Organiza tus páginas con colecciones
Guarda y categoriza el contenido según tus preferencias.
En este instructivo, se muestra cómo entrenar el modelo ResNet-50 en un dispositivo Cloud TPU con PyTorch. Puedes aplicar el mismo patrón a otros modelos de clasificación de imágenes optimizados con TPU que usen PyTorch y el conjunto de datos ImageNet.
En este documento, usarás los siguientes componentes facturables de Google Cloud:
Compute Engine
Cloud TPU
Para generar una estimación de costos en función del uso previsto, usa la calculadora de precios.
Es posible que los usuarios de Google Cloud nuevos cumplan con los requisitos para acceder a una prueba gratuita.
Antes de comenzar
Antes de comenzar este instructivo, verifica que tu proyecto Google Cloud esté configurado correctamente.
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 $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
En esta explicación, se usan componentes facturables de Google Cloud. Consulta la página de precios de Cloud TPU para calcular los costos. Asegúrate de limpiar los recursos que creaste cuando hayas terminado de usarlos para evitar cargos innecesarios.
Para evitar que se apliquen cargos a tu cuenta de Google Cloud por los recursos usados en este instructivo, borra el proyecto que contiene los recursos o conserva el proyecto y borra los recursos individuales.
Desconéctate de la VM de TPU:
(vm)$exit
El mensaje ahora debería mostrar username@projectname, que indica que estás en Cloud Shell.
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-09-04 (UTC)"],[],[],null,["# Training Resnet50 on Cloud TPU with PyTorch\n\n*** ** * ** ***\n\nThis tutorial shows you how to train the ResNet-50 model\non a Cloud TPU device with PyTorch. You can apply the same pattern to\nother TPU-optimised image classification models that use PyTorch and the\nImageNet dataset.\n\nThe model in this tutorial is based on [Deep Residual Learning for Image\nRecognition](https://arxiv.org/pdf/1512.03385.pdf), which first introduces\nthe residual network (ResNet) architecture. The tutorial uses the 50-layer\nvariant, ResNet-50, and demonstrates training the model using\n[PyTorch/XLA](https://github.com/pytorch/xla).\n| **Warning:** This tutorial uses a third-party dataset. Google provides no representation, warranty, or other guarantees about the validity, or any other aspects of this dataset.\n\n\nObjectives\n----------\n\n- Prepare the dataset.\n- Run the training job.\n- Verify the output results.\n\n\nCosts\n-----\n\n\nIn this document, you use the following billable components of Google Cloud:\n\n\n- Compute Engine\n- Cloud TPU\n\n\nTo generate a cost estimate based on your projected usage,\nuse the [pricing calculator](/products/calculator). \nNew Google Cloud users might be eligible for a [free trial](/free). \n\n\u003cbr /\u003e\n\n\nBefore you begin\n----------------\n\nBefore starting this tutorial, check that your Google Cloud project is correctly\nset up.\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- 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\n1. This walkthrough uses billable components of Google Cloud. Check the [Cloud TPU pricing page](/tpu/docs/pricing) to estimate your costs. Be sure to clean up resources you created when you've finished with them to avoid unnecessary charges.\n\n\nCreate a TPU VM\n---------------\n\n1. Open a Cloud Shell window.\n\n [Open Cloud Shell](https://console.cloud.google.com/?cloudshell=true)\n2. Create a TPU VM\n\n ```bash\n gcloud compute tpus tpu-vm create your-tpu-name \\\n --accelerator-type=v3-8 \\\n --version=tpu-ubuntu2204-base \\\n --zone=us-central1-a \\\n --project=your-project\n ```\n | **Note:** The first time you run a command in a new Cloud Shell VM, an `Authorize Cloud Shell` page is displayed. Click `Authorize` at the bottom of the page to allow `gcloud` to make Google Cloud API calls with your credentials.\n3. Connect to your TPU VM using SSH:\n\n ```bash\n gcloud compute tpus tpu-vm ssh your-tpu-name --zone=us-central1-a\n ```\n4. Install PyTorch/XLA on your TPU VM:\n\n ```bash\n (vm)$ pip install torch torch_xla[tpu] torchvision -f https://storage.googleapis.com/libtpu-releases/index.html -f https://storage.googleapis.com/libtpu-wheels/index.html\n ```\n5. Clone the [PyTorch/XLA GitHub repo](https://github.com/pytorch/xla)\n\n ```bash\n (vm)$ git clone --depth=1 https://github.com/pytorch/xla.git\n ```\n6. Run the training script with fake data\n\n ```bash\n (vm) $ PJRT_DEVICE=TPU python3 xla/test/test_train_mp_imagenet.py --fake_data --batch_size=256 --num_epochs=1\n ```\n\nClean up\n--------\n\n\nTo avoid incurring charges to your Google Cloud account for the resources used in this\ntutorial, either delete the project that contains the resources, or keep the project and\ndelete the individual resources.\n\n1. Disconnect from the TPU VM:\n\n ```bash\n (vm) $ exit\n ```\n\n Your prompt should now be `username@projectname`, showing you\n are in the Cloud Shell.\n2. Delete your TPU VM.\n\n ```bash\n $ gcloud compute tpus tpu-vm delete your-tpu-name \\\n --zone=us-central1-a\n ```\n\n\nWhat's next\n-----------\n\n- [Training diffusion models with Pytorch](/tpu/docs/tutorials/diffusion-pytorch)\n- [Troubleshooting Pytorch on TPUs](/tpu/docs/troubleshooting/trouble-pytorch)\n- [Pytorch/XLA documentation](https://pytorch.org/xla/)"]]
