[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-09-04。"],[[["\u003cp\u003eOnline Prediction is a Preview feature not recommended for production use, with no SLAs or technical support commitments from Google.\u003c/p\u003e\n"],["\u003cp\u003eGoogle Distributed Cloud (GDC) air-gapped provides prebuilt containers for serving online predictions from models trained using TensorFlow or PyTorch.\u003c/p\u003e\n"],["\u003cp\u003eTo use prebuilt containers, models must be saved as compliant model artifacts, and the process requires setting up a project, creating a prediction cluster, and establishing a storage bucket.\u003c/p\u003e\n"],["\u003cp\u003eDepending on whether you use TensorFlow or PyTorch, model artifacts must be exported in different formats, such as a TensorFlow SavedModel directory or a PyTorch archive file named \u003ccode\u003emodel.mar\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eModels must be uploaded to a designated storage bucket with a specific structure: \u003ccode\u003es3://<BUCKET_NAME>/<MODEL_ID>/<MODEL_VERSION_ID>\u003c/code\u003e.\u003c/p\u003e\n"]]],[],null,["# Export model artifacts for prediction\n\n| **Preview:** Online Prediction is a Preview feature that is available as-is and is not recommended for production environments. Google provides no service-level agreements (SLA) or technical support commitments for Preview features. For more information, see GDC's [feature stages](/distributed-cloud/hosted/docs/latest/gdch/resources/feature-stages).\n\nGoogle Distributed Cloud (GDC) air-gapped offers\n[prebuilt containers](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-online-predictions#available-container-images)\nto serve online predictions from models trained using the following\nmachine learning (ML) frameworks:\n\n- TensorFlow\n- PyTorch\n\nTo use one of these prebuilt containers, you must save your model as one or\nmore *model artifacts* that comply with the requirements of the prebuilt\ncontainer. These requirements apply whether or not your model artifacts are\ncreated on Distributed Cloud.\n\nBefore you begin\n----------------\n\nBefore exporting model artifacts, perform the following steps:\n\n1. Create and train a prediction model targeting one of the [supported containers](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-online-predictions#available-container-images).\n2. If you don't have a project, [set up a project for Vertex AI](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-set-up-project).\n3. Work with your Infrastructure Operator (IO) to\n [create the prediction cluster](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/prediction-user-cluster).\n\n The IO creates the cluster for you, associates it with your project, and\n assigns the appropriate node pools within the cluster, considering the\n resources you need for online predictions.\n4. [Create a storage bucket for your project](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/create-storage-buckets).\n\n5. Create the Vertex AI Default Serving\n (`vai-default-serving-sa`) service account within your project. For\n information about service accounts, see\n [Set up service accounts](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-set-up-project#set-up-service).\n\n6. Grant the Project Bucket Object Viewer (`project-bucket-object-viewer`) role\n to the Vertex AI Default Serving (`vai-default-serving-sa`)\n service account for the storage bucket you created. For information\n about granting bucket access to service accounts, see\n [Grant bucket access](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/grant-obtain-storage-access#grant_bucket_access).\n\n7. To get the permissions that you need to access Online Prediction,\n ask your Project IAM Admin to grant you the Vertex AI\n Prediction User (`vertex-ai-prediction-user`) role. For information about\n this role, see [Prepare IAM permissions](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-ao-permissions).\n\nFramework-specific requirements for exporting to prebuilt containers\n--------------------------------------------------------------------\n\nDepending on\n[the ML framework you plan to use for prediction](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-online-predictions#available-container-images),\nyou must export model artifacts in different formats. The following sections\ndescribe the acceptable model formats for each ML framework.\n| **Important:** To access the URLs listed on this page, you must connect to the internet. The URLs are provided to access outside of your air-gapped environment.\n\n### TensorFlow\n\nIf you [use TensorFlow to train a model](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-online-predictions#tf),\nexport your model as a [TensorFlow SavedModel directory](https://www.tensorflow.org/guide/saved_model).\n\nThere are several ways to export `SavedModels` from TensorFlow training\ncode. The following list describes a few ways that work for various\nTensorFlow APIs:\n\n- If you use Keras for training,\n [use `tf.keras.Model.save` to export a SavedModel](https://www.tensorflow.org/guide/keras/save_and_serialize#whole-model_saving_loading).\n\n- If you use an Estimator for training,\n [use `tf.estimator.Estimator.export_saved_model` to export a SavedModel](https://www.tensorflow.org/guide/estimator#savedmodels_from_estimators).\n\n- Otherwise,\n [use `tf.saved_model.save`](https://www.tensorflow.org/guide/saved_model#saving_a_custom_model)\n or\n [use `tf.compat.v1.saved_model.SavedModelBuilder`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/builder).\n\nIf you are not using Keras or an Estimator, then make sure to\n[use the `serve` tag and `serving_default` signature when you export your SavedModel](https://www.tensorflow.org/tfx/serving/serving_basic#train_and_export_tensorflow_model)\nto ensure Vertex AI can use your model artifacts to serve\npredictions. Keras and Estimator handle this task automatically.\nLearn more about\n[specifying signatures during export](https://www.tensorflow.org/guide/saved_model#specifying_signatures_during_export).\n\nTo serve predictions using these artifacts, create a `Model` with the\n[prebuilt container for prediction](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-online-predictions#tf)\nmatching the version of TensorFlow that you used for training.\n\n### PyTorch\n\nIf you [use PyTorch to train a model](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-online-predictions#pt),\nyou must package the model artifacts including either a\n[default](https://pytorch.org/serve/#default-handlers) or\n[custom](https://pytorch.org/serve/custom_service.html)\nhandler by creating an archive file using\n[Torch model archiver](https://github.com/pytorch/serve/tree/master/model-archiver).\nThe prebuilt PyTorch images expect the archive to be named `model.mar`, so make\nsure you set the model name to *model*.\n\nFor information about optimizing the memory usage, latency, or throughput of a\nPyTorch model served with TorchServe, see the\n[PyTorch performance guide](https://github.com/pytorch/serve/blob/master/docs/performance_guide.md).\n\nUpload your model\n-----------------\n\nYou must upload your model to [the storage bucket you created](#storage-bucket).\nFor more information about uploading objects to storage buckets, see\n[Upload and download storage objects in projects](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/upload-download-storage-objects).\n\nThe path to the storage bucket of your model must have the following structure: \n\n s3://\u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e/\u003cvar translate=\"no\"\u003eMODEL_ID\u003c/var\u003e/\u003cvar translate=\"no\"\u003eMODEL_VERSION_ID\u003c/var\u003e\n\nFor export details, see the\n[framework-specific requirements for exporting to prebuilt containers](#framework-specific-requirements)."]]
