list

copy

Understanding TFX CLI Flags

Common flags

Kubeflow specific flags

Generated files by TFX CLI