Introduzione all'addestramento personalizzato: pulizia del progetto
Mantieni tutto organizzato con le raccolte
Salva e classifica i contenuti in base alle tue preferenze.
Questa pagina ti guida nella pulizia delle risorse Google Cloud che hai
creato per addestrare il modello di classificazione delle immagini e fornire previsioni.
Ogni pagina presuppone che tu abbia già eseguito le istruzioni delle pagine precedenti del tutorial.
Il resto di questo documento presuppone che tu stia utilizzando lo stesso ambiente Cloud Shell creato seguendo la prima pagina di questo tutorial. Se la sessione Cloud Shell originale non è più aperta, puoi tornare all'ambiente nel seguente modo:
In the Google Cloud console, activate Cloud Shell.
Nella sessione di Cloud Shell, esegui questo comando:
cdhello-custom-sample
Elimina risorse Vertex AI
Questa sezione descrive come eliminare tutte le risorse Vertex AI
che hai creato per questo tutorial.
Annulla il deployment del modello dall'endpoint
Questa sezione descrive come annullare il deployment del modello dall'endpoint. Puoi
considerare questa azione come un modo per disconnettere il modello dall'endpoint.
Trova la riga del tuo modello, hello_custom. In quella riga, fai clic su Mostra
altro more_vert. Poi,
fai clic su Elimina modello.
Nella finestra di dialogo Elimina modello, fai clic su Elimina.
Elimina la pipeline di addestramento e il job personalizzati
La pipeline di addestramento e il job personalizzato sono solo record dell'addestramento
eseguito in precedenza. Se vuoi eliminare il tuo job personalizzato:
Nella console Google Cloud , nella sezione Vertex AI, vai alla pagina Pipeline di addestramento.
Trova la riga della pipeline di addestramento, hello_custom. In quella riga,
fai clic su Mostra altro more_vert. Quindi, fai clic su Elimina pipeline di
addestramento.
Nella finestra di dialogo Elimina job di addestramento, fai clic su Elimina.
Per andare alla pagina Job personalizzati, fai clic su Job personalizzato nella
consoleGoogle Cloud o fai clic sul seguente link:
Trova la riga del tuo job personalizzato, hello_custom-custom-job. In quella riga,
fai clic su Mostra altro more_vert. Quindi, fai clic su Elimina lavoro personalizzato.
Nella finestra di dialogo Elimina job di addestramento, fai clic su Elimina.
Pulire la sessione Cloud Shell
Cloud Shell non comporta costi e elimina automaticamente il disco
home dopo un periodo di inattività. Tuttavia, se prevedi di utilizzare Cloud Shell per altri scopi nel prossimo futuro, ti consigliamo di rimuovere manualmente i file che hai creato per questo tutorial.
Nella sessione di Cloud Shell, esegui questi comandi:
cd..
rm-rfhello-custom-sample
Elimina il bucket Cloud Storage
Nella sessione di Cloud Shell, esegui questo comando:
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2025-09-04 UTC."],[],[],null,["# Hello custom training: Clean up your project\n\nThis page guides you through cleaning up the Google Cloud resources that you\ncreated to train your image classification model and serve predictions from it.\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\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.\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\nDelete Vertex AI resources\n--------------------------\n\nThis section describes how to delete all of the Vertex AI resources\nthat you created for this tutorial.\n\n### Undeploy your model from your endpoint\n\nThis section describes how to undeploy your model from your endpoint. You can\nthink about this action as a way of disconnecting your model from your endpoint.\n\nYou must follow this section before you can [delete your\nendpoint](#delete-endpoint) or [delete your model](#delete-model).\n\n1. In the Google Cloud console, in the Vertex AI section, go to\n the **Endpoints** page.\n\n [Go to Endpoints](https://console.cloud.google.com/vertex-ai/endpoints)\n2. Click `hello_custom` to go to the endpoint details page.\n\n3. On the row for your model, `hello_custom`, click **Undeploy model\n delete**.\n\n4. In the **Undeploy model from endpoint** dialog, click **Undeploy**.\n\n### Delete your endpoint\n\nBefore you delete an endpoint, you must [undeploy your model from your\nendpoint](#undeploy-model). After you've deleted your endpoint, you won't\nbe able to re-use that endpoint name for up to 7 days.\n\nAfter you've undeployed your model from the endpoint, do the following\nto delete your endpoint:\n\n1. In the Google Cloud console, in the Vertex AI section, go to\n the **Endpoints** page.\n\n [Go to Endpoints](https://console.cloud.google.com/vertex-ai/endpoints)\n2. Find your the row of your endpoint, `hello_custom`, again. On that row, click\n **View more more_vert** . Then click **Remove endpoint**.\n\n3. In the **Remove endpoint** dialog, click **Confirm**.\n\n### Delete your model\n\nBefore you follow this section, you must [undeploy your model from your\nendpoint](#undeploy-model). Afterward, do the following to delete your model:\n\n1. In the Google Cloud console, in the Vertex AI section, go to\n the **Models** page.\n\n [Go to Models](https://console.cloud.google.com/vertex-ai/models)\n2. Find your the row of your model, `hello_custom`. On that row, click **View\n more more_vert** . Then\n click **Delete model**.\n\n3. In the **Delete model** dialog, click **Delete**.\n\n### Delete your custom training pipeline and job\n\nYour training pipeline and custom job are just records of the training that\nhappened earlier. If you want to delete your custom job, do the following:\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. Find your the row of your training pipeline, `hello_custom`. On that row,\n click **View more more_vert** . Then click **Delete training\n pipeline**.\n\n3. In the **Delete training job** dialog, click **Delete**.\n\n4. To go to the **Custom jobs** page, click **Custom job** in the\n Google Cloud console, or click the following link:\n\n [Go to Custom jobs](https://console.cloud.google.com/vertex-ai/training/custom-jobs)\n5. Find your the row of your custom job, `hello_custom-custom-job`. On that row,\n click **View more more_vert** . Then click **Delete custom job**.\n\n6. In the **Delete training job** dialog, click **Delete**.\n\nClean up your Cloud Shell session\n---------------------------------\n\nCloud Shell incurs no charges, and it [automatically deletes your home\ndisk after a period of inactivity](/shell/docs/limitations). However, if you\nplan to use Cloud Shell for other purposes in the near future, you\nmight want to manually remove the files that you created for this tutorial.\n\nIn your Cloud Shell session, run the following commands: \n\n cd ..\n rm -rf hello-custom-sample\n\nDelete your Cloud Storage bucket\n--------------------------------\n\nIn your Cloud Shell session, run the following command: \n\n gcloud storage rm gs://\u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e --recursive --continue-on-error\n\nReplace \u003cvar translate=\"no\"\u003eBUCKET_NAME\u003c/var\u003e with the name of the Cloud Storage\nbucket that you created when reading the [first page of this\ntutorial](/vertex-ai/docs/tutorials/image-classification-custom).\n\nDelete your Cloud Run function\n------------------------------\n\nIn your Cloud Shell session, run the following command: \n\n gcloud functions delete classify_flower --region=us-central1 --quiet\n\nWhat's next\n-----------\n\n- To learn about additional ways to train ML models on Vertex AI,\n try one of the other [Vertex AI tutorials](/vertex-ai/docs/tutorials).\n\n- Read an [overview of how Vertex AI\n works](/vertex-ai/docs/start/introduction-unified-platform)."]]
