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Halaman ini menunjukkan cara membuat instance PyTorch Deep Learning VM Images dengan PyTorch dan alat lainnya yang telah diinstal sebelumnya. Anda dapat membuat instance PyTorch dari Cloud Marketplace dalam konsol Google Cloud atau menggunakan command line.
Sebelum memulai
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.
Jika Anda menggunakan GPU dengan Deep Learning VM, periksa
halaman kuota
untuk memastikan bahwa Anda memiliki
cukup GPU yang tersedia dalam project Anda. Jika GPU tidak tercantum di halaman kuota atau Anda memerlukan kuota GPU tambahan, minta penambahan kuota.
Membuat instance Deep Learning VM PyTorch dari Cloud Marketplace
Untuk membuat instance Deep Learning VM PyTorch
dari Cloud Marketplace, selesaikan langkah-langkah berikut:
Buka halaman Deep Learning VM Cloud Marketplace di konsol Google Cloud .
Di bagian GPU, pilih Jenis GPU dan Jumlah GPU.
Jika Anda tidak ingin menggunakan GPU,
klik tombol Hapus GPU
dan lanjutkan ke langkah 7. Pelajari GPU lebih lanjut.
Di bagian Framework, pilih PyTorch 1.8 + fast.ai 2.1
(CUDA 11.0).
Jika Anda menggunakan GPU, driver NVIDIA diperlukan.
Anda dapat menginstal driver sendiri, atau memilih Instal driver GPU NVIDIA secara otomatis saat mulai pertama kali.
Anda memiliki opsi untuk memilih Aktifkan akses ke JupyterLab melalui URL, bukan SSH (Beta). Dengan mengaktifkan fitur Beta ini, Anda dapat mengakses instance JupyterLab menggunakan URL. Siapa pun yang memiliki peran Editor atau Pemilik di projectGoogle Cloud Anda dapat mengakses URL ini.
Saat ini, fitur ini hanya berfungsi di Amerika Serikat, Uni Eropa, dan Asia.
Pilih jenis boot disk dan ukuran boot disk.
Pilih setelan jaringan yang Anda inginkan.
Klik Deploy.
Jika Anda memilih untuk menginstal driver NVIDIA, tunggu 3-5 menit hingga penginstalan selesai.
Setelah VM di-deploy, halaman akan diperbarui dengan petunjuk untuk
mengakses instance.
Membuat instance Deep Learning VM PyTorch dari command line
Untuk menggunakan Google Cloud CLI guna membuat instance Deep Learning VM baru, Anda harus menginstal dan menginisialisasi Google Cloud CLI terlebih dahulu:
--image-family harus berupa pytorch-latest-cpu
atau pytorch-VERSION-cpu
(misalnya, pytorch-1-13-cpu).
--image-project harus berupa deeplearning-platform-release.
Dengan satu atau beberapa GPU
Compute Engine menawarkan opsi untuk menambahkan satu atau beberapa GPU ke instance virtual machine Anda. GPU menawarkan pemrosesan yang lebih cepat untuk banyak tugas machine learning dan data yang kompleks. Untuk mempelajari GPU lebih lanjut, lihat GPU di Compute Engine.
Untuk membuat instance Deep Learning VM dengan kelompok image PyTorch terbaru dan satu atau beberapa GPU yang terpasang, masukkan perintah berikut di command line:
--image-family harus berupa pytorch-latest-gpu
atau pytorch-VERSION-CUDA-VERSION
(misalnya, pytorch-1-10-cu110).
--image-project harus berupa deeplearning-platform-release.
--maintenance-policy harus berupa TERMINATE. Untuk mempelajari lebih lanjut, lihat
Batasan GPU.
--accelerator menentukan jenis GPU yang akan digunakan. Harus
ditentukan dalam format
--accelerator="type=TYPE,count=COUNT".
Misalnya, --accelerator="type=nvidia-tesla-p100,count=2".
Lihat tabel model GPU untuk mengetahui daftar jenis dan jumlah GPU yang tersedia.
--metadata digunakan untuk menentukan bahwa driver NVIDIA harus diinstal
atas nama Anda. Nilainya adalah install-nvidia-driver=True. Jika ditentukan,
Compute Engine akan memuat driver stabil terbaru saat booting pertama
dan melakukan langkah-langkah yang diperlukan (termasuk reboot terakhir untuk mengaktifkan
driver).
Jika Anda memilih untuk menginstal driver NVIDIA, tunggu 3-5 menit hingga penginstalan selesai.
