Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Dengan Vertex AI Pipelines, Anda dapat menjalankan pipeline machine learning (ML)
yang dibangun menggunakan Kubeflow Pipelines SDK atau TensorFlow Extended dengan cara
serverless. Dokumen ini menjelaskan cara menggunakan Vertex AI Pipelines untuk
memvisualisasikan, menganalisis, dan membandingkan proses pipeline.
Untuk mempelajari lebih lanjut cara menjalankan dan menjadwalkan pipeline, baca panduan untuk
menjalankan pipeline.
Memvisualisasikan proses pipeline menggunakan konsol Google Cloud
Gunakan petunjuk berikut untuk mempelajari lebih lanjut cara menggunakan konsol Google Cloud untuk
memvisualisasikan proses pipeline.
Di bagian Pilih project terbaru, klik kotak project.
Klik nama proses pipeline yang ingin Anda analisis.
Halaman pipeline akan muncul dan menampilkan grafik runtime pipeline.
Ringkasan pipeline muncul di panel Analisis proses pipeline.
Grafik pipeline menampilkan langkah-langkah alur kerja di pipeline.
Ringkasan pipeline menampilkan informasi dasar tentang proses pipeline
dan parameter yang digunakan dalam proses pipeline ini.
Untuk mempelajari langkah atau artefak pipeline lebih lanjut, klik langkah atau artefak
dalam grafik runtime.
Panel Analisis proses pipeline menampilkan informasi tentang langkah atau artefak
pipeline ini.
Untuk langkah-langkah pipeline, informasi ini mencakup detail eksekusi,
parameter input yang diteruskan ke langkah tersebut, dan setiap parameter output
yang diteruskan ke pipeline.
Untuk mempelajari langkah pipeline yang dipilih lebih lanjut:
Klik Lihat tugas untuk melihat detail tugas.
Halaman detail tugas mencakup informasi seperti jenis mesin yang digunakan
untuk menjalankan langkah ini, image container tempat langkah dijalankan, dan
kunci enkripsi yang digunakan oleh langkah ini.
Klik Lihat log untuk melihat log yang dihasilkan oleh langkah pipeline ini.
Panel log akan muncul. Gunakan log untuk membantu men-debug perilaku
pipeline Anda.
Untuk artefak, informasi ini mencakup jenis data artefak,
lokasi penyimpanan artefak, dan metrik artefak.
Untuk mempelajari artefak yang dipilih lebih lanjut:
Klik URI artefak untuk membuka lokasi tersebut di Cloud Storage.
Klik Open in ML Metadata untuk melihat silsilah artefak di
Vertex ML Metadata. Untuk mengetahui informasi selengkapnya tentang silsilah artefak pipeline, lihat Melacak silsilah artefak pipeline.
Jika Anda baru mengenal Vertex ML Metadata, baca pengantar Vertex ML Metadata.
Membandingkan operasi pipeline menggunakan konsol Google Cloud
Gunakan petunjuk berikut untuk membandingkan proses pipeline di konsol Google Cloud .
[[["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 2025-09-04 UTC."],[],[],null,["# Visualize and analyze pipeline results\n\n| To learn more,\n| run the \"Build Vertex AI Pipelines that generate model metrics and visualizations, and compare pipeline runs\" notebook in one of the following\n| environments:\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb)\n|\n|\n| \\|\n|\n| [Open in Colab Enterprise](https://console.cloud.google.com/vertex-ai/colab/import/https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fmetrics_viz_run_compare_kfp.ipynb)\n|\n|\n| \\|\n|\n| [Open\n| in Vertex AI Workbench](https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fmetrics_viz_run_compare_kfp.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb)\n\nVertex AI Pipelines lets you run machine learning (ML) pipelines\nthat were built using the Kubeflow Pipelines SDK or TensorFlow Extended in a serverless\nmanner. This document describes how to use Vertex AI Pipelines to\nvisualize, analyze, and compare pipeline runs.\n\nTo learn more about running and scheduling pipelines, read the guide to\n[running a pipeline](/vertex-ai/docs/pipelines/run-pipeline).\n\nVisualize pipeline runs using Google Cloud console\n--------------------------------------------------\n\nUse the following instructions to learn more about using Google Cloud console to\nvisualize pipeline runs.\n\n1. Open Vertex AI Pipelines in Google Cloud console.\n\n [Go to Vertex AI Pipelines](https://console.cloud.google.com/vertex-ai/pipelines?project=_)\n2. In **Select a recent project**, click a project tile.\n\n3. Click the run name of the pipeline run that you want to analyze.\n\n The pipeline run page appears and displays the pipeline's runtime graph.\n The pipeline's summary appears in the **Pipeline run analysis** pane.\n - The pipeline graph shows the workflow steps in the pipeline.\n - The pipeline summary shows the basic information about the pipeline run and the parameters that were used in this pipeline run.\n4. To learn more about a pipeline step or artifact, click the step or artifact\n in the runtime graph.\n\n The **Pipeline run analysis** pane shows information about this pipeline\n step or artifact.\n - For pipeline steps, this information includes execution details, the\n input parameters that were passed to the step, and any output parameters\n that the step passed to the pipeline.\n\n To learn more about the selected pipeline step:\n - Click **View job** to see the job details.\n\n The job details page includes information like the machine type used\n to run this step, the container image that the step runs in, and the\n encryption key used by this step.\n - Click **View logs** to see the logs produced by this pipeline step.\n\n The logs pane appears. Use the logs to help debug the behavior of\n your pipeline.\n - For artifacts, this information includes the data type of the artifact,\n the location where the artifact is stored, and the artifact's metrics.\n\n To learn more about the selected artifact:\n - Click the artifact's **URI** to open that location in Cloud Storage.\n\n - Click **Open in ML Metadata** to view the lineage of the artifact in\n Vertex ML Metadata. For more information about pipeline\n artifact lineage, see [Track the lineage of pipeline artifacts](/vertex-ai/docs/pipelines/lineage).\n If you're new to Vertex ML Metadata, read the [introduction to Vertex ML Metadata](/vertex-ai/docs/ml-metadata/introduction).\n\nCompare pipeline runs using Google Cloud console\n------------------------------------------------\n\nUse the following instructions to compare pipeline runs in Google Cloud console.\n\n1. Open Vertex AI Pipelines in Google Cloud console.\n\n [Go to Vertex AI Pipelines](https://console.cloud.google.com/vertex-ai/pipelines?project=_)\n2. Select the checkboxes of the pipeline runs that you want to compare.\n\n3. In the Vertex AI Pipelines menubar, click\n **compare_arrows\n Compare**.\n\n The **Compare runs** pane appears.\n4. The **Compare runs** pane lists your pipeline's parameters and metrics.\n\n This information helps you to perform analysis, such as analyzing how\n different sets of hyperparameters affect a model's metrics.\n\nWhat's next\n-----------\n\n- Read the [introduction to Vertex AI Pipelines](/vertex-ai/docs/pipelines/introduction) to learn more about orchestrating ML workflows.\n- Learn how to [build a machine learning pipeline](/vertex-ai/docs/pipelines/build-pipeline)."]]
