Merge pull request #1 from krazyhaas/krazyhaas-tfx-rfc

rfcs/20190630-tfx-on-kfp.md

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+# Kubeflow Pipelines & TFX
+
+Status        | Implemented
+:------------ | :--------------------------------------------
+**Author(s)** | Ajay Gopinathan ([email protected])
+**Sponsor**   | Konstantinos Katsiapis ([email protected])
+**Created**   | 2019-06-30
+
+## Objective
+
+This RFC documents the design and engineering effort proposed by the
+[Kubeflow Pipelines](https://github.com/kubeflow/pipelines) team to support
+[TFX](https://www.tensorflow.org/tfx) with Kubeflow Pipelines (KFP).
+
+TFX is an open-source effort by the TensorFlow team aimed at providing users
+with tools for building production grade machine-learning (ML) workflows. TFX
+provides an ML pipeline authoring framework in Python which encodes
+Google’s best practices for ML pipelines, including:
+
+*   scalable and battle-tested components
+*   ML-focused pipeline design patterns
+*   strongly typed artifacts
+*   artifact provenance tracking through [ML Metadata](https://github.com/google/ml-metadata)
+
+An important value-proposition of the TFX framework is that it is agnostic to
+the orchestration framework. At launch, TFX supported two orchestration engines
+natively:
+
+*   [Apache Airflow](https://airflow.apache.org/) for running locally
+*   [Kubeflow Pipelines](https://www.kubeflow.org/docs/pipelines/)
+    for running in the cloud
+
+This document describes how TFX pipelines are run using
+Kubeflow Pipelines as its orchestration engine. It can be viewed as an extension
+of the main
+[TFX orchestration and configuration](https://github.com/tensorflow/community/tree/master/rfcs/20190718-tfx-orchestration.md)
+design document.
+
+## Motivation
+
+### TFX on Kubeflow Requirements
+
+The main focus areas for running TFX on Kubeflow were:
+
+*   **Portability:** The user-facing code in a TFX pipeline should be portable.
+    An early stated goal of our work was that we wanted the same pipeline to be
+    runnable on both Airflow and KFP with a _single-line change_ in the
+    pipeline construction code.
+*   **Scalability:** TFX on KFP should solve the use-case of large-scale
+    workloads, thereby showcasing the advantages of running on Google Cloud
+    Platform (GCP). This meant enabling the use of strongly differentiating GCP
+    services such as BigQuery, DataFlow and Cloud ML Engine for training and
+    serving in the pipeline code.
+
+At launch, both of these requirements were achieved. Using KFP required a
+single-line change in the pipeline code, and the sample pipeline for KFP
+showcased the use of GCP services for running workloads at scale.
+
+### Overview of TFX pipelines
+
+A TFX pipeline is a **logical pipeline** consisting of a series of components.
+Each component is defined in terms of inputs, outputs, and execution properties.
+Inputs and outputs are represented as channels of ML Metadata Artifacts.
+Logically, each component consists of three parts:
+
+*   `Driver`: Responsible for resolving input artifacts from the ML Metadata
+    store. Determines if the execution has been previously cached and if so,
+    whether the call to the `Executor` can be skipped.
+*   `Executor`: Executes the main logic of the component, and provides a uniform
+    interface around TFX libraries, as well as custom logic.
+*   `Publisher`: Records output artifacts produced by the `Executor`, and passes
+    these output artifact metadata to downstream steps.
+
+When running a pipeline under Airflow, the logical pipeline is converted to a
+series of _Airflow operators_. Each component comprises 3 operators representing
+the `Driver`, `Executor` and `Publisher`:
+
+![TFX message passing with Apache Airflow's XCom](20190630-tfx-on-kfp/tfx-oss-xcom-passing.png)
+
+At runtime, each `Driver` is responsible for resolving the metadata of input
+artifacts for a given component from MLMD, and for determining if any previously
+cached result of the component run can be used instead. If no cached result was
+found, the `Driver` invokes the `Executor` which performs the main application
+logic of the component. Upon completion, the `Publisher` writes the output
+metadata to MLMD. In the case of Airflow, the `Publisher` operator also
+publishes the same metadata for consumption by downstream components using
+Apache Airflow’s
+(XCom)[https://airflow.apache.org/concepts.html?highlight=xcom#xcoms] mechanism.
