Crea una instancia de VM de aprendizaje profundo de TensorFlow
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En esta página, se muestra cómo crear una instancia de Deep Learning VM Image de TensorFlow con TensorFlow y otras herramientas preinstaladas. Puedes crear una instancia de TensorFlow desde Cloud Marketplace dentro de la consola de Google Cloud o mediante la línea de comandos.
Antes de comenzar
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.
Si usas GPU con la VM de aprendizaje profundo, revisa la página de cuotas para asegurarte de tener suficientes GPU disponibles en el proyecto. Si las GPU no están enumeradas en la página de las cuotas o necesitas una cuota de GPU adicional, solicita un aumento de la cuota.
Crea una instancia de VM de aprendizaje profundo de TensorFlow desde Cloud Marketplace
Para crear una instancia de VM de aprendizaje profundo de TensorFlow desde Cloud Marketplace, completa los siguientes pasos:
Ve a la página Deep Learning VM de Cloud Marketplace en la Google Cloud consola.
En GPU, selecciona el Tipo de GPU y la Cantidad de GPU.
Si no deseas usar las GPU, haz clic en el botón Borrar GPU y avanza al paso 7. Obtén más información sobre GPU.
En Framework, selecciona una de las versiones del framework de TensorFlow.
Si usas GPU, se requiere un controlador NVIDIA.
Puedes instalar el controlador tú mismo o seleccionar Instalar automáticamente el controlador de GPU de NVIDIA en el primer inicio.
Puedes seleccionar Habilitar el acceso a JupyterLab mediante una URL en lugar de SSH (Beta). Habilitar esta función Beta te permite acceder a tu instancia de JupyterLab mediante una URL. Cualquier persona que tenga el rol de editor o propietario en tu proyecto deGoogle Cloud puede acceder a esta URL.
En la actualidad, esta característica solo funciona en Estados Unidos, la Unión Europea y Asia.
Selecciona un tipo de disco de arranque y su tamaño.
Selecciona la configuración de red que desees.
Haga clic en Implementar.
Si eliges instalar los controladores NVIDIA, espera de 3 a 5 minutos para que se complete la instalación.
Después de implementar la VM, la página se actualiza con instrucciones para que puedas acceder a la instancia.
Crea una instancia de Deep Learning VM de TensorFlow desde la línea de comandos
Para usar Google Cloud CLI a fin de crear una nueva instancia de VM de aprendizaje profundo, primero debes instalar y, luego, inicializar Google Cloud CLI:
Descarga y, luego, instala Google Cloud CLI en función de las instrucciones que se indican en Instala Google Cloud CLI.
Un nombre anterior de familia de imágenes de TensorFlow o TensorFlow Enterprise (consulta la sección sobre cómo elegir una imagen)
--image-project debe ser deeplearning-platform-release
Con una o más GPU
Compute Engine ofrece la opción de agregar una o más GPU a tus instancias de máquina virtual. Las GPU ofrecen un procesamiento más rápido para muchas tareas complejas de datos y aprendizaje automático. Para obtener más información sobre las GPU, consulta GPU en Compute Engine.
Si deseas aprovisionar una instancia de VM de aprendizaje profundo con una o más GPU, sigue estos pasos:
Un nombre anterior de familia de imágenes de TensorFlow o TensorFlow Enterprise (consulta la sección sobre cómo elegir una imagen)
--image-project debe ser deeplearning-platform-release
--maintenance-policy debe ser TERMINATE Para obtener más información, consulta Restricciones de GPU.
--accelerator especifica el tipo de GPU que se usará. Debe especificarse con el formato --accelerator="type=TYPE,count=COUNT".
Por ejemplo, --accelerator="type=nvidia-tesla-p100,count=2".
Consulta la tabla de modelos de GPU para obtener una lista de los tipos y recuentos de GPU disponibles.
--metadata se usa para especificar que el controlador NVIDIA se debe instalar en tu nombre. El valor es install-nvidia-driver=True.
Si se especifica, Compute Engine carga el controlador estable más reciente en el primer arranque y realiza los pasos necesarios (incluido un reinicio final para activar el controlador).
Si elegiste instalar los controladores NVIDIA, espera de 3 a 5 minutos para que se complete la instalación.
La VM puede demorar hasta 5 minutos en terminar de aprovisionarse. En este momento, no podrás establecer una conexión SSH en tu máquina. Cuando se complete la instalación, puedes establecer una conexión SSH y ejecutar nvidia-smi para verificar que la instalación del controlador se haya realizado de forma correcta.
Cuando hayas configurado la imagen, puedes guardar una instantánea para poder iniciar instancias derivadas sin tener que esperar la instalación del controlador.
Puedes crear una instancia de VM de aprendizaje profundo interrumpible. Una instancia interrumpible es una que puedes crear y ejecutar a un precio mucho menor que las instancias normales. Sin embargo, Compute Engine podría detener (interrumpir) estas instancias si requiere acceso a los recursos para otras tareas.
Las instancias interrumpibles siempre se detienen después de 24 horas. Para obtener más información acerca de las instancias interrumpibles, consulta Instancias de VM interrumpibles.
