Bermigrasi dari Kubeflow Pipelines ke Vertex AI Pipelines
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Simpan dan kategorikan konten berdasarkan preferensi Anda.
Bagi developer yang berpengalaman membangun pipeline Kubeflow, penting halnya untuk memahami perbedaan antara Vertex AI Pipelines dengan Kubeflow Pipelines.
Penerusan data (input/output)
Penerusan data menggunakan input dan output berbeda, mulai dari Kubeflow Pipelines SDK v1 hingga Kubeflow Pipelines SDK v2. Kubeflow Pipelines SDK v2 memiliki pemisahan parameter dan artefak, sehingga tidak dapat diteruskan ke satu sama lain.
Untuk mengetahui informasi lebih mendetail, lihat
Dasar-Dasar Pipeline Kubeflow Pipelines dan Jenis Data Kubeflow Pipelines.
Penggunaan versi bahasa khusus domain (DSL)
Vertex AI Pipelines dapat menjalankan pipeline yang dibangun menggunakan TFX v0.30.0 atau yang lebih baru, atau bahasa khusus domain (DSL) Kubeflow Pipelines SDK v2.
DSL Kubeflow Pipelines SDK v2 tersedia di Kubeflow Pipelines SDK v1.6 atau yang lebih baru.
Kubeflow Pipeline dapat menjalankan pipeline yang dibangun menggunakan Kubeflow Pipelines SDK. Kubeflow Pipelines v1.6 atau yang lebih baru juga dapat menjalankan pipeline yang dibangun menggunakan DSL Kubeflow Pipelines SDK v2.
Penyimpanan
Kubeflow Pipelines dan Vertex AI Pipelines menangani penyimpanan dengan cara berbeda. Di Kubeflow Pipeline, Anda dapat menggunakan resource Kubernetes seperti klaim volume persisten. Di Vertex AI Pipelines, data Anda disimpan di Cloud Storage dan dipasang ke dalam komponen menggunakan Cloud Storage FUSE.
Di Vertex AI Pipelines, Anda dapat menggunakan Google Cloud layanan untuk menyediakan resource. Misalnya, Anda dapat menggunakan Cloud Storage FUSE untuk mengakses bucket Cloud Storage sebagai volume yang terpasang pada langkah pipeline. Jika Cloud Storage URI Anda adalah
gs://example-bucket/example-pipeline, maka container komponen pipeline Anda dapat menggunakan Cloud Storage FUSE untuk mengakses URI tersebut dengan jalur berikut: /gcs/example-bucket/example-pipeline.
Saat Anda menjalankan pipeline menggunakan Vertex AI Pipelines, root pipeline harus sudah ditentukan dalam anotasi @pipeline atau saat Anda membuat operasi pipeline.
Di Kubeflow Pipelines, cara menentukan root pipeline bersifat opsional. Artefak yang dijalankan oleh pipeline disimpan menggunakan MinIO secara default.
Fitur yang tidak didukung di Vertex AI Pipelines
Fitur Kubeflow Pipelines berikut tidak didukung di
Vertex AI Pipelines.
Masa Berlaku Cache: Di Kubeflow Pipelines, Anda dapat menentukan bahwa eksekusi komponen yang di-cache akan berakhir setelah jangka waktu tertentu menggunakan DSL Kubeflow Pipelines SDK v1.
Anda tidak dapat menentukan bahwa eksekusi komponen akan berakhir setelah jangka waktu yang ditentukan menggunakan DSL Kubeflow Pipelines SDK v2.
Di Vertex AI Pipelines, saat menjalankan pipeline menggunakan create_run_from_job_spec, Anda dapat menggunakan argumen enable_caching untuk menentukan bahwa operasi pipeline ini tidak menggunakan caching.
Pengulangan: Di Pipeline Kubeflow, Anda dapat menentukan komponen pipeline yang dipanggil secara rekursif.
Vertex AI Pipelines tidak mendukung komponen pipeline yang dipanggil secara rekursif.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-19 UTC."],[],[],null,["# Migrate from Kubeflow Pipelines to Vertex AI Pipelines\n\nFor developers with experience building Kubeflow pipelines it is\nimportant to understand the following ways that Vertex AI Pipelines is\ndifferent from Kubeflow Pipelines.\n\n### Data passing (inputs/outputs)\n\n- Data passing using inputs and outputs differs from Kubeflow Pipelines SDK v1 to Kubeflow Pipelines SDK v2. Kubeflow Pipelines SDK v2 has the separation of parameters and artifacts, and they can't be passed into one another. For more detailed information, see [Kubeflow Pipelines Pipelines Basics](https://www.kubeflow.org/docs/components/pipelines/v2/pipelines/pipeline-basics/) and [Kubeflow Pipelines Data Types](https://www.kubeflow.org/docs/components/pipelines/v2/data-types/).\n\n### Domain-specific language (DSL) version usage\n\n- Vertex AI Pipelines can run pipelines that were built using\n TFX v0.30.0 or later, *or* the Kubeflow Pipelines SDK v2\n domain-specific language (DSL).\n\n The Kubeflow Pipelines SDK v2 DSL is available in Kubeflow Pipelines SDK v1.6 or\n later.\n\n Kubeflow Pipelines can run pipelines that were built using the\n Kubeflow Pipelines SDK. Kubeflow Pipelines v1.6 or later can also run pipelines\n built using the Kubeflow Pipelines SDK v2 DSL.\n\n### Storage\n\n- Kubeflow Pipelines and Vertex AI Pipelines handle\n storage differently. In Kubeflow Pipelines you can make use of Kubernetes\n resources such as persistent volume claims. In Vertex AI Pipelines\n your data is stored on Cloud Storage, and mounted into your components\n using [Cloud Storage FUSE](/storage/docs/gcs-fuse).\n\n In Vertex AI Pipelines, you can use Google Cloud services to make\n resources available --- for example, you can use Cloud Storage FUSE\n to access a Cloud Storage bucket as a mounted volume in a pipeline\n step. If your Cloud Storage URI is\n `gs://example-bucket/example-pipeline`, then your pipeline component's\n container can use Cloud Storage FUSE to access that URI as the\n following path: `/gcs/example-bucket/example-pipeline`.\n | **Important:** It's best practice that you avoid hardcoding the paths to external resources into your pipeline. Instead, pass the paths to external resources into your pipeline as a parameter. This makes it easier for you to run your pipeline in different environments, and to change the location of the resources used in a pipeline run.\n- When you run a pipeline using Vertex AI Pipelines, the pipeline root\n must have been specified in the `@pipeline` annotation or when you created\n the pipeline run.\n\n In Kubeflow Pipelines, specifying the pipeline root is optional. The\n artifacts of a pipeline run are stored using [MinIO](https://min.io/) by default.\n\n### Features not supported in Vertex AI Pipelines\n\n- The following Kubeflow Pipelines features are not supported in\n Vertex AI Pipelines.\n\n - **Cache Expiration**: In Kubeflow Pipelines, you can specify that\n cached component executions\n expire after a specified amount of time using the Kubeflow Pipelines SDK v1\n DSL.\n\n You can't specify that component executions expire after a\n specified amount of time using the Kubeflow Pipelines SDK v2 DSL.\n\n In Vertex AI Pipelines, when you run a pipeline using\n `create_run_from_job_spec` you can use the `enable_caching` argument to\n specify that this pipeline run does not use caching.\n - **Recursion**: In Kubeflow Pipelines, you can specify pipeline\n components that are\n called recursively.\n\n Vertex AI Pipelines doesn't support pipeline\n components that are called recursively."]]