+
+## Design proposal
+
+### Kubeflow Pipelines Orchestration
+
+KFP uses [Argo](https://argoproj.github.io/argo/) as its orchestration engine.
+Argo is a Kubernetes-specific engine for orchestrating the execution of
+workflows where each individual workflow step is the execution of a
+containerized application. Argo employs a YAML-based specification to construct
+the workflow graph, which also specifies how each container’s application should
+be invoked.
+
+Passing data from upstream components to downstream ones is accomplished via
+[Argo output parameters](https://argoproj.github.io/docs/argo/examples/readme.html#output-parameters).
+The output results of a component are written to named, container-local files
+after every iteration. The contents of this file can then be passed as input
+parameters to subsequent steps. In particular, the contents are passed as raw
+strings which can be used as command-line arguments when invoking the downstream
+step using a templating mechanism in the Argo specification.
+
+In order to run a TFX pipeline on KFP, the user specifies `KubeflowRunner`
+instead of `AirflowDAGRunner` in the pipeline definition file. The logical
+pipeline definition itself remains unchanged, thus ensuring portability of
+pipelines across orchestration engines.
+
+In contrast to Apache Airflow, using `KubeflowRunner` and running the pipeline
+file does not actually launch the pipeline. Instead, the logical pipeline is
+**compiled**, resulting in a pipeline definition file in YAML, which contains
+the Argo specification for a workflow that can be run on Kubernetes. The user
+must then manually upload this pipeline definition file to a cluster running
+Kubeflow Pipelines before it can be run.
+
+![TFX on Kubeflow](20190630-tfx-on-kfp/tfx-on-kubeflow.png)
+
+In the Kubeflow cluster, users use an interactive UI to select and launch their
+pipeline. The KFP APIServer will then submit the uploaded pipeline definition to
+the **Argo controller** to orchestrate the actual workflow. The Argo
+specification specifies which container to execute and which command line
+invocation to use during each step.
+
+KFP provides a [Python SDK](https://www.kubeflow.org/docs/pipelines/sdk/) for
+constructing ML workflows on top of Argo. The main abstraction used is the
+[ContainerOp](https://www.kubeflow.org/docs/pipelines/sdk/build-component/)
+class, which can be viewed as a Python representation of a containerized
+workflow step in Argo. During compilation, each TFX component in the pipeline is
+transformed into a `ContainerOp`. There are three key elements of `ContainerOp`
+which are used when constructing the individual steps in TFX pipelines:
+
+*   **Image:** All TFX components are executed using the same pre-built
+    [Docker image](https://hub.docker.com/r/tensorflow/tfx) which contains the
+    TFX library and its dependencies.
+*   **Command-line arguments:** The command-line arguments specify how the image
+    should be invoked. In particular, they specify the exact TFX component and
+    executor that needs to run for a given step. Metadata representing input
+    artifacts are passed as arguments to a container step using Argo’s built-in
+    templating mechanism.
+*   **File outputs:** Argo can use the contents of container-local files
+    produced within each step as input data to be passed to downstream steps.
+    When the TFX container successfully completes the execution of an
+    `Executor`, it writes the ML Metadata representation (that is, Artifact and
+    ArtifactType protos) of output artifacts into named local files, which will
+    be passed along to downstream components by Argo. This can be viewed as the
+    **_publish_** step equivalent of using Airflow’s XCom mechanism.
+
+Consider the snippet of a TFX pipeline consisting of components `Transform`,
+`SchemaGen` and `Trainer`. `Transform` produces transformed examples as well as
+the transform graph itself, which are consumed by the `Trainer` component.
+`Trainer` also consumes the schema produced by `SchemaGen` component.
+
+![TFX with Kubeflow containers](20190630-tfx-on-kfp/tfx-kfp-containers.png)
+
+In KFP, each component is now represented as the execution of the TFX container
+image. Individual components have customized command-line invocations, which are
+based on their input arguments and which TFX executor to execute.