Si deseas crear una instancia de VM de aprendizaje profundo interrumpible, sigue estos pasos:
Sigue las instrucciones que se encuentran arriba para crear una instancia nueva mediante la línea de comandos. Agrega lo siguiente al comando gcloud compute instances create:
--preemptible
¿Qué sigue?
Si deseas obtener instrucciones para conectarte a tu nueva instancia de VM de aprendizaje profundo a través de la consola de Google Cloud o la línea de comandos, consulta Conéctate a instancias. El nombre de tu instancia es el nombre de la implementación que especificaste con -vm anexado.
[[["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)"],[[["\u003cp\u003eThis page guides users on creating a TensorFlow Deep Learning VM instance with pre-installed TensorFlow and other tools, available through the Google Cloud Marketplace or the command line.\u003c/p\u003e\n"],["\u003cp\u003eUsers can customize their VM instance by selecting machine type, zone, GPU type and number, TensorFlow framework version, boot disk specifications, and networking settings.\u003c/p\u003e\n"],["\u003cp\u003eNVIDIA GPU drivers can be automatically installed upon the first startup if using GPUs, or it can be done manually, though automatic installation will require a few minutes to complete.\u003c/p\u003e\n"],["\u003cp\u003eThe command line method offers flexibility to create instances with or without GPUs and specifies required parameters such as image family, zone, and project, with specific options for GPU and driver setup.\u003c/p\u003e\n"],["\u003cp\u003eUsers have the option to create preemptible instances, which are more cost-effective but subject to termination by Compute Engine, by using the \u003ccode\u003e--preemptible\u003c/code\u003e command in the creation process.\u003c/p\u003e\n"]]],[],null,["# Create a TensorFlow Deep Learning VM instance\n\nThis page shows you how to create\na TensorFlow Deep Learning VM Images instance\nwith TensorFlow and other tools pre-installed. You can create\na TensorFlow instance from Cloud Marketplace within\nthe Google Cloud console or using the command line.\n\nBefore you begin\n----------------\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. If you are using GPUs with your Deep Learning VM, check the [quotas page](https://console.cloud.google.com/quotas) to ensure that you have enough GPUs available in your project. If GPUs are not listed on the quotas page or you require additional GPU quota, [request a\n quota increase](/compute/quotas#requesting_additional_quota).\n\nCreating a TensorFlow Deep Learning VM instance from the Cloud Marketplace\n--------------------------------------------------------------------------\n\nTo create a TensorFlow Deep Learning VM instance\nfrom the Cloud Marketplace, complete the following steps:\n\n1. Go to the Deep Learning VM Cloud Marketplace page in\n the Google Cloud console.\n\n [Go to the Deep Learning VM Cloud Marketplace page](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning)\n2. Click **Get started**.\n\n3. Enter a **Deployment name** , which will be the root of your VM name.\n Compute Engine appends `-vm` to this name when naming your instance.\n\n4. Select a **Zone**.\n\n5. Under **Machine type** , select the specifications that you\n want for your VM.\n [Learn more about machine types.](/compute/docs/machine-types)\n\n6. Under **GPUs** , select the **GPU type** and **Number of GPUs** .\n If you don't want to use GPUs,\n click the **Delete GPU** button\n and skip to step 7. [Learn more about GPUs.](/gpu)\n\n 1. Select a **GPU type** . Not all GPU types are available in all zones. [Find a combination that is supported.](/compute/docs/gpus)\n 2. Select the **Number of GPUs** . Each GPU supports different numbers of GPUs. [Find a combination that is supported.](/compute/docs/gpus)\n7. Under **Framework**, select one of the TensorFlow\n framework versions.\n\n8. If you're using GPUs, an NVIDIA driver is required.\n You can install the driver\n yourself, or select **Install NVIDIA GPU driver automatically\n on first startup**.\n\n9. You have the option to select **Enable access to JupyterLab via URL\n instead of SSH (Beta)**. Enabling this Beta feature lets you\n access your JupyterLab\n instance using a URL. Anyone who is in the Editor or Owner role in your\n Google Cloud project can access this URL.\n Currently, this feature only works in\n the United States, the European Union, and Asia.\n\n10. Select a boot disk type and boot disk size.\n\n11. Select the networking settings that you want.\n\n12. Click **Deploy**.\n\nIf you choose to install NVIDIA drivers, allow 3-5 minutes for installation\nto complete.\n\nAfter the VM is deployed, the page updates with instructions for\naccessing the instance.\n\nCreating a TensorFlow Deep Learning VM instance from the command line\n---------------------------------------------------------------------\n\nTo use the Google Cloud CLI to create\na new Deep Learning VM instance,\nyou must first install and initialize the [Google Cloud CLI](/sdk/docs):\n\n1. Download and install the Google Cloud CLI using the instructions given on [Installing Google Cloud CLI](/sdk/downloads).