Pengantar pelatihan kustom: Melatih model klasifikasi gambar kustom
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Simpan dan kategorikan konten berdasarkan preferensi Anda.
Halaman ini menunjukkan cara menjalankan aplikasi pelatihan TensorFlow Keras di Vertex AI. Model khusus ini melatih model klasifikasi gambar yang dapat mengklasifikasikan bunga berdasarkan jenisnya.
Setiap halaman mengasumsikan bahwa Anda telah menjalankan petunjuk dari
halaman tutorial sebelumnya.
Bagian selanjutnya dari dokumen ini mengasumsikan bahwa Anda menggunakan lingkungan
Cloud Shell yang sama dengan yang Anda buat saat mengikuti halaman pertama tutorial
ini. Jika sesi Cloud Shell asli Anda tidak lagi
terbuka, Anda dapat kembali ke lingkungan dengan melakukan tindakan berikut:
In the Google Cloud console, activate Cloud Shell.
Klik add_box Create untuk membuka panel Train new model.
Pada langkah Choose training method, lakukan hal berikut:
Di menu drop-down Set data, pilih No managed dataset. Aplikasi pelatihan khusus ini memuat data dari library TensorFlow Datasets, bukan set data Vertex AI yang terkelola.
Pastikan Custom training (advanced) dipilih.
Klik Continue.
Pada langkah Model details, di kolom Name, masukkan hello_custom. Klik Continue.
Pada langkah Training container, berikan informasi yang diperlukan kepada Vertex AI untuk menggunakan paket pelatihan yang Anda upload ke Cloud Storage:
Pilih Prebuilt container.
Di menu drop-down Model framework, pilih TensorFlow.
Di menu drop-down Model framework version, pilih 2.3.
Di kolom Package location, masukkan cloud-samples-data/ai-platform/hello-custom/hello-custom-sample-v1.tar.gz.
Di kolom Python module, masukkan trainer.task. trainer adalah nama paket Python di tarball, dan task.py berisi kode pelatihan Anda. Oleh karena itu, trainer.task adalah nama modul yang akan dijalankan oleh Vertex AI.
Di kolom Model output directory, klik Browse. Lakukan hal berikut di panel Select folder:
Buka bucket Cloud Storage Anda.
Klik Create new folder create_new_folder.
Beri nama folder baru output. Lalu klik Create.
Klik Select.
Pastikan kolom tersebut memiliki nilai gs://BUCKET_NAME/output, di mana BUCKET_NAME adalah nama bucket Cloud Storage Anda.
Nilai ini diteruskan ke Vertex AI di
kolom API baseOutputDirectory yang menetapkan beberapa variabel lingkungan yang dapat diakses oleh aplikasi pelatihan Anda saat dijalankan.
Misalnya, saat Anda menetapkan kolom ini ke gs://BUCKET_NAME/output, Vertex AI akan menetapkan variabel lingkungan AIP_MODEL_DIR ke gs://BUCKET_NAME/output/model. Di akhir pelatihan, Vertex AI akan menggunakan artefak apa pun di direktori AIP_MODEL_DIR untuk membuat resource model.
Pada langkah Hyperparameters opsional, pastikan kotak Enable hyperparameter tuning tidak dicentang. Tutorial ini tidak menggunakan penyesuaian hyperparameter. Klik Continue.
Pada langkah Compute and pricing, alokasikan resource untuk tugas pelatihan kustom:
Di menu drop-down Region, pilih us-central1 (Iowa).
Di menu drop-down Machine type, pilih n1-standard-4 dari bagian Standard.
Jangan tambahkan akselerator atau kumpulan pekerja untuk tutorial ini. Klik Continue.
Pada langkah Prediction container, berikan informasi yang diperlukan Vertex AI untuk menyajikan prediksi:
Pilih Prebuilt container.
Di bagian Prebuilt container settings, lakukan hal berikut:
Di menu drop-down Model framework, pilih TensorFlow.
Di menu drop-down Model framework version, pilih 2.3.
Di menu drop-down Accelerator type, pilih None.
Pastikan kolom Model directory memiliki nilai gs://BUCKET_NAME/output, di mana BUCKET_NAME adalah nama bucket Cloud Storage Anda. Ini cocok dengan nilai Model output directory yang Anda sediakan pada langkah sebelumnya.
Biarkan kolom di bagian Predict schemata kosong.