+The execution of each step is controlled by instances of the
+[`ExecutorRunner`](https://github.com/tensorflow/tfx/blob/master/tfx/orchestration/kubeflow/executor_wrappers.py)
+base class. This class is responsible for constructing the arguments required by
+all TFX executors, namely:
+
+*   `input_dict`: A dictionary of input artifacts. These are constructed at
+    runtime using the values of the Argo output-parameters that were passed in
+    as inputs.
+*   `output_dict`: A dictionary of output artifacts. These are pre-determined
+    for each derived class of `ExecutorRunner` and specialized per-component.
+*   `exec_properties`: A dictionary of runtime parameters, whose values may
+    either be primitive Python types, or serialized JSON representation of
+    protocol buffers.
+
+The arguments are constructed and used to call into the specified TFX `Executor`
+(for example, `tfx.components.trainer.executor.Executor`). If execution is
+successful, `ExecutorRunner` writes each output artifact (as specified in
+`output_dict`) and their schema types in JSON-serialized format into a container
+local file. The contents of this file are then passed as ML Metadata artifacts
+for consumption by downstream steps. The KFP UI visualizes both input and output
+parameters for each step.
+
+![TFX artifacts with the Kubeflow UI](20190630-tfx-on-kfp/tfx-kfp-ui.png)
+
+### ML Metadata Tracking
+
+In contrast to Airflow, TFX on KFP does not have drivers and publishers.
+Instead, metadata is recorded passively in KFP’s APIServer, by parsing the
+status of the Argo workflow custom resource definition (CRD) periodically. Each
+Argo workflow CRD status contains recorded values of Argo output parameters
+(that is, the contents of the named local files) upon successful completion of
+the workflow step. KFP employs a custom Kubernetes controller called
+PersistenceAgent, which  periodically polls for the latest status of all Argo
+workflow resources, and updates the state in the APIServer.
+
+![TFX with Kubeflow Pipelines and Argo](20190630-tfx-on-kfp/tfx-kfp-argo-workflow.png)
+
+The APIServer parses Argo workflows and looks for Argo output parameters that
+look like serialized MLMD artifacts in specially named files (by convention, the
+files are named `/output/ml_metadata/{output_name}`). These artifacts and their
+types are then recorded into an MLMD instance powered by the same MySQL server
+that backs KFP’s persistent data.
+
+## Future Work
+
+While TFX on KFP works, it still does not have feature parity with the Apache
+Airflow version. We are exploring the following directions concurrently to close
+the gap between the two orchestrators:
+
+*   **Metadata-driven orchestration**: The current version of TFX on KFP records
+    artifacts in MLMD, but does so passively. This is due to the lack of drivers
+    and publishers in the initial implementation. Hence, lineage tracking and
+    caching is not currently possible.
+*   **Enabling arbitrary user containers with MLMD artifacts as the interface
+    between pipeline steps:** Currently, incorporating a custom step in a TFX
+    OSS pipeline requires users to implement a custom executor. Users in Cloud
+    frequently have an existing application, written in a non-Python language
+    (such as R, Java, etc), which they would like to plug into their TFX-based
+    pipeline.
+*   **Unified pipeline authoring experience:** TFX and KFP both present users
+    with a Python-based DSL for constructing their pipelines. The DSL constructs
+    look very similar from the user’s point of view, but are fundamentally very
+    different underneath. This has led to customer confusion. Unifying the DSL,
+    and presenting a single user-facing experience for constructing ML pipelines
+    is a goal that we’re actively exploring.
+*   **Pipeline-level runtime parameters:** KFP provides the possibility of
+    specifying pipeline-level parameters so users can run the same pipeline with
+    different combinations of control parameters. Since the pipeline definition
+    is a YAML-based file equipped with a templating mechanism, all pipeline
+    runtime parameters are restricted to string types. This presents a challenge
+    for specifying pipeline parameters at runtime that are not simple strings
+    (for example, the number of training steps in `Trainer` is specified in a
+    protocol buffer which must be serialized at runtime to be consumed by the
+    component). Contrast this to the Airflow scenario, where arbitrary code can
+    execute to yield runtime parameters since the pipeline definition and
+    runtime environment exist in the same execution scope.
+