\n2. Initialize the SDK using the instructions given on [Initializing Cloud\n SDK](/sdk/docs/initializing).\n\nTo use `gcloud` in Cloud Shell, first activate Cloud Shell using the\ninstructions given on [Starting Cloud Shell](/shell/docs/starting-cloud-shell).\n\nYou can create a TensorFlow instance with or without GPUs. \n\n### Without GPUs\n\nTo provision a Deep Learning VM instance without a GPU: \n\n export IMAGE_FAMILY=\"tf-ent-latest-cpu\"\n export ZONE=\"us-west1-b\"\n export INSTANCE_NAME=\"my-instance\"\n\n gcloud compute instances create $INSTANCE_NAME \\\n --zone=$ZONE \\\n --image-family=$IMAGE_FAMILY \\\n --image-project=deeplearning-platform-release\n\nOptions:\n\n- `--image-family` must be one of the following:\n - `tf-ent-latest-cpu` to get the latest [TensorFlow Enterprise](/tensorflow-enterprise/docs) 2 image\n - An earlier TensorFlow or TensorFlow Enterprise image family name (see [Choosing an image](/deep-learning-vm/docs/images))\n- `--image-project` must be `deeplearning-platform-release`.\n\n### With one or more GPUs\n\nCompute Engine offers the option of adding one or more\nGPUs to your virtual machine instances. GPUs offer faster processing\nfor many complex data and machine learning tasks. To learn more about\nGPUs, see [GPUs on Compute Engine](/compute/docs/gpus).\n\nTo provision a Deep Learning VM instance with one or more GPUs: \n\n export IMAGE_FAMILY=\"tf-ent-latest-gpu\"\n export ZONE=\"us-west1-b\"\n export INSTANCE_NAME=\"my-instance\"\n\n gcloud compute instances create $INSTANCE_NAME \\\n --zone=$ZONE \\\n --image-family=$IMAGE_FAMILY \\\n --image-project=deeplearning-platform-release \\\n --maintenance-policy=TERMINATE \\\n --accelerator=\"type=nvidia-tesla-v100,count=1\" \\\n --metadata=\"install-nvidia-driver=True\"\n\nOptions:\n\n- `--image-family` must be one of the following:\n\n - `tf-ent-latest-gpu` to get the latest [TensorFlow Enterprise](/tensorflow-enterprise/docs) 2 image\n - An earlier TensorFlow or TensorFlow Enterprise image family name (see [Choosing an image](/deep-learning-vm/docs/images))\n- `--image-project` must be `deeplearning-platform-release`.\n\n- `--maintenance-policy` must be `TERMINATE`. To learn more, see\n [GPU Restrictions](/compute/docs/gpus#restrictions).\n\n- `--accelerator` specifies the GPU type to use. Must be\n specified in the format\n `--accelerator=\"type=`\u003cvar translate=\"no\"\u003eTYPE\u003c/var\u003e`,count=`\u003cvar translate=\"no\"\u003eCOUNT\u003c/var\u003e`\"`.\n For example, `--accelerator=\"type=nvidia-tesla-p100,count=2\"`.\n See the [GPU models\n table](/compute/docs/gpus#other_available_nvidia_gpu_models)\n for a list of available GPU types and counts.\n\n Not all GPU types are supported in all regions. For details, see\n [GPU regions and zones availability](/compute/docs/gpus/gpu-regions-zones).\n- `--metadata` is used to specify that the NVIDIA driver should\n be installed on your behalf. The value is `install-nvidia-driver=True`.\n If specified, Compute Engine loads the latest stable\n driver on the first boot and performs the necessary steps (including\n a final reboot to activate the driver).\n\nIf you've elected to install NVIDIA drivers, allow 3-5 minutes\nfor installation to complete.\n\nIt may take up to 5 minutes before your VM is fully provisioned. In this\ntime, you will be unable to SSH into your machine. When the installation is\ncomplete, to guarantee that the driver installation was successful, you can\nSSH in and run `nvidia-smi`.\n\nWhen you've configured your image, you can save a snapshot of your\nimage so that you can start derivitave instances without having to wait\nfor the driver installation.\n\n### About TensorFlow Enterprise\n\n[TensorFlow Enterprise](/tensorflow-enterprise/docs) is a\ndistribution of\n[TensorFlow](https://www.tensorflow.org/)\nthat has been optimized to run on Google Cloud and includes\n[Long Term Version\nSupport](/tensorflow-enterprise/docs/overview#long_term_version_support).\n\nCreating a preemptible instance\n-------------------------------\n\nYou can create a preemptible Deep Learning VM instance. A preemptible\ninstance is an instance you can create and run at a much lower price than\nnormal instances. However, Compute Engine might stop (preempt) these\ninstances if it requires access to those resources for other tasks.\nPreemptible instances always stop after 24 hours. To learn more about\npreemptible instances, see [Preemptible VM\nInstances](/compute/docs/instances/preemptible).\n\nTo create a preemptible Deep Learning VM instance:\n\n- Follow the instructions located above to create a new instance using the\n command line. To the `gcloud compute instances create` command, append the\n following:\n\n ```\n --preemptible\n ```\n\nWhat's next\n-----------\n\nFor instructions on connecting to your new Deep Learning VM instance\nthrough the Google Cloud console or command line, see [Connecting to\nInstances](/compute/docs/instances/connecting-to-instance). Your instance name\nis the **Deployment name** you specified with `-vm` appended."]]