Klik Start training untuk memulai pipeline pelatihan kustom.
Sekarang Anda dapat melihat pipeline pelatihan baru, yang bernama hello_custom, di halaman Training. (Anda mungkin perlu memuat ulang halaman.) Pipeline pelatihan melakukan dua hal utama:
Pipeline pelatihan membuat resource tugas kustom bernama hello_custom-custom-job. Setelah beberapa saat, Anda dapat melihat referensi ini di halaman Custom jobs di bagian Training:
Tugas kustom menjalankan aplikasi pelatihan menggunakan resource komputasi yang Anda tentukan di bagian ini.
Setelah tugas kustom selesai, pipeline pelatihan akan mencari artefak yang dibuat oleh aplikasi pelatihan Anda di direktori output/model/ bucket Cloud Storage Anda. Pipeline menggunakan artefak ini untuk membuat resource model.
Memantau pelatihan
Untuk melihat log pelatihan, lakukan hal berikut:
Di konsol Google Cloud , di bagian Vertex AI, buka
halaman Custom jobs.
[[["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,["# Hello custom training: Train a custom image classification model\n\n| To learn more,\n| run the following notebooks in the environment of your choice:\n|\n| - \"Use the Vertex AI SDK for Python to train and deploy a custom image classification model for batch prediction.\":\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.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%2Fcustom%2Fsdk-custom-image-classification-batch.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%2Fcustom%2Fsdk-custom-image-classification-batch.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb)\n| - \"Use the Vertex AI SDK for Python to train and deploy a custom image classification model for online prediction.\":\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-online.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%2Fcustom%2Fsdk-custom-image-classification-online.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%2Fcustom%2Fsdk-custom-image-classification-online.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-online.ipynb)\n\nThis page shows you how to run a TensorFlow Keras training application on\nVertex AI. This particular model trains an image classification\nmodel that can classify flowers by type.\nThis tutorial has several pages:\n\n\u003cbr /\u003e\n\n1. [Setting up your project and environment.](/vertex-ai/docs/tutorials/image-classification-custom)\n\n2. Training a custom image classification model.\n\n3. [Serving predictions from a custom image classification\n model.](/vertex-ai/docs/tutorials/image-classification-custom/serving)\n\n4. [Cleaning up your project.](/vertex-ai/docs/tutorials/image-classification-custom/cleanup)\n\nEach page assumes that you have already performed the instructions from the\nprevious pages of the tutorial.\nThe rest of this document assumes that you are using the same Cloud Shell environment that you created when following the [first page of this\ntutorial](/vertex-ai/docs/tutorials/image-classification-custom). If your original Cloud Shell session is no longer open, you can return to the environment by doing the following:\n\n\u003cbr /\u003e\n\n1. In the Google Cloud console, activate Cloud Shell.\n\n [Activate Cloud Shell](https://console.cloud.google.com/?cloudshell=true)\n2. In the Cloud Shell session, run the following command:\n\n ```bash\n cd hello-custom-sample\n ```\n\nRun a custom training pipeline\n------------------------------\n\nThis section describes using the training package that you uploaded to\nCloud Storage to run a Vertex AI custom training\npipeline.\n\n1. In the Google Cloud console, in the Vertex AI section, go to\n the **Training pipelines** page.\n\n [Go to Training pipelines](https://console.cloud.google.com/vertex-ai/training/training-pipelines)\n2. Click **add_box\n Create** to open the **Train new model** pane.\n\n3. On the **Choose training method** step, do the following:\n\n 1. In the **Dataset** drop-down list, select **No managed dataset** . This\n particular training application loads data from the [TensorFlow\n Datasets](https://www.tensorflow.org/datasets/) library rather than a managed Vertex AI\n dataset.\n\n 2. Ensure that **Custom training (advanced)** is selected.\n\n Click **Continue**.\n4. On the **Model details** step, in the **Name** field, enter\n `hello_custom`. Click **Continue**.\n\n5. On the **Training container** step, provide Vertex AI with\n information it needs to use the training package that you uploaded to\n Cloud Storage:\n\n 1. Select **Prebuilt container**.\n\n 2. In the **Model framework** drop-down list, select **TensorFlow**.\n\n 3. In the **Model framework version** drop-down list, select **2.3**.\n\n 4. In the **Package location** field, enter\n `cloud-samples-data/ai-platform/hello-custom/hello-custom-sample-v1.tar.gz`.