在本教程中，您将使用 Google Cloud 控制台和 Cloud Shell 与 Google Cloud进行互动。或者，您也可以使用其他安装了 Google Cloud CLI 的 Bash Shell（而不是 Cloud Shell）。
Sign in to your Google Cloud account. If you're new to
Google Cloud,
create an account to evaluate how our products perform in
real-world scenarios. New customers also get $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
At the bottom of the Google Cloud console, a
Cloud Shell
session starts and displays a command-line prompt. Cloud Shell is a shell environment
with the Google Cloud CLI
already installed and with values already set for
your current project. It can take a few seconds for the session to initialize.
[[["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。"],[],[],null,["# Hello custom training: Set up your project and environment\n\nThis page walks through setting up your Google Cloud project to use\nVertex AI and downloading some TensorFlow code for training. You will\nalso download code for a web app that gets predictions.\nThis tutorial has several pages:\n\n\u003cbr /\u003e\n\n1. Setting up your project and environment.\n\n2. [Training a custom image classification\n model.](/vertex-ai/docs/tutorials/image-classification-custom/training)\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.\n\nBefore you begin\n----------------\n\nThroughout this tutorial, use Google Cloud console and\n[Cloud Shell](/shell/docs) to interact with Google Cloud. Alternatively,\ninstead of Cloud Shell, you\ncan use another Bash shell with the [Google Cloud CLI](/sdk/docs) installed.\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Vertex AI and Cloud Run functions APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,cloudfunctions)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Vertex AI and Cloud Run functions APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,cloudfunctions)\n\n1. In the Google Cloud console, activate Cloud Shell.\n\n [Activate Cloud Shell](https://console.cloud.google.com/?cloudshell=true)\n\n\n At the bottom of the Google Cloud console, a\n [Cloud Shell](/shell/docs/how-cloud-shell-works)\n session starts and displays a command-line prompt. Cloud Shell is a shell environment\n with the Google Cloud CLI\n already installed and with values already set for\n your current project. It can take a few seconds for the session to initialize.\n2. If Cloud Shell does not display\n `(`\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e`)$`\n in its prompt (where \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e is replaced by your\n Google Cloud project ID), then run the following command to\n configure Cloud Shell to use your project:\n\n gcloud config set project \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e\n\nCreate a Cloud Storage bucket\n-----------------------------\n\nCreate a regional [Cloud Storage](/storage/docs) bucket in the `us-central1`\nregion to use for the rest of this tutorial. As you follow the tutorial, use the\nbucket for several purposes:\n\n- Store training code for Vertex AI to use in a custom training job.\n- Store the model artifacts that your custom training job outputs.\n- Host the web app that gets predictions from your Vertex AI endpoint.\n\nTo create the Cloud Storage bucket, run the following command in your\nCloud Shell session: \n\n gcloud storage buckets create gs://\u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e --project=\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e --location=us-central1\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: The ID of your Google Cloud project.\n- \u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e: A name that you choose for your bucket. For example, `hello_custom_`\u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e. Learn about [requirements for bucket\n names](/storage/docs/buckets#naming).\n\nDownload sample code\n--------------------\n\nDownload sample code to use for the rest of the tutorial. \n\n gcloud storage cp gs://cloud-samples-data/ai-platform/hello-custom/hello-custom-sample-v1.tar.gz - | tar -xzv\n\nTo optionally view the sample code files, run the following command: \n\n ls -lpR hello-custom-sample\n\nThe `hello-custom-sample` directory has four items:\n\n- `trainer/`: A directory of TensorFlow Keras code for training the flower\n classification model.\n\n- `setup.py`: A configuration file for packaging the `trainer/` directory into\n a Python source distribution that Vertex AI can use.\n\n- `function/`: A directory of Python code for a\n [Cloud Run function](/functions/docs) that can receive and preprocess\n prediction requests from a web browser, send them to Vertex AI,\n process the prediction responses, and send them back to the browser.\n\n- `webapp/`: A directory with code and markup for a web app that gets flower\n classification predictions from Vertex AI.\n\nWhat's next\n-----------\n\nFollow the [next page of this tutorial](/vertex-ai/docs/tutorials/image-classification-custom/training) to run a custom\ntraining job on Vertex AI."]]