Mungkin perlu waktu hingga 5 menit sebelum VM Anda disediakan sepenuhnya. Selama
waktu ini, Anda tidak akan dapat melakukan SSH ke komputer Anda. Setelah penginstalan selesai, untuk memastikan bahwa penginstalan driver berhasil, Anda dapat login melalui SSH dan menjalankan nvidia-smi.
Setelah mengonfigurasi image, Anda dapat menyimpan snapshot image agar dapat memulai instance turunan tanpa harus menunggu penginstalan driver.
Membuat instance preemptible
Anda dapat membuat instance Deep Learning VM preemptible. Instance preemptible adalah instance yang dapat Anda buat dan jalankan dengan harga yang jauh lebih rendah daripada instance normal. Namun, Compute Engine dapat menghentikan (melakukan preempt) instance ini jika memerlukan akses ke resource tersebut untuk tugas lainnya.
Preemptible instance selalu berhenti setelah 24 jam. Untuk mempelajari lebih lanjut instance preemptible, lihat Instance VM Preemptible.
Untuk membuat instance Deep Learning VM yang dapat di-preempt:
Ikuti petunjuk di atas untuk membuat instance baru menggunakan
command line. Untuk perintah gcloud compute instances create, tambahkan
berikut ini:
--preemptible
Langkah berikutnya
Untuk mengetahui petunjuk tentang cara menghubungkan ke instance Deep Learning VM baru Anda melalui konsol atau command line, lihat Menghubungkan ke Instance. Google Cloud Nama instance Anda
adalah Deployment name yang Anda tentukan dengan tambahan -vm.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-17 UTC."],[[["\u003cp\u003eThis guide explains how to create a PyTorch Deep Learning VM instance, either through the Google Cloud Marketplace or using the command line interface.\u003c/p\u003e\n"],["\u003cp\u003eYou can configure your instance with or without GPUs, and if using GPUs, an NVIDIA driver installation is required, which can be done automatically.\u003c/p\u003e\n"],["\u003cp\u003eWhen creating an instance, options such as machine type, zone, framework, and networking settings, along with specific configurations for GPUs, can be customized.\u003c/p\u003e\n"],["\u003cp\u003eUsing the command line, you can create instances with specified PyTorch versions, and it includes provisions for creating instances with or without GPUs, along with specifying details like image family and project.\u003c/p\u003e\n"],["\u003cp\u003ePreemptible instances, which offer a lower price but can be terminated, are available for creation and can be done by adding a \u003ccode\u003e--preemptible\u003c/code\u003e flag to your gcloud command.\u003c/p\u003e\n"]]],[],null,["# Create a PyTorch Deep Learning VM instance\n\nThis page shows you how to create a PyTorch Deep Learning VM Images instance\nwith PyTorch and other tools pre-installed. You can create\na PyTorch 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 PyTorch Deep Learning VM instance from the Cloud Marketplace\n-----------------------------------------------------------------------\n\nTo create a PyTorch 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 **PyTorch 1.8 + fast.ai 2.1\n (CUDA 11.0)**.\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 PyTorch Deep Learning VM instance from the command line\n------------------------------------------------------------------\n\nTo use the Google Cloud CLI to create a new a Deep Learning VM\ninstance, you 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\n### Without GPUs\n\nTo create a Deep Learning VM instance\nwith the latest PyTorch image family and a\nCPU, enter the following at the command line: \n\n export IMAGE_FAMILY=\"pytorch-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 either `pytorch-latest-cpu`\n or `pytorch-`\u003cvar translate=\"no\"\u003eVERSION\u003c/var\u003e`-cpu`\n (for example, `pytorch-1-13-cpu`).\n\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 GPUs to your\nvirtual machine instances. GPUs offer faster processing for many complex data\nand machine learning tasks. To learn more about GPUs, see [GPUs on\nCompute Engine](/compute/docs/gpus).\n\nTo create a Deep Learning VM instance with the\nlatest PyTorch image family and one\nor more attached GPUs, enter the following at the command line: \n\n export IMAGE_FAMILY=\"pytorch-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 either `pytorch-latest-gpu`\n or `pytorch-`\u003cvar translate=\"no\"\u003eVERSION\u003c/var\u003e`-`\u003cvar translate=\"no\"\u003eCUDA-VERSION\u003c/var\u003e\n (for example, `pytorch-1-10-cu110`).\n\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 be installed\n on your behalf. The value is `install-nvidia-driver=True`. If specified,\n Compute Engine loads the latest stable driver on the first boot\n and performs the necessary steps (including a final reboot to activate the\n driver).\n\nIf you've elected to install NVIDIA drivers, allow 3-5 minutes for installation\nto 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\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."]]