\n\n 5. In the **Python module** field, enter `trainer.task`. `trainer` is the\n name of the Python package in your tarball, and `task.py` contains your\n training code. Therefore, `trainer.task` is the name of the module that\n you want Vertex AI to run.\n\n 6. In the **Model output directory** field, click **Browse** . Do the\n following in the **Select folder** pane:\n\n 1. Navigate to your Cloud Storage bucket.\n\n 2. Click **Create new folder create_new_folder**.\n\n 3. Name the new folder `output`. Then click **Create**.\n\n 4. Click **Select**.\n\n Confirm that field has the value\n `gs://`\u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e`/output`, where \u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e\n is the name of your Cloud Storage bucket.\n\n This value gets passed to Vertex AI in the\n [`baseOutputDirectory` API\n field](/vertex-ai/docs/reference/rest/v1/CustomJobSpec#FIELDS.base_output_directory), which sets\n several environment variables that your training application can access\n when it runs.\n\n For example, when you set this field to\n `gs://`\u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e`/output`, Vertex AI sets\n the `AIP_MODEL_DIR` environment variable to\n `gs://`\u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e`/output/model`. At the end of training,\n Vertex AI uses any artifacts in the `AIP_MODEL_DIR` directory\n to create a model resource.\n\n Learn more about the [environment variables set by this\n field](/vertex-ai/docs/training/code-requirements#environment-variables).\n\n Click **Continue**.\n6. On the optional **Hyperparameters** step, make sure that the **Enable\n hyperparameter tuning** checkbox is cleared. This tutorial does not use\n hyperparameter tuning. Click **Continue**.\n\n7. On the **Compute and pricing** step, allocate resources for the custom\n training job:\n\n 1. In the **Region** drop-down list, select **us-central1 (Iowa)**.\n\n 2. In the **Machine type** drop-down list, select **n1-standard-4** from the\n **Standard** section.\n\n Do not add any accelerators or worker pools for this tutorial. Click\n **Continue**.\n8. On the **Prediction container** step, provide Vertex AI with\n information it needs to serve predictions:\n\n 1. Select **Prebuilt container**.\n\n 2. In the **Prebuilt container settings** section, do the following:\n\n 1. In the **Model framework** drop-down list, select **TensorFlow**.\n\n 2. In the **Model framework version** drop-down list, select **2.3**.\n\n 3. In the **Accelerator type** drop-down list, select **None**.\n\n 4. Confirm that **Model directory** field has the value\n `gs://`\u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e`/output`, where\n \u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e is the name of your Cloud Storage\n bucket. This matches the **Model output directory** value that you\n provided in a previous step.\n\n 3. Leave the fields in the **Predict schemata** section blank.\n\n9. Click **Start training** to start the custom training pipeline.\n\nYou can now view your new *training pipeline* , which is named `hello_custom`, on\nthe **Training** page. (You might need to refresh the page.) The training\npipeline does two main things:\n\n1. The training pipeline creates a *custom job* resource named\n `hello_custom-custom-job`. After a few moments, you can view this resource\n on the **Custom jobs** page of the **Training** section:\n\n [Go to Custom jobs](https://console.cloud.google.com/vertex-ai/training/custom-jobs)\n\n The custom job runs the training application using the computing resources\n that you specified in this section.\n2. After the custom job completes, the training pipeline finds the artifacts\n that your training application creates in the `output/model/` directory of\n your Cloud Storage bucket. It uses these artifacts to create\n a *model* resource.\n\n### Monitor training\n\nTo view training logs, do the following:\n\n1. In the Google Cloud console, in the Vertex AI section, go to\n the **Custom jobs** page.\n\n [Go to Custom jobs](https://console.cloud.google.com/vertex-ai/training/custom-jobs)\n2. To view details for the `CustomJob` that you just created, click\n `hello_custom-custom-job` in the list.\n\n3. On the job details page, click **View logs**.\n\n### View your trained model\n\nWhen the custom training pipeline completes, you can find the trained model in\nthe Google Cloud console, in the Vertex AI section, on the\n**Models** page.\n\n[Go to Models](https://console.cloud.google.com/vertex-ai/models)\n\nThe model has the name `hello_custom`.\n\nWhat's next\n-----------\n\nFollow the [next page of this tutorial](/vertex-ai/docs/tutorials/image-classification-custom/serving) to serve\npredictions from your trained ML model."]]